A direct-current bus balance control system of an optical storage alternating current-direct current microgrid

Abstract

The application relates to a direct-current bus balance control system of a photovoltaic energy storage AC / DC microgrid, which comprises the following: a direct-current bus as a core power collection node for power transmission and distribution; a photovoltaic unit connected to the direct-current bus through a direct-current converter for realizing maximum power tracking control and steady-state power consumption; a dual-energy energy storage unit connected to the direct-current bus through a bidirectional direct-current converter for compensating power fluctuation to maintain bus voltage stability; a DC / AC AC / DC conversion unit for bidirectional exchange of AC / DC energy; a multi-voltage grade load unit connected to the direct-current bus through a high-frequency isolation conversion module, comprising direct-current and alternating-current multi-voltage grade loads, for forming an adaptive voltage regulation mechanism with the direct-current bus to adapt to different load power supply requirements; and an energy management unit for scheduling cooperative operation of each unit through an electric energy routing control unit to achieve direct-current bus dynamic balance and optimal distribution of multiple energies.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and microgrid control technology, and in particular to a DC bus balance control system for a photovoltaic-storage AC / DC microgrid. Background Technology

[0002] With the continuous increase in the penetration rate of renewable energy sources such as photovoltaics, photovoltaic-storage AC / DC microgrids have become a core development direction of distributed energy systems due to their ability to flexibly adapt to mixed AC / DC loads and improve the absorption rate of new energy sources. They are widely used in scenarios such as industrial and agricultural IoT, smart parks, and power supply in remote areas. These systems achieve power aggregation and interaction between photovoltaics, energy storage, loads, and the grid through a DC bus. The stability of the DC bus voltage directly determines the system's operational accuracy, equipment lifespan, and power supply reliability, making it a core key technology for the design and operation and maintenance of photovoltaic-storage microgrids.

[0003] Currently, most photovoltaic-storage AC / DC microgrids employ traditional energy dispatch and voltage control schemes, using bipolar junction transistors or conventional power conversion architectures for energy conversion and distribution. However, these schemes still have significant limitations in terms of low power consumption, low cost, multi-condition adaptability, and intelligent management, making it difficult to meet the demands of high-density integration, low-voltage adaptability, and large-scale IoT applications. Specifically, the shortcomings of existing technologies are mainly reflected in the following aspects: First, the power response mechanism is poorly designed and lacks a division of labor and coordination strategy. The intermittency and volatility of photovoltaic power output, along with the dynamic changes in load demand, easily lead to the problem of instantaneous peak fluctuations in DC bus voltage and the coexistence of steady-state power surplus and deficit. Traditional solutions do not specifically regulate power fluctuations, relying solely on a single energy storage unit or grid supplementary energy adjustment. This not only results in insufficient voltage stability accuracy but also exacerbates equipment losses and energy consumption.

[0004] Second, the system has low integration and is difficult to control in terms of power consumption and cost. In existing solutions, the functional modules such as photovoltaic access, energy storage regulation, AC / DC conversion and load adaptation are mostly designed in a distributed manner. There is a lack of unified management and control interface between the modules. Multiple independent control circuits and bias units need to be configured, which increases the overall size, complexity and hardware cost of the system. In addition, the lag in coordination between modules leads to additional power consumption, which is not conducive to on-chip integration and low-cost large-scale application.

[0005] Third, it suffers from poor low-voltage adaptability and insufficient reliability during operating condition switching. Traditional solutions rely on conventional bandgap reference voltage design, resulting in a relatively high operating voltage threshold, making it difficult to adapt to the microgrid operation requirements in low power supply voltage scenarios. At the same time, the lack of a rapid coordinated control mechanism during grid-connected and off-grid switching makes it prone to voltage interruptions and surges, failing to meet the continuous power supply requirements of sensitive loads and limiting the application of the system in scenarios without grid support.

[0006] Fourth, there is a lack of intelligent control and safety traceability capabilities. Existing systems mostly focus on basic voltage and power regulation, lacking carbon emission metering, operational data safety traceability, and end-to-end fault diagnosis functions, making it difficult to adapt to the current needs of microgrid decarbonization and intelligent management; moreover, there are mismatch errors in data interaction between modules, and fault response is delayed, further reducing the stability and maintainability of system operation.

[0007] Fifth, the adaptability to multiple loads is insufficient, and the energy utilization efficiency is low. Traditional solutions have poor adaptability to AC and DC loads of different voltage levels and lack adaptive voltage regulation mechanisms, which can easily lead to problems such as unbalanced power supply to loads and energy waste. At the same time, the dispatching strategy is too simple and does not take into account both the curtailment rate and the cost of electricity, making it difficult to achieve optimal energy allocation among multiple energy sources and multiple loads.

[0008] In summary, existing DC bus control schemes for photovoltaic-storage AC / DC microgrids suffer from drawbacks such as large size, high power consumption, poor low-voltage adaptability, insufficient reliability during operating condition switching, and low level of intelligence. These limitations fail to meet the application requirements of high-density integration, low-cost scalability, and low-carbon intelligent operation. Therefore, developing a high-precision, highly integrated, low-power, and multi-condition adaptable DC bus balance control method for photovoltaic-storage AC / DC microgrids has become a research hotspot and urgent need in the field of new energy microgrids. Summary of the Invention

[0009] This invention aims to overcome the shortcomings of existing photovoltaic-storage AC / DC microgrid DC bus balancing control technology, and solve the problems of unclear power response division, significant bus voltage fluctuation, low reliability of grid-connected and off-grid switching, poor multi-load adaptability, high power consumption and cost, and lack of intelligent management in traditional solutions. It provides a photovoltaic-storage AC / DC microgrid DC bus balancing control system that does not require complex module matching, occupies little space, and has low power consumption, meeting the needs of industrial parks, smart buildings, distributed generation projects and other scenarios for high stability, high energy utilization, low cost and low-carbon intelligent operation.

[0010] To achieve the above objectives, the present invention provides the following solution: A DC bus balance control system for a photovoltaic-storage AC / DC microgrid includes: a DC bus, photovoltaic units, dual-energy storage units, DC / AC conversion units, multi-voltage load units, power routing control units, and energy management units; The DC bus is the core power aggregation node, which is electrically connected to the photovoltaic unit, the dual-energy storage unit, the DC / AC conversion unit and the multi-voltage load unit for power transmission and distribution. The photovoltaic unit is connected to the DC bus through an adaptive DC converter to achieve maximum power point tracking control and steady-state power absorption; The dual-energy storage unit is connected to the DC bus via a bidirectional DC-DC converter to compensate for power fluctuations and maintain bus voltage stability. The DC / AC converter unit is used for bidirectional exchange of AC and DC energy. The multi-voltage-level load unit is connected to the DC bus through a high-frequency isolation conversion module, including DC and AC multi-voltage-level loads, which are used to form an adaptive voltage regulation mechanism with the DC bus to adapt to different load power supply requirements. The energy management unit schedules the coordinated operation of each unit through the power routing control unit to achieve dynamic balance of the DC bus and optimal allocation of multiple energy sources.

[0011] Optionally, the photovoltaic unit converts solar energy into DC power through an adaptive DC converter and transmits it stably to the DC bus, and adopts the improved perturbation-observation method MPPT control algorithm to run the maximum power point tracking function.

[0012] Optionally, the improved disturbance-observation method (MPPT) control algorithm includes: S1. Initialization: Set the initial perturbation step size ΔU, sampling period Ts, and maximum power point voltage threshold U. mpp_ref ; S2. Obtain the real-time output voltage U of the photovoltaic array during the k-th sampling. pv (k) Real-time output current I pv (k), calculate the real-time output power: S3. Calculate the power deviation ΔP and voltage deviation ΔU between the current and previous sampling: S4. Disturbance direction determination logic: If ΔP>0 and ΔU>0: maintain the current voltage disturbance direction and continue to increase the reference voltage; If ΔP>0 and ΔU<0: reverse the disturbance direction and reduce the reference voltage; If ΔP < 0 and ΔU > 0: reverse the disturbance direction and decrease the reference voltage; If ΔP < 0 and ΔU < 0: Maintain the current disturbance direction and increase the reference voltage; S5. Variable step size adjustment: When |ΔP| <P th When the system is near the maximum power point, the perturbation step size is reduced to 1 / 5 of the original step size to improve tracking accuracy; when |ΔP| ≥ P th At that time, the large stride length is restored to improve the tracking speed; among which, P th Power threshold; S6. Output Adjustment: Adjust the final generated reference voltage. U ref The duty cycle D of the PWM drive signal is generated by inputting a PI controller. The calculation formula is as follows: ; in, Kp This is the proportionality coefficient. Ki The integral coefficient is... Ts The sampling period is k This is the sequence number of the current sampling time. n The variable used for summation operations.

[0013] Optionally, the power routing control unit obtains the DC bus power, the operating status of each unit, and the energy storage charge status information through the sampling link, generates adjustment commands through calculation, and transmits the adjustment commands to the converters and switching equipment of each unit through the drive link. It adopts a dual closed-loop control strategy of voltage outer loop and power inner loop to balance the DC bus power and obtain the control result.

[0014] Optionally, the dual closed-loop control strategy of voltage outer loop-power inner loop includes: Voltage outer loop control: based on the rated voltage U of the DC bus dc_ref For reference, compared with the real-time sampled DC bus voltage U dc The deviation input of (k) to the PI controller generates the system total power reference value P. total_ref : ; in, , T is the voltage loop PI control parameter. s To control the cycle; Power inner loop distribution and control: In grid-connected mode, power allocation follows the priority order of photovoltaic power, energy storage supplementary power, and grid reserve. The power balance relationship is as follows: ; in, P loss For the total system loss, P grid For grid interaction power, P pv For the output power of the photovoltaic array, P bat The charging and discharging power of the energy storage battery, P load Power consumed by local load; In off-grid mode, the power balance relationship is as follows: ; With a dual-energy storage unit as the core, the photovoltaic unit and the energy storage unit work together to supply power. When the energy storage SOC is lower than the preset threshold, non-critical loads are cut off according to load priority to ensure power supply to critical loads.

[0015] Optionally, in grid-connected mode, when P pv > P load + P loss When there is a power surplus, the surplus power is preferentially used for charging the dual-energy storage unit. When the energy storage SOC is ≥ 95%, the remaining surplus power is fed into the grid through the DC / AC unit. when P pv < P load + P loss When there is a power deficit, it is preferentially supplemented by the discharge of the dual-energy storage unit. When the energy storage SOC ≤ 20%, the remaining deficit is supplemented by the grid through rectification by the DC / AC conversion unit.

[0016] Optionally, the energy management unit takes the dynamic balance of DC bus power as the core constraint, constructs a scheduling optimization model with multi-objective optimization dimensions, adapts to both grid-connected and off-grid operation modes, generates the optimal power scheduling plan, and sends it to the power routing control unit for execution; wherein, the multi-objective optimization dimensions include: minimum curtailment rate, minimum electricity cost, and minimum energy storage cycle loss.

[0017] Optionally, the scheduling optimization model includes: a multi-objective optimization objective function and hard constraints; The multi-objective optimization objective function is: the comprehensive optimization objective is the weighted minimization of each sub-objective, as shown in the following formula: ; in, To comprehensively optimize the target value, These are the weighting coefficients for each sub-objective. For the light waste rate target item, For electricity cost target item, The target item for energy storage cycle loss; The hard constraints include: power balance constraints, equipment safe operation constraints, and off-grid load priority constraints.

[0018] The beneficial effects of this invention are as follows: Compared to traditional solutions, the proposed photovoltaic-storage AC / DC microgrid DC bus control scheme offers comprehensive improvements in stability, adaptability, economy, and intelligence, demonstrating significant technical advantages and strong practicality. It employs a single-core DC bus + dual-energy storage design, abandoning the traditional multi-node distributed control architecture, simplifying the system structure, and reducing matching errors and maintenance costs. The synergistic effect of high- and low-frequency power response division and dual error suppression mechanisms significantly improves bus voltage stability. DC / AC ensures power supply reliability and flexibility. Integrated carbon metering, blockchain on-chaining, and full-link fault diagnosis functions address both low-carbon and intelligent management needs. The overall circuit is based on general microgrid technology, offering strong compatibility and flexible design, providing efficient and low-cost bus balance control solutions for various scenarios, and possessing broad application value. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a diagram of the hierarchical control architecture of a photovoltaic-storage AC / DC microgrid according to an embodiment of the present invention; Figure 2 This is a diagram of a DC / AC conversion unit according to an embodiment of the present invention; Figure 3 This is a pre-charging and filtering circuit diagram of the DC / AC conversion unit according to an embodiment of the present invention; Figure 4 This is a diagram of a DC / DC converter unit according to an embodiment of the present invention; Figure 5 This is a control logic diagram of the DC / AC conversion unit according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the energy storage voltage control algorithm according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware topology of a photovoltaic-storage AC / DC microgrid system according to an embodiment of the present invention; Figure 8 This is a logic diagram for photovoltaic power smoothing control in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1 This embodiment proposes a DC bus balance control system for a photovoltaic-storage AC / DC microgrid, including: a DC bus, photovoltaic units, dual-energy storage units, DC / AC conversion units, multi-voltage load units, power routing control units, and energy management units; The DC bus is the core power aggregation node, which is electrically connected to the photovoltaic unit, the dual-energy storage unit, the DC / AC conversion unit and the multi-voltage load unit for power transmission and distribution. The photovoltaic unit is connected to the DC bus through an adaptive DC converter to achieve maximum power point tracking control and steady-state power absorption; The dual-energy storage unit is connected to the DC bus via a bidirectional DC-DC converter to compensate for power fluctuations and maintain bus voltage stability. The DC / AC converter unit is used for bidirectional exchange of AC and DC energy. The multi-voltage-level load unit is connected to the DC bus through a high-frequency isolation conversion module, including DC and AC multi-voltage-level loads, which are used to form an adaptive voltage regulation mechanism with the DC bus to adapt to different load power supply requirements. The energy management unit schedules the coordinated operation of each unit through the power routing control unit to achieve dynamic balance of the DC bus and optimal allocation of multiple energy sources.

[0024] Specifically, in this embodiment, such as Figure 1 As shown, a hierarchical control architecture for a photovoltaic-storage AC / DC microgrid is provided, aiming to solve the DC bus power imbalance problem caused by the intermittency of photovoltaic output, the volatility of load demand, and the disturbance of grid-connected / off-grid switching in existing photovoltaic-storage microgrids. It includes: a core power aggregation node DC bus, photovoltaic units, energy storage units, DC / AC conversion units, multi-voltage level load units, power routing control units, and energy management units, wherein: The photovoltaic unit is connected through an adaptive DC-DC converter to achieve maximum power point tracking control and steady-state power absorption; each of the energy storage units is connected through a bidirectional DC-DC converter to compensate for power fluctuations and maintain bus voltage stability; the DC / AC conversion unit realizes bidirectional exchange of AC and DC energy; the energy management unit schedules the coordinated operation of each unit through the power routing control unit to achieve dynamic balance of the DC bus and optimal allocation of multiple energy sources.

[0025] Furthermore, the photovoltaic unit converts solar energy into DC power through an adaptive DC converter and transmits it stably to the DC bus. It adopts the improved perturbation-observation method MPPT control algorithm and runs the maximum power point tracking function.

[0026] Specifically, in this embodiment, the photovoltaic unit is used to convert solar energy into DC power through an adaptive DC converter and stably transmit it to the DC bus, while operating the maximum power point tracking (MPPT) function. The MPPT function dynamically adjusts the system's operating voltage and current to consistently track and lock onto this optimal maximum power point, thereby maximizing photovoltaic output. In this embodiment, the photovoltaic unit adopts the improved perturbation-observation method (MPPT) control algorithm to solve the misjudgment problem of the traditional perturbation-observation method when there are sudden changes in illumination. The specific calculation process and formula are as follows: S1. Initialization: Set the initial perturbation step size ΔU, sampling period Ts, and maximum power point voltage threshold U. mpp_ref ; S2. Obtain the real-time output voltage U of the photovoltaic array during the k-th sampling. pv (k) Real-time output current I pv (k), calculate the real-time output power: S3. Calculate the power deviation ΔP and voltage deviation ΔU between the current and previous sampling: S4. Disturbance direction determination logic: If ΔP>0 and ΔU>0: maintain the current voltage disturbance direction and continue to increase the reference voltage; If ΔP>0 and ΔU<0: reverse the disturbance direction and reduce the reference voltage; If ΔP < 0 and ΔU > 0: reverse the disturbance direction and decrease the reference voltage; If ΔP < 0 and ΔU < 0: Maintain the current disturbance direction and increase the reference voltage; S5. Variable step size adjustment: When |ΔP| <P th When the system is near the maximum power point, the perturbation step size is reduced to 1 / 5 of the original step size to improve tracking accuracy; when |ΔP| ≥ P th At that time, the large stride length is restored to improve the tracking speed; among which, P th Power threshold; S6. Output Adjustment: Adjust the final generated reference voltage. U ref The duty cycle D of the PWM drive signal is generated by inputting a PI controller. The calculation formula is as follows: ; in, Kp This is the proportionality coefficient. Ki The integral coefficient is... Ts The sampling period.

[0027] The algorithm's MPPT can operate stably within a light intensity range of 100-1000W / ㎡ and an ambient temperature range of -20℃-60℃, effectively suppressing the power surge of intermittent photovoltaic output on the DC bus.

[0028] Furthermore, the power routing control unit obtains the DC bus power, the operating status of each unit, and the energy storage charge status information through the sampling link, generates adjustment commands through calculation, and transmits the adjustment commands to the converters and switching equipment of each unit through the drive link. It adopts a dual closed-loop control strategy of voltage outer loop and power inner loop to balance the DC bus power and obtain the control result.

[0029] Specifically, this embodiment employs a dual closed-loop control strategy of voltage outer loop and power inner loop to achieve stable control of the DC bus voltage. The specific calculation process and formulas are as follows: Voltage outer loop control: based on the rated voltage U of the DC bus dc_ref For reference, compared with the real-time sampled DC bus voltage U dc The deviation input of (k) to the PI controller generates the system total power reference value P. total_ref : ; in, , Here are the voltage loop PI control parameters, and Ts is the control period; Power inner loop distribution and control: In grid-connected mode, power allocation follows the priority order of photovoltaic power, energy storage supplementary power, and grid reserve. The power balance relationship is as follows: ; in, P grid For grid interaction power, P pv For the output power of the photovoltaic array, P bat The charging and discharging power of the energy storage battery, P load Power consumed by local load, P loss This represents the total system loss. In off-grid mode, the power balance relationship is as follows: ; With a dual-energy storage unit as the core, the photovoltaic unit and the energy storage unit work together to supply power. When the energy storage SOC is lower than the preset threshold, non-critical loads are cut off according to load priority to ensure power supply to critical loads.

[0030] Furthermore, the energy management unit takes the dynamic balance of DC bus power as the core constraint, constructs a scheduling optimization model with multi-objective optimization dimensions, adapts to both grid-connected and off-grid operation modes, generates the optimal power scheduling plan, and sends it to the power routing control unit for execution; wherein, the multi-objective optimization dimensions include: minimum curtailment rate, minimum electricity cost, and minimum energy storage cycle loss.

[0031] Specifically, in this embodiment, constructing the scheduling optimization model includes: Construct a multi-objective optimization objective function: The overall optimization objective is the weighted minimization of the sub-objectives, as shown in the following formula: ; in, To comprehensively optimize the target value, The weight coefficients for each sub-objective satisfy the following conditions: The power grid peak-valley electricity pricing policy and low-carbon requirements can be configured online, with default weights of 0.4, 0.35, and 0.25, respectively. The curtailment rate is a target term used to constrain the level of photovoltaic new energy consumption. The calculation formula is as follows: ; in, The total number of time periods in the scheduling cycle. for Maximum photovoltaic power generation during the period for Actual photovoltaic output during the period The scheduling step size is for a single time period; This is the electricity cost target item, used to adapt to the peak-valley electricity pricing policy of the power grid and optimize the economic efficiency of system operation. The calculation formula is as follows: ; in, for Time-of-use electricity purchase price for Power purchased from the grid during the specified time period for The on-grid tariff for photovoltaic power during certain periods for Power fed into the grid during specific time periods; The target term for energy storage cycle loss is used to mitigate frequent charging and discharging of energy storage and extend its service life. The calculation formula is as follows: ; in, for Time-of-use energy storage charging and discharging power, This is the lifespan loss factor corresponding to the charge / discharge capacity of an energy storage unit.

[0032] The hard constraints include: power balance constraints, equipment safe operation constraints, and off-grid load priority constraints.

[0033] Specifically, in this embodiment, the optimization model must meet the following constraints and fully match the safe operation boundaries and DC bus balance requirements of each underlying unit: Power balance constraints: Grid connection mode: ; Offline mode: ; In the formula, To provide real-time power output for supercapacitors. Total load power, This refers to the power loss of system lines and equipment.

[0034] Equipment safety operation constraints: Photovoltaic output constraints: ; Energy storage charge and discharge constraints: (Completely consistent with the protection threshold of the energy storage unit); Power grid interaction constraints: (Negative for grid feed, positive for electricity purchase) (Rated capacity of DC / AC unit). DC bus voltage stability constraints: ; Off-grid load priority constraints: When operating off-grid, if the energy storage SOC ≤ 30%, the third-level non-critical loads will be forcibly disconnected; if the SOC ≤ 20%, the second-level secondary critical loads will be forcibly disconnected, and only the first-level critical loads will be supplied with power, which is fully matched with the load unit control logic.

[0035] Scheduling policy execution logic: In grid-connected mode, based on the scheduling plan generated by the optimization model, the scheduling logic of "photovoltaic priority, energy storage supplement, and grid backup" is executed: photovoltaic output is given priority to supply the load, surplus power is given priority to charge energy storage, and the remaining power after the energy storage is fully charged is fed into the grid; load power deficit is given priority to be supplemented by energy storage discharge, and the grid supplements the power when the energy storage is insufficient.

[0036] In off-grid mode, the scheduling logic of "energy storage core, photovoltaic coordination, and load tiering" is implemented: the dual-energy type energy storage unit is used as the core power supply, the photovoltaic unit outputs power in MPPT mode, and the power is allocated according to the load priority to ensure continuous power supply to important loads.

[0037] Furthermore, this embodiment also includes a full-cycle carbon metering function: In this embodiment, the emission factor method is used to accurately calculate carbon emissions and carbon reductions throughout the entire system, providing quantitative support for low-carbon management.

[0038] Carbon emission accounting: The total carbon emissions of the system are the sum of the emissions from all carbon emission sources within the accounting boundary, which covers the entire process from photovoltaics to energy storage, load, and power grid. The formula is as follows: ; In the formula: This represents the total carbon emissions of the system during the accounting period. The carbon emissions generated from purchasing electricity from the grid are calculated using the following formula: ; in, This represents the total electricity purchased from the power grid during the accounting period (unit: kWh). The emission factor is the latest regional power grid baseline emission factor (unit: kgCO2e / kWh) released by the provincial power grid where the project is located. It is the periodic conversion value of carbon emissions for the entire life cycle of system equipment, covering carbon emissions in all stages of equipment production, transportation, operation and maintenance, and scrapping, and is amortized to the accounting period according to the design service life of the equipment.

[0039] Carbon emission reduction accounting; The system's carbon emission reduction is the difference between the baseline emission level and the actual emission level, calculated using the following formula: ; In the formula, The baseline carbon emissions represent the carbon emissions generated when all electricity for the system's load is purchased from the grid, assuming no photovoltaic renewable energy supply. The calculation formula is as follows: ; in, The total electricity consumption of the system during the accounting period (unit: kWh).

[0040] Low-carbon closed-loop management: The energy management unit generates standardized carbon metering and accounting reports on a daily, monthly, and yearly basis, and links carbon emission reduction data with power dispatch strategies: when grid electricity prices are flat, priority is given to increasing the proportion of photovoltaic self-generation and self-consumption to maximize carbon emission reduction benefits; when grid electricity prices are low, priority is given to charging energy storage through the grid to reduce photovoltaic curtailment and achieve synergistic optimization of economic efficiency and low carbon emissions.

[0041] Furthermore, this embodiment also includes a blockchain on-chain function: In this embodiment, the blockchain on-chain function adopts a consortium blockchain architecture. It uses national cryptographic algorithms to achieve encrypted on-chain storage of core data, solving the problems of easy tampering and poor traceability of traditional microgrid operation data. Simultaneously, it provides reliable data support for green electricity trading and carbon asset accounting. The specific implementation process is as follows: Data Acquisition and Preprocessing: Core on-chain data is collected from the power routing control unit according to a preset cycle, including: DC bus operation data, cumulative photovoltaic power generation, cumulative energy storage charging and discharging data, grid interaction power, load power consumption, carbon metering and carbon emission reduction data, and equipment anomaly and fault alarm data; the collected data is cleaned, deduplicated, and formatted to remove outliers.

[0042] Data encryption and hash generation: The original data is encrypted using the national standard SM2 asymmetric encryption algorithm, and a unique hash value is generated for the encrypted data packet using the national standard SM3 hash algorithm, ensuring the confidentiality and integrity of the data.

[0043] Block generation and on-chain consensus: The data hash value, the original encrypted data packet, the timestamp, and the device's unique identifier are packaged to generate a block, which is then broadcast to all nodes on the chain through the consortium chain nodes. The on-chain nodes use the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism to complete the data verification. After the verification is successful, the block is written into the distributed ledger to complete the on-chain storage, and a unique data traceability identifier is generated at the same time.

[0044] Data traceability and trusted sharing: It supports querying the full historical data of the corresponding block through traceability identifiers. The data is tamper-proof and cannot be deleted. At the same time, it can open authorized data interfaces to power grid companies, carbon exchanges, and green electricity trading platforms to provide trusted data certificates for the system's carbon asset accounting and green electricity consumption certification.

[0045] Synergistic interaction between blockchain on-chain functionality and the underlying system: The energy management unit will optimize and generate power scheduling plans, energy storage charging and discharging thresholds, grid interaction power limits, and load switching commands, and send them to the power routing control unit in real time via a high-speed communication link. Based on the upper-level scheduling commands, the power routing control unit will execute the lower-level real-time closed-loop control to complete the DC bus power balance regulation. At the same time, the power routing control unit will send real-time operating data and control execution results back to the energy management unit, forming a closed-loop control link of "scheduling-execution-feedback-optimization", ultimately realizing the intelligent optimal operation and low-carbon precise control of the system.

[0046] Specifically, in this embodiment, the various units of the DC bus power balancing system form a closed-loop control link through electrical connections: The photovoltaic unit is connected to the DC bus through an adaptive DC converter. The converter uses maximum power point tracking to stabilize the photovoltaic output transmission and reduce the impact of power fluctuations. The energy storage unit is connected in parallel to the DC bus via a bidirectional DC converter to adapt to energy surplus and deficit regulation; like Figure 2 As shown, this embodiment provides a DC / AC conversion unit that adopts a modular topology design, such as... Figure 3 The circuit shown integrates a pre-charge circuit and a filter circuit: In the pre-charge circuit, a switch and a parallel resistor suppress the inrush current during the power-on initialization phase, preventing damage to power devices from instantaneous large current and ensuring safe power-on of the system; The filter circuit consists of a filter inductor and a filter capacitor, forming a low-pass filter network to attenuate high-frequency harmonics to optimize the energy flow waveform and improve system stability. Multi-voltage load units are connected to the DC bus through corresponding high-frequency isolation conversion modules to adapt to the voltage and power requirements of different loads; The power routing control unit adopts a high-performance processor architecture to collect bus power, operating parameters of each unit and energy storage charge status in real time. Through hardware-driven control, it adjusts the output of photovoltaic converter, energy storage charging and discharging power, DC / AC unit energy interaction scale and load access mode to achieve DC bus power balance. The energy management unit interacts with the power routing control unit through a communication link to issue power commands. The carbon metering function calculates carbon emission data based on the emission factor method, and the blockchain on-chain function uploads operation data according to a preset period to ensure data traceability and secure sharing.

[0047] This embodiment significantly improves the DC bus power disturbance immunity through hardware design and control strategy synergy, keeping power fluctuations within a reasonable range, while also improving energy utilization efficiency. It is suitable for photovoltaic-storage AC / DC microgrid scenarios with multiple energy access and multiple load types.

[0048] Furthermore, the DC converter of the photovoltaic unit adopts maximum power point tracking to dynamically adjust the output.

[0049] Furthermore, the photovoltaic unit in this embodiment employs maximum power point tracking, such as... Figure 4 The diagram shows a DC / DC bridge consisting of multiple phase-interleaved power arms. Each arm integrates a high-frequency power switching device and is connected in parallel to the DC bus through the power output terminal. By utilizing the multi-channel collaborative working characteristics, the impact of photovoltaic power output fluctuations on the bus power is reduced, thereby improving the stability of photovoltaic power transmission.

[0050] Furthermore, the bidirectional DC-DC converter of the energy storage branch adopts a multi-phase interleaved power bridge arm DC / DC converter; the bidirectional DC-DC converter has a built-in high-bandwidth power control module; it is also used to interact with the power routing control unit through a high-speed digital interface to receive power regulation commands.

[0051] Furthermore, the bidirectional DC-DC converter of the energy storage branch involved in this embodiment is adapted to the power fluctuation compensation requirement. It forms a power conversion circuit through complementary high-frequency switching devices DC / DC and interacts with the power routing control unit through a high-speed digital interface. When instantaneous power fluctuations occur on the DC bus, it quickly responds to commands and realizes power compensation.

[0052] Furthermore, the grid side of the AC / DC conversion unit includes a "pre-charge-filter" collaborative hardware link, which limits inrush current during unit startup and other stages through a pre-charge circuit. The collaborative hardware link includes a pre-charge circuit and a filter circuit. The pre-charge circuit is used to limit inrush current during unit startup, and the filter circuit is used to suppress harmonic components in energy flow.

[0053] Furthermore, the control logic of the DC / AC converter unit involved in this embodiment is as follows: Figure 5 As shown, a grid-type inverter control strategy is adopted to generate PWM waves to drive the inverter and maintain system power balance.

[0054] Furthermore, the power routing control unit adopts a high-performance processor architecture, including a computing module and a drive module. The computing module is used to perform energy storage power control and bus power balance control, and outputs control commands to the drive module. The drive module is used to generate drive signals based on the control commands. The drive signals are used to synchronously regulate each converter and switching device through the drive link.

[0055] Furthermore, the power routing control unit involved in this embodiment adopts a high-performance processor architecture to collect DC bus power, operating parameters of each unit and energy storage state of charge (SOC) in real time, and adjust the energy storage charging and discharging power, the energy interaction scale of DC / AC units and the load access mode to achieve DC bus power balance control.

[0056] Furthermore, the multi-channel maximum power point tracking converter of the photovoltaic unit integrates current sampling elements and voltage sampling elements; the sampling elements are connected through the sampling interface of the power routing control unit to form a real-time acquisition hardware link for photovoltaic side operation data; the computing module processes the acquired raw data and issues adjustment commands, which are transmitted to the photovoltaic converter through the PWM drive interface.

[0057] Furthermore, the power routing control unit in this embodiment possesses the capability of photovoltaic power data acquisition and command generation. This capability is achieved through the collaboration of the hardware link and computing module of the "photovoltaic unit-power routing control unit": the photovoltaic unit's converter integrates a sampling element, which is connected through the sampling interface of the power routing control unit, forming a real-time acquisition hardware link for photovoltaic side operating data; the computing module of the power routing control unit relies on its own hardware computing resources to process the acquired raw data and issue commands; the commands are transmitted to the photovoltaic converter through a dedicated drive interface to adjust the converter output; the above-mentioned "data acquisition-processing-command issuance" entire link relies on hardware transmission and hardware computing module hardware computing to meet the real-time control requirements of DC bus power balance.

[0058] Furthermore, the energy storage power control and bus power balance control are implemented based on a high-performance processor architecture; the computing module carries out power control and balance control and outputs instruction references; the drive module receives the instructions processed by the computing module and generates drive signals, and controls the power adjustment of each converter based on the drive signals.

[0059] Furthermore, the energy storage power control and bus power balance control in this embodiment are implemented based on the high-performance processor architecture of the power routing control unit, and the specific logic is as follows: like Figure 6 As shown, the energy storage voltage control algorithm focuses on power point tracking accuracy. It calculates the difference between the reference voltage and the actual acquired voltage, and after processing by the built-in controller in the arithmetic module, the drive module outputs a control signal to generate a complementary switching signal, controlling the power flow of the energy storage branch converter. This algorithm relies on a high-performance processor architecture; the arithmetic module handles mathematical operations and instruction output, while the drive module ensures drive timing and coordination among units, achieving rapid tracking of power commands.

[0060] As a preferred implementation method, the DC bus power balancing system for photovoltaic-storage AC / DC microgrids disclosed in this embodiment solves the DC bus power imbalance problem caused by the intermittency of photovoltaic output, the volatility of load demand, and the disturbance of grid connection / off-grid switching in existing photovoltaic-storage microgrids through "differentiated hardware topology design + coordinated control strategy". The overall system architecture is built around "power transmission - fluctuation compensation - precise control", and the hardware selection and functional design of each unit are based on the actual microgrid operation requirements.

[0061] like Figure 7 As shown, the hardware topology of the photovoltaic-storage AC / DC microgrid system in this embodiment adopts a hybrid AC / DC bus architecture, with the core consisting of two major energy hubs: an AC bus and a DC bus. The photovoltaic system and energy storage system are connected to the DC bus via independent DC / DC converters, achieving stable access to renewable energy and bidirectional energy interaction between the energy storage units. On the DC bus side, multiple DC / DC converters connect to low-power loads, high-power loads, and charging piles, adapting to different power levels of DC power demand. The AC bus and DC bus achieve bidirectional energy flow through a bidirectional DC / AC unit, while the AC bus side connects to fast-charging piles via an independent DC / AC unit, meeting the power supply requirements of high-power fast-charging scenarios. This topology, through differentiated hardware link design, achieves efficient connection between the source-grid-load-storage units and the DC bus, providing a stable hardware foundation for DC bus power balance.

[0062] When the system is working, each unit achieves power balance based on the above topology: the photovoltaic system feeds photovoltaic power into the DC bus through a DC / DC converter, providing the core energy source for DC-side loads and the energy storage system; the energy storage system charges and stores energy when there is a power surplus on the DC bus and discharges to replenish energy when there is a power shortage, thus smoothing out power fluctuations; the AC / DC bidirectional DC / AC unit realizes bidirectional energy interaction between the DC bus and the AC grid, providing grid backup support for the DC bus under grid-connected conditions, enabling surplus power to be fed into the grid and power shortage to be replenished; various DC loads are connected to the DC bus through corresponding DC / DC converters, realizing flexible adaptation to power demand; and fast charging piles draw power directly from the AC bus through independent DC / AC units, reducing the impact on the power stability of the DC bus.

[0063] To address the DC bus power fluctuation problem caused by the intermittency and randomness of photovoltaic output, this embodiment uses... Figure 8 The photovoltaic power smoothing control logic shown achieves proactive regulation, reducing power disturbances at the source. The core process of this control logic is as follows: the difference between the original output power of the photovoltaic system and the target power set by the system is calculated to obtain the power to be smoothed; based on the power to be smoothed, a corresponding power adjustment command is generated; and the output curve of the photovoltaic unit is actively adjusted through this command to smooth photovoltaic power fluctuations, reduce the power disturbance amplitude of the DC bus from the source, reduce the power compensation pressure on the downstream energy storage unit and the grid, and further enhance the stability of the DC bus power.

[0064] This implementation method is through Figure 7 Hardware topology and Figure 8The active damping control strategy works in deep coordination to achieve rapid balance of DC bus power under complex operating conditions. The high-performance architecture of the power routing control unit ensures the rapid generation and execution of control commands, fully verifying the engineering feasibility and stability of the technical solution of this invention.

[0065] Example 2 Based on the same inventive concept, this embodiment also provides a DC bus power balance method for a photovoltaic-storage AC / DC microgrid, based on Figure 7 The AC / DC hybrid bus topology implementation shown includes: The photovoltaic power is converted by the DC / DC converter of the photovoltaic system and then transmitted to the DC bus. The energy storage system is connected in parallel to the DC bus via a bidirectional DC / DC converter to achieve bidirectional energy interaction for charging and discharging. The bidirectional DC / AC converter unit enables bidirectional flow of AC and DC energy between the DC bus and the AC bus (external power grid). The DC bus side is connected to DC load units of various voltage levels, such as low-power loads, high-power loads, and charging piles, through multiple DC / DC isolation conversion modules to meet the power supply needs of various DC electrical equipment. The AC bus side is connected to the fast charging pile through an independent DC / AC conversion unit to meet the power demand of high-power fast charging scenarios; After obtaining the DC bus power, the operating status of each unit and the energy storage charge status information through the sampling link of the power routing control unit, the power adjustment command is generated through calculation and transmitted to the converter and switching equipment of each unit through the drive link to balance the DC bus power and obtain the control result. pass Figure 8 The photovoltaic power smoothing control logic shown actively and smoothly regulates the photovoltaic output to reduce DC bus power fluctuations from the source end. Power scheduling strategies are formulated through energy management units, integrating carbon metering and blockchain on-chain functions; The DC bus power balancing method for the photovoltaic-storage AC / DC microgrid provided in this embodiment has all the advantages of the DC bus power balancing system for the photovoltaic-storage AC / DC microgrid provided in Embodiment 1.

[0066] Example 3 This embodiment also discloses a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the control logic of the system described in Embodiment 1 and the steps of the method described in Embodiment 2.

[0067] Example 4 This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the control logic of the system described in Embodiment 1 and the steps of the method described in Embodiment 2.

[0068] Example 5 This embodiment also discloses a computer program product, including a computer program that, when executed by a processor, implements the control logic of the system described in Embodiment 1 and the steps of the method described in Embodiment 2.

[0069] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A DC bus balance control system for a photovoltaic-storage AC / DC microgrid, characterized in that, include: DC bus, photovoltaic unit, dual-energy storage unit, DC / AC conversion unit, multi-voltage load unit, power routing control unit, energy management unit; The DC bus is the core power aggregation node, which is electrically connected to the photovoltaic unit, the dual-energy storage unit, the DC / AC conversion unit and the multi-voltage load unit for power transmission and distribution. The photovoltaic unit is connected to the DC bus via a DC converter to achieve maximum power point tracking control and steady-state power consumption. The dual-energy storage unit is connected to the DC bus via a bidirectional DC-DC converter to compensate for power fluctuations and maintain bus voltage stability. The DC / AC converter unit is used for bidirectional exchange of AC and DC energy. The multi-voltage-level load unit is connected to the DC bus through a high-frequency isolation conversion module, including DC and AC multi-voltage-level loads, which are used to form an adaptive voltage regulation mechanism with the DC bus to adapt to different load power supply requirements. The energy management unit schedules the coordinated operation of each unit through the power routing control unit to achieve dynamic balance of the DC bus and optimal allocation of multiple energy sources.

2. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 1, characterized in that, The photovoltaic unit converts solar energy into DC power through an adaptive DC converter and transmits it stably to the DC bus. It adopts the improved perturbation-observation method MPPT control algorithm and runs the maximum power point tracking function.

3. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 1, characterized in that, The improved disturbance-observation method (MPPT) control algorithm includes: S1. Initialization: Set the initial perturbation step size ΔU, sampling period Ts, and maximum power point voltage threshold U. mpp_ref ; S2. Obtain the real-time output voltage U of the photovoltaic array during the k-th sampling. pv (k) Real-time output current I pv (k), calculate the real-time output power: S3. Calculate the power deviation ΔP and voltage deviation ΔU between the current and previous sampling: S4. Disturbance direction determination logic: If ΔP > 0 and ΔU > 0: maintain the current voltage disturbance direction and continue to increase the reference voltage; If ΔP > 0 and ΔU < 0: reverse the disturbance direction and reduce the reference voltage; If ΔP < 0 and ΔU > 0: reverse the disturbance direction and reduce the reference voltage; If ΔP < 0 and ΔU < 0: Maintain the current disturbance direction and increase the reference voltage; S5. Variable step size adjustment: When |ΔP| < P th When the system is near the maximum power point, the perturbation step size is reduced to 1 / 5 of the original step size to improve tracking accuracy; when |ΔP| ≥ P th At that time, the large stride length is restored to improve the tracking speed; among which, P th Power threshold; S6. Output Adjustment: Adjust the final generated reference voltage. U ref The duty cycle D of the PWM drive signal is generated by inputting a PI controller. The calculation formula is as follows: ； in, Kp This is the proportionality coefficient. Ki The integral coefficient is... Ts The sampling period is k This is the sequence number of the current sampling time. n The variable used for summation operations.

4. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 1, characterized in that, The power routing control unit obtains DC bus power, operating status of each unit, and energy storage charge status information through the sampling link. It then generates adjustment commands through calculation and transmits the adjustment commands to the converters and switching devices of each unit through the drive link. It adopts a dual closed-loop control strategy of voltage outer loop and power inner loop to balance the DC bus power and obtain control results.

5. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 4, characterized in that, The dual closed-loop control strategy of voltage outer loop and power inner loop includes: Voltage outer loop control: based on the rated voltage U of the DC bus dc_ref For reference, compared with the real-time sampled DC bus voltage U dc The deviation input of (k) to the PI controller generates the system total power reference value P. total_ref : ； in, , T is the voltage loop PI control parameter. s To control the cycle; Power inner loop distribution and control: In grid-connected mode, power allocation follows the priority order of photovoltaic power, energy storage supplementary power, and grid reserve. The power balance relationship is as follows: ； in, P grid For grid interaction power, P pv For the output power of the photovoltaic array, P bat The charging and discharging power of the energy storage battery, P load Power consumed by local load, P loss This represents the total system loss. In off-grid mode, the power balance relationship is as follows: ； With a dual-energy storage unit as the core, the photovoltaic unit and the energy storage unit work together to supply power. When the energy storage SOC is lower than the preset threshold, non-critical loads are cut off according to load priority to ensure power supply to critical loads.

6. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 5, characterized in that, In grid-connected mode when P pv > P load + P loss When there is a power surplus, the surplus power is preferentially used for charging the dual-energy storage unit. When the energy storage SOC is ≥ 95%, the remaining surplus power is fed into the grid through the DC / AC unit. when P pv < P load + P loss When there is a power deficit, it is preferentially supplemented by the discharge of the dual-energy storage unit. When the energy storage SOC ≤ 20%, the remaining deficit is supplemented by the grid through rectification by the DC / AC conversion unit.

7. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 1, characterized in that, The energy management unit takes the dynamic balance of DC bus power as the core constraint, constructs a scheduling optimization model with multiple objectives, adapts to both grid-connected and off-grid operation modes, generates the optimal power scheduling plan, and sends it to the power routing control unit for execution. The multiple objectives include: minimum curtailment rate, minimum electricity cost, and minimum energy storage cycle loss.

8. The DC bus balance control system for the photovoltaic-storage AC / DC microgrid according to claim 7, characterized in that, The scheduling optimization model includes: a multi-objective optimization objective function and hard constraints; The multi-objective optimization objective function is: the comprehensive optimization objective is the weighted minimization of each sub-objective, as shown in the following formula: ； in, To comprehensively optimize the target value, These are the weighting coefficients for each sub-objective. For the light waste rate target item, For electricity cost target item, The target item for energy storage cycle loss; The hard constraints include: power balance constraints, equipment safe operation constraints, and off-grid load priority constraints.

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