Multi-level Charge and Discharge Management and Control System for Photovoltaic Energy Storage System

Abstract

The present invention belongs to the technical field of photovoltaic energy storage, and discloses a multi-level charge and discharge management control system for a photovoltaic energy storage system; it includes: a microgrid layer: used to build a full-bridge - half-bridge hybrid LLC resonant circuit; collect the original data of the power supplier, power consumer, and photovoltaic energy storage system through the full-bridge - half-bridge hybrid LLC resonant circuit; also install blockchain communication modules for each power supplier, power consumer, and photovoltaic energy storage system and configure parameters; the configured parameters include node ID, preset smart contract template, and preset communication protocol; a blockchain layer: used to add a timestamp and device signature to the original data based on the preset smart contract template, and then broadcast it to a blockchain network integrating a DAG + PoS consensus mechanism for storage; while ensuring data security, improving energy utilization efficiency, and enhancing grid stability, the present invention creates significant economic benefits and sustainable development value for each participating entity.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage, and more specifically, to a multi-level charge and discharge management and control system for a photovoltaic energy storage system. Background Art

[0002] Existing photovoltaic energy storage systems mostly rely on a centralized controller for unified scheduling, which has a single-point failure risk, is difficult to adapt to the dynamic scenarios of large-scale access of distributed energy, and has insufficient cross-level energy optimization capabilities.

[0003] Chinese Patent Application No. CN116388247A discloses a control method for a photovoltaic energy storage system and a photovoltaic energy storage system: The photovoltaic energy storage system includes: a photovoltaic inverter module and a plurality of energy storage modules. The photovoltaic input end of the photovoltaic inverter module is used for electrically connecting with photovoltaic modules. The DC bus port of the photovoltaic inverter module is also connected to each of the energy storage modules. The AC port of the photovoltaic inverter module is used for electrically connecting with the power grid or a load; The photovoltaic inverter module is also communicatively connected to each energy storage module through a communication bus; The control method is applied to the photovoltaic inverter module, and the control method includes: when operating in an off-grid mode, obtaining the current bus voltage on the DC bus port; determining a voltage adjustment amount according to the current bus voltage and a reference bus voltage; generating a voltage adjustment signal to each energy storage module according to the voltage adjustment amount; The voltage adjustment signal is used to instruct the energy storage module to adjust the charge and discharge current of the energy storage module according to the voltage adjustment amount. This invention improves the accuracy of the first reference current, which is beneficial to subsequent precise control of the charge and discharge current of the energy storage module.

[0004] Although the above method can meet most scenarios, through research and practical application of the above method and the existing technology, it is found that the above method and the existing technology have at least the following partial defects:

[0005] Lack of a decentralized architecture, difficult to adapt to distributed energy scenarios, with a single-point failure risk; user electricity consumption data is prone to leakage; it is impossible to achieve efficient multi-level energy interaction; it is difficult to balance the requirements of a wide voltage range and high efficiency.

[0006] In view of this, the present invention proposes a multi-level charge and discharge management and control system for a photovoltaic energy storage system to solve the above problems. Summary of the Invention

[0007] In order to overcome the above defects of the existing technology and to achieve the above object, the present invention provides the following technical solution: A multi-level charge and discharge management and control system for a photovoltaic energy storage system, including:

[0008] Microgrid layer: Used to build a full-bridge - half-bridge hybrid LLC resonant circuit; collect the original data of the supplier, the demander, and the photovoltaic energy storage system through the full-bridge - half-bridge hybrid LLC resonant circuit; also install blockchain communication modules for each supplier, demander, and photovoltaic energy storage system and configure parameters; the configured parameters include node ID, preset smart contract template, and preset communication protocol;

[0009] Blockchain layer: Used to add timestamps and device signatures to the original data based on the preset smart contract template, and then broadcast it to the blockchain network integrating the DAG+PoS consensus mechanism for storage;

[0010] Matching layer: Extract and decrypt the original data from the preset smart contract template of the blockchain, splice the original data as a label vector, calculate the matching degree score of the label vectors of the supply and demand sides based on the Jaccard-TOPSIS algorithm, and obtain all the suppliers and demanders corresponding to when the matching degree score is higher than the preset score threshold;

[0011] Decision-making layer: Used to optimize the matching of all the suppliers and demanders obtained by the matching layer based on the competition and cooperation game strategy, obtain the optimal control strategy, generate control instructions according to the optimal control strategy, and transmit them to the microgrid layer through the blockchain network for execution.

[0012] Furthermore, the method for obtaining the optimal control strategy includes:

[0013] Step 1: Construct the state space: The state space includes the original data of the photovoltaic energy storage system, the original data of the supplier, the original data of the demander, and the matching degree score;

[0014] Step 2: Construct the action space: It includes the many-to-many transaction combinations and transaction parameters between P suppliers and Q demanders screened by the matching layer. The transaction parameters include the power supply quantity, power supply period, power supply price of each supplier, and the corresponding demander. Among them, the power supply period meets the preset charge and discharge rate limits of the battery, and the lower limit of the power supply price is the marginal cost, and the upper limit is the sum of the electricity price and the preset floating price;

[0015] Step 3: Construct the strategy space: Define the supplier strategy set and the demander strategy set. The supplier strategy set includes the sealed bid strategy and the dynamic price reduction strategy; among them, the supplier generates a sealed bid according to the matching degree score and the marginal cost: the winning bid judgment criterion for the supplier is: select the supplier with the smallest sealed bid, and its final income is the second lowest price; obtain the dynamic price reduction strategy according to the marginal cost and the current health state of the battery;

[0016] The demander strategy set includes the dynamic Dutch auction strategy and the fixed price strategy; the dynamic Dutch auction strategy dynamically adjusts the initial price based on the supply-demand ratio and the grid state;

[0017] The fixed - price strategy is calculated based on the estimated electricity consumption of the demand - side itself; the condition for the demand - side to trigger a transaction is that the price corresponding to the moment is not less than the reserved price preset by the demand - side;

[0018] Dynamically allocate the initial strategy weights of each strategy set based on historical transaction data and grid load status;

[0019] Step 4: Divide the suppliers according to the SOH. Suppliers with an SOH not lower than the preset SOH threshold are classified as high - response type, and suppliers with an SOH lower than the preset SOH threshold are classified as high - reliability type to obtain suppliers at different levels; Build alliances based on suppliers at different levels. The alliance includes a supplier alliance composed of each level alone and a supplier alliance composed of all suppliers obtained from the matching layer; Calculate the marginal contribution of the formed alliance: Allocate the benefits according to the proportion of the marginal contributions of different suppliers in the alliance to the total marginal contribution; For suppliers with malicious price - hiking or monopolistic behaviors, introduce a secondary penalty term for marginal contribution punishment; Iteratively calculate the change value of the marginal contribution. If it exceeds the preset equilibrium threshold, trigger the adjustment of the strategy weights of the strategy set;

[0020] Step 5: If the SOH decay of the supplier exceeds the preset decay threshold, prohibit the corresponding supplier from participating in the matching; Update the supplier credit value in combination with historical transaction rates and penalty records;

[0021] Step 6: Define a reward function according to transaction efficiency, revenue stability, revenue, and supplier price - hiking punishment;

[0022] Step 7: Update the strategy weights using the Q - learning algorithm;

[0023] Step 8: Repeat updating the Q - value function until the Q - value converges to obtain the corresponding optimal Q - value function. According to the environmental state, use the optimal Q - value function to select the optimal action from the action space as the optimal control strategy.

[0024] Furthermore, the method for obtaining the matching degree score of the label vectors of the supply - demand sides includes:

[0025] Extract discrete labels from the supplier label vector and the demand - side label vector, and form sets A and B respectively; Obtain the number of discrete labels in sets A and B respectively; Calculate the Jaccard similarity between sets A and B;

[0026] Extract continuous labels from the supplier label vector and the demand - side label vector, and construct a decision matrix with a dimension of where is the number of suppliers; is the number of continuous labels; is the number of continuous labels;

[0027] Perform standardization processing on the decision matrix to obtain a standardized matrix ;

[0028] Dynamically adjust the weights of continuous tags based on a nature-inspired optimization algorithm to obtain a weight vector ;

[0029] Multiply the normalization matrix by the weight vector to obtain a standard matrix ;

[0030] Obtain the positive ideal solution by forming the maximum value of each column in the standard matrix ; obtain the negative ideal solution by forming the minimum value of each column in the standard matrix ;

[0031] Use the Euclidean distance formula to calculate the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution respectively; calculate the closeness to the positive ideal solution based on the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution; calculate the matching degree score according to the Jaccard similarity and the closeness

[0032] Furthermore, the method for building a full-bridge - half-bridge hybrid LLC resonant circuit includes:

[0033] The full-bridge half-bridge hybrid LLC resonant circuit includes a first MOSFET, a second MOSFET, a third MOSFET, a fourth MOSFET, a fifth MOSFET, a sixth MOSFET, a transformer, a resonant inductor, a resonant capacitor, a first diode, a second diode, and an excitation inductor; the drain of the first MOSFET is connected to the PV+ input terminal, the source is connected to the drain of the third MOSFET, and the gate is connected to the full-bridge drive signal PWM1; the drain of the second MOSFET is connected to the PV+ input terminal, the source is connected to the drain of the fourth MOSFET, and the gate is connected to the full-bridge drive signal PWM2; the drain of the third MOSFET is connected to the source of the first MOSFET and the drain is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM3; the drain of the fourth MOSFET is connected to the source of the second MOSFET, the source is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM4; one end of the resonant inductor is connected to the source of the first MOSFET and the drain of the third MOSFET, and the other end is connected to one end of the resonant capacitor; the other end of the resonant capacitor is connected to the source of the second MOSFET and the drain of the fourth MOSFET; the upper end of the primary winding of the transformer is connected to the source of the first MOSFET and the drain of the third MOSFET, and the lower end is connected to the source of the second MOSFET and the drain of the fourth MOSFET; the upper end of the secondary winding of the transformer is connected to the anode of the first diode, the center tap is the positive end of the DC bus, and the lower end is connected to the anode of the second diode; the cathodes of the first diode and the second diode are both connected to one end of the excitation inductor, and the other end of the excitation inductor is the battery Bat+ port; the drain of the fifth MOSFET is connected to the battery Bat+ port, the source is connected to the negative end of the DC bus, and the gate is connected to the full-bridge drive signal PWM5; the drain of the sixth MOSFET is connected to the negative end of the DC bus, the source is connected to the battery Bat- port, and the gate is connected to the full-bridge drive signal PWM6.

[0034] Further, the first MOSFET, the second MOSFET, the third MOSFET, the fourth MOSFET, the fifth MOSFET, and the sixth MOSFET are all N-channel types; a Rogowski coil is embedded in the secondary winding of the transformer.

[0035] Further, the original data of the photovoltaic energy storage system includes output power, voltage, current, and temperature;

[0036] The original data of the supplier includes power supply quantity, SOC, SOH, charge and discharge current, and charge and discharge voltage;

[0037] The original data of the demander includes required power quantity, power consumption, voltage, and frequency.

[0038] Further, the method for obtaining SOH includes:

[0039] The impedance value of the supplier's energy storage device is measured by a sweep frequency signal generated by a Rogowski coil embedded in the secondary winding of the transformer, and the SOH is calculated and obtained.

[0040] Furthermore, the method for obtaining the SOC includes:

[0041] The SOC is obtained by performing real-time integration on the charge and discharge current in combination with the battery rated capacity, wherein the battery rated capacity is adjusted in combination with the battery initial capacity according to the SOH.

[0042] Furthermore, the method for integrating the blockchain network into the consensus mechanism of DAG+PoS includes:

[0043] Taking the DAG structure as the underlying transaction processing layer of the blockchain network;

[0044] Taking PoS as the upper-layer global consensus layer of the blockchain network; verifying nodes are elected through stake pledging and are responsible for the final transaction confirmation between the supplier and the demander and the chain state synchronization;

[0045] Embedding the stake weight of PoS in the DAG structure; the verification priority of each node in the blockchain network is determined by the product value of the coin holding amount and the activity, and the greater the product value, the higher the verification priority; wherein, the coin holding amount is obtained through transactions, and the activity is obtained through participating in transaction verification and correct verification; nodes obtain the transaction verification right by pledging a preset number of tokens, and the verified transactions are propagated in parallel through the DAG;

[0046] Dynamically adjusting the edge weight of the DAG through the stake value of PoS.

[0047] Furthermore, when a fork occurs in the DAG structure, the branch with the highest weight is preferentially selected, and the transactions of the remaining branches are rolled back to the pending confirmation pool according to the timestamp.

[0048] Furthermore, the blockchain network encrypts the original data using the national cryptography SM4 algorithm, and the key is dynamically allocated through a quantum random number generator; the electricity consumption data of the demander is processed through homomorphic encryption, and the supplier only obtains the aggregated result of the demander.

[0049] The technical effects and advantages of the multi-level charge and discharge management and control system of the photovoltaic energy storage system of the present invention:

[0050] Through the decentralized architecture and blockchain smart contracts, the present invention effectively solves the single-point failure risk of traditional centralized control and improves the system robustness in the distributed energy scenario; the integrated homomorphic encryption and quantum key technologies achieve the full-process privacy protection of user electricity consumption data; the dynamic energy scheduling mechanism based on the competition-cooperation game strategy and fuzzy matching algorithm significantly enhances the supply-demand matching efficiency and flexibility among multiple levels; the application of the full-bridge half-bridge hybrid LLC resonant circuit takes into account the wide voltage range adaptability and power conversion efficiency. Ultimately, while ensuring data security, improving energy utilization efficiency, and enhancing grid stability, it creates significant economic benefits and sustainable development value for each participating entity. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 FIG. is a schematic diagram of the multi-level charge and discharge management control system of the photovoltaic energy storage system of the present invention;

[0052] Figure 2 FIG. is a schematic diagram of the full-bridge half-bridge hybrid LLC resonant circuit of the present invention;

[0053] Figure 3 FIG. is a schematic diagram of the method flow for obtaining the optimized control strategy of the present invention;

[0054] Figure 4 FIG. is a closed-loop diagram of the optimized control strategy of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Please refer to Figure 1 As shown, the multi-level charge and discharge management control system of the photovoltaic energy storage system in this embodiment includes:

[0058] Microgrid layer: used to build a full-bridge half-bridge hybrid LLC resonant circuit; collect the original data of the supplier, demander, and photovoltaic energy storage system through the full-bridge half-bridge hybrid LLC resonant circuit; also install blockchain communication modules and configure parameters for each supplier, demander, and photovoltaic energy storage system; the configured parameters include node ID, preset smart contract template (such as defining electricity trading fields (including capacity, price, time window, and SOH, etc.)) and preset communication protocol;

[0059] Refer to Figure 2, a method for building a full-bridge half-bridge hybrid LLC resonant circuit includes:

[0060] The full-bridge half-bridge hybrid LLC resonant circuit includes a first MOSFET Q1, a second MOSFET Q2, a third MOSFET Q3, a fourth MOSFET Q4, a fifth MOSFET Q5, a sixth MOSFET Q6, a transformer T1, a resonant inductor Lr, a resonant capacitor Cr, a first diode D1, a second diode D2, and an exciting inductor Lm; the drain of the first MOSFET Q1 is connected to the PV+ input terminal, the source is connected to the drain of the third MOSFET Q3, and the gate is connected to the full-bridge drive signal PWM1; the drain of the second MOSFET Q2 is connected to the PV+ input terminal, the source is connected to the drain of the fourth MOSFET Q4, and the gate is connected to the full-bridge drive signal PWM2; the drain of the third MOSFET Q3 is connected to the source of the first MOSFET Q1 and the drain is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM3; the drain of the fourth MOSFET Q4 is connected to the source of the second MOSFET Q2, the source is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM4; one end of the resonant inductor Lr is connected to the source of the first MOSFET Q1 and the drain of the third MOSFET Q3, and the other end is connected to one end of the resonant capacitor Cr; the other end of the resonant capacitor Cr is connected to the source of the second MOSFET Q2 and the drain of the fourth MOSFET Q4; the upper end of the primary winding of the transformer T1 is connected to the source of the first MOSFET Q1 and the drain of the third MOSFET Q3, and the lower end is connected to the source of the second MOSFET Q2 and the drain of the fourth MOSFET Q4; the upper end of the secondary winding of the transformer T1 is connected to the anode of the first diode D1, the center tap is the positive end of the DC bus, and the lower end is connected to the anode of the second diode D2; the cathodes of the first diode D1 and the second diode D2 are both connected to one end of the exciting inductor Lm, and the other end of the exciting inductor Lm is the battery Bat+ port; the drain of the fifth MOSFET Q5 is connected to the battery Bat+ port, the source is connected to the negative end of the DC bus, and the gate is connected to the full-bridge drive signal PWM5; the drain of the sixth MOSFET Q6 is connected to the negative end of the DC bus, the source is connected to the battery Bat- port, and the gate is connected to the full-bridge drive signal PWM6.

[0061] The full-bridge half-bridge hybrid LLC resonant circuit can comprehensively and accurately collect the data of the supplier and the demander by establishing three ports: the input terminal of the photovoltaic power supply, the positive and negative terminals of the DC bus, and the positive and negative terminals of the battery. Taking the supplier as an example, the input terminal of the photovoltaic power supply can input the direct current generated by the photovoltaic modules into the system as the main energy source of the system, and the positive and negative terminals of the battery can serve as the energy output interface of the energy storage unit. Taking the demander as an example, the positive and negative terminals of the DC bus can serve as the core hub for system energy distribution, receiving the electric energy input from the photovoltaic or battery; the positive and negative terminals of the battery can serve as the energy input interface of the energy storage unit. Taking the photovoltaic energy storage system as an example, the input terminal of the photovoltaic power supply can serve as the only channel for photovoltaic energy to enter the system, and the positive and negative terminals of the DC bus can serve as the intermediate node for photovoltaic energy distribution. When in the power supply / charging state, the energy flow path is from the input terminal of the photovoltaic power supply to the positive and negative terminals of the DC bus, and then to the positive and negative terminals of the load / battery; when in the discharge state, the energy flow path is from the positive and negative terminals of the battery to the positive and negative terminals of the DC bus, and then to the load. The full-bridge half-bridge hybrid LLC resonant circuit can dynamically distribute energy to the bus by adjusting the MOSFET switches (such as adjusting PWM1 - PWM6), achieving the supply-demand balance among the photovoltaic, battery, and load. Each endpoint can also provide real-time status feedback for the system to support the final optimization control, etc. The full-bridge half-bridge hybrid LLC resonant circuit combines the high power processing ability of the full-bridge circuit with the cost-effectiveness of the half-bridge circuit to achieve efficient power conversion under a wide input voltage range, supporting the bidirectional energy flow between the photovoltaic battery, the energy storage unit, and the power grid. By utilizing the LLC resonance characteristics, soft switching is achieved, reducing the switching loss and improving the system efficiency. At the same time, the voltage and current waveforms are optimized to reduce harmonic pollution, ensuring the stability and reliability of the charge and discharge process. Its multi-port design can flexibly adapt to the access requirements of distributed energy sources, and can cooperate with multi-level control strategies to improve the overall energy efficiency and operation economy of the energy storage system.

[0062] The original data of the photovoltaic energy storage system includes output power, voltage, current, and temperature. By monitoring the output power, voltage, current, and temperature, it provides the basic data for energy source and stability constraints for charge and discharge management, ensuring the efficient operation of the system.

[0063] The original data of the supplier includes power supply quantity, SOC, SOH, charge and discharge current, and charge and discharge voltage. By collecting the power supply quantity, SOC, SOH, charge and discharge current, and voltage, it reflects the available capacity, health status, and charge and discharge ability of the supplier, providing a basis for optimizing the energy storage regulation strategy.

[0064] The methods for obtaining SOH include:

[0065] Measuring the impedance value of the supplier's energy storage device through the swept-frequency signal generated by the Rogowski coil embedded in the secondary winding of transformer T1, and calculating to obtain SOH, such as ; among which, is the battery AC impedance value obtained through testing; is the AC impedance value when the battery is in a brand-new state; is the AC impedance value when the battery reaches the end-of-life state. Calculating the SOH value can provide core data support for the adjustment of the charge and discharge strategy of the energy storage system, the equipment maintenance plan, and the reliability analysis, and help optimize the use efficiency and safety of the energy storage equipment.

[0066] The methods for obtaining the SOC include:

[0067] obtaining the SOC by integrating the charge and discharge current in real time in combination with the rated capacity of the battery; such as ; among which, is the rated capacity of the battery, and the rated capacity of the battery is adjusted according to the SOH in combination with the initial capacity of the battery ; such as ; is the charge and discharge efficiency; is the real-time charge and discharge current, negative for charging and positive for discharging; the extended Kalman filter algorithm can be further combined to dynamically correct the SOC based on the Thevenin equivalent circuit model. By integrating the charge and discharge current and combining the rated capacity adjusted considering the SOH (state of health), the remaining battery power is quantified in real time, providing a key basis for formulating the charge and discharge strategy; also, with the help of the extended Kalman filter algorithm and the equivalent circuit model, the calculation error of the SOC can be dynamically corrected, avoiding the cumulative deviation of the integration, and improving the accuracy and reliability of the SOC result.

[0068] The original data of the demander includes the required electricity quantity, power consumption, voltage, and frequency; by collecting the required electricity quantity, power consumption, voltage, and frequency, the load demand and the grid state are fed back in real time, driving the supply-demand matching and dynamic response, and realizing the precise distribution of energy.

[0069] Blockchain layer: used to add a timestamp and device signature to the original data based on a preset intelligent contract template, and then broadcast it to the blockchain network integrating the DAG+PoS consensus mechanism for storage;

[0070] The method for integrating the DAG+PoS consensus mechanism into the blockchain network includes:

[0071] Taking the DAG structure as the underlying transaction processing layer of the blockchain network;

[0072] Taking the PoS as the upper-layer global consensus layer of the blockchain network; validating nodes are elected through stake pledging, responsible for the final transaction confirmation between the supplier and the demander and the chain state synchronization;

[0073] Embed the stake weight of PoS in the DAG structure; the verification priority of each node in the blockchain network is determined by the coin holding amount and activity; among them, the coin holding amount is obtained through transactions, and the activity is obtained through participating in transaction verification and correct verification; nodes obtain the right to verify transactions by staking a preset number of tokens, and the verified transactions are propagated in parallel through the branch structure of the DAG and are bound through the PoS mechanism, which can ensure the finality of the data on the node and prevent double-spending attacks;

[0074] Dynamically adjust the edge weight of the DAG through the stake value of PoS; the larger the coin holding amount of the node, the higher the weight of the transaction branch generated by it, and it is more likely to be referenced by subsequent nodes. This design not only retains the asynchronous concurrency advantage of the DAG, but also enhances the network stability through the economic incentive of PoS and avoids malicious node manipulation.

[0075] When a fork occurs in the DAG structure, the weights of each branch can be calculated by the product of the number of transactions and the stake value of the verification node, and the branch with the highest weight is preferentially selected, and the transactions of the remaining branches are rolled back to the pending confirmation pool according to the timestamp. It is also possible to deduct 50% of the staked tokens from the verification nodes with malicious forks (such as the number of consecutive forks exceeding five times).

[0076] In terms of data storage, the DAG structure, as the underlying transaction processing layer, can achieve parallel propagation and efficient processing of transactions, can quickly store the original data of the supplier, the demander and the photovoltaic energy storage system, and ensure the timeliness of data records; PoS, as the upper-layer global consensus layer, elects verification nodes through stake pledge for transaction confirmation and chain state synchronization, improving the security and reliability of data storage. In transaction decision-making, the verification priority of nodes is determined by the coin holding amount and activity, which encourages each supplier to actively participate in the system and maintain a good transaction reputation. At the same time, dynamically adjusting the edge weight of the DAG can better adapt to the supply and demand changes and strategy adjustments in charge and discharge management control, enabling the system to achieve more reasonable supply and demand matching and generation of optimization control strategies based on accurate and timely data, and enhancing the stability and efficiency of the entire charge and discharge management control system.

[0077] The blockchain network encrypts the original data using the national cryptographic SM4 algorithm, and the key is dynamically allocated through a quantum random number generator. For example, if the NIST post-quantum cryptographic algorithm based on Lattice is adopted, the session key is updated every 5 minutes through a trusted execution environment (TEE); the electricity consumption data of the demander is processed by homomorphic encryption, and the supplier only obtains the aggregated result of the demander. For example, if the Paillier algorithm is used to encrypt the electricity consumption of the demander, the supplier can only perform aggregated calculations on the ciphertext of the demander's electricity consumption. The blockchain network encrypts the original data using the national cryptographic SM4 algorithm and combines the quantum random number generator with post-quantum cryptographic technology. The role in the charge and discharge management and control of the photovoltaic energy storage system is reflected in the following aspects: through the SM4 algorithm and the dynamic session key update mechanism, it ensures the confidentiality and integrity of the original data of the supplier, demander, and photovoltaic system during the transmission and storage process, and resists the risk of quantum attacks; homomorphic encryption technology (such as the Paillier algorithm) allows the electricity consumption data of the demander to be aggregated and calculated in the ciphertext state, protecting user privacy while supporting the supplier to generate supply-demand matching strategies based on the encrypted data; the combination of the trusted execution environment (TEE) and the quantum random number generator ensures the security and randomness of key distribution, enhances the anti-attack ability of the system, provides a safe and reliable data basis for charge and discharge management and control, ensures that the decision-making layer generates optimization strategies based on true and reliable data, and improves the efficiency and fairness of cross-level transactions.

[0078] Matching layer: Extract and decrypt the original data from the preset smart contract template of the blockchain, splice the original data as a label vector, and calculate the matching degree score of the label vectors of the supply and demand sides based on the Jaccard-TOPSIS algorithm. Obtain all the suppliers and demanders corresponding to when the matching degree score is higher than the preset score threshold;

[0079] The method for obtaining the matching degree score of the label vectors of the supply and demand sides includes:

[0080] Extract the discrete labels from the supplier label vector and the demander label vector, and form sets A and B respectively; obtain the number of discrete labels in sets A and B respectively, such as equipment type and SOH type, etc.; calculate the Jaccard similarity between sets A and B; such as ; where, is the supplier credit value, initially 1; by extracting the discrete labels (such as equipment type, SOH type, etc.) in the label vectors of the supply and demand sides to construct sets, calculating the Jaccard similarity between the sets, and introducing the supplier credit value for weighted adjustment, the matching degree score is finally formed; this process provides a data basis for the supply-demand matching of the photovoltaic energy storage system, helps the system screen out transaction combinations with high label attribute fit and reliable supplier credit, thereby improving the accuracy of resource matching and transaction efficiency, and optimizing the resource allocation logic in multi-level charge and discharge management.

[0081] Extract continuous labels from the supplier label vector and the demander label vector to construct a decision matrix with a dimension of ; , where is the number of suppliers; is the number of continuous labels;

[0082] Perform normalization processing on the decision matrix to obtain a normalized matrix ;

[0083] Dynamically adjust the weights of continuous labels based on a nature-inspired optimization algorithm to obtain a weight vector ;

[0084] Multiply the normalized matrix by the weight vector to obtain a standard matrix ;

[0085] Obtain the positive ideal solution by taking the maximum value of each column in the standard matrix labels, and obtain the negative ideal solution by taking the minimum value of each column in the standard matrix labels;

[0086] Use the Euclidean distance formula to calculate the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution respectively; If the distance from the supplier to the positive ideal solution is ; where is the th normalized continuous label of the th supplier; is the th positive ideal solution; The distance from the supplier to the negative ideal solution is ; where is the th negative ideal solution; Calculate the closeness to the positive ideal solution based on the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution; If the closeness is ; Calculate the matching degree score according to the Jaccard similarity and the closeness; If the matching degree score is ; where is the th supplier and the th demander 's Jaccard similarity; is the th supplier and the th demander Closeness. By constructing positive and negative ideal solutions, using the Euclidean distance formula to calculate the gap between the supplier's continuous labels and the ideal solutions, and then quantifying the degree of approximation of the supplier to the positive ideal solution through the closeness formula, the matching potential of continuous labels (such as numerical parameters like electricity quantity and price) can be explored; combining the Jaccard similarity of discrete labels (reflecting the matching of attributes such as equipment type and SOH type) with the closeness of continuous labels to form a matching score can comprehensively evaluate the fit between the supply and demand sides in terms of attribute characteristics and numerical parameters, providing data support for the optimal matching of supply and demand and the generation of trading pairs in the photovoltaic energy storage system, and improving the accuracy and rationality of resource allocation in multi-level charge and discharge management.

[0087] A method for calculating the matching score of the label vectors of the supply and demand sides based on the Jaccard-TOPSIS algorithm combines the advantages of the Jaccard coefficient for measuring the similarity of discrete labels and the TOPSIS method for evaluating the closeness of continuous labels, and can accurately identify the matching degree between the supply and demand sides. By setting an appropriate preset score threshold, supply and demand combinations with high adaptability can be quickly screened out, enabling the system to allocate resources more targeted when formulating charge and discharge management strategies, improving the energy utilization efficiency, reducing energy waste, promoting more efficient transactions between the supply and demand sides at the same time, ensuring the supply and demand balance and optimal control in the charge and discharge process of the entire photovoltaic energy storage system, and enhancing the stability and economy of system operation.

[0088] Decision-making layer: Used to perform optimal matching on all suppliers and demanders obtained by the matching layer based on the competition and cooperation game strategy, obtain an optimal control strategy, generate control instructions according to the optimal control strategy, and transmit them to the microgrid layer for execution through the blockchain network.

[0089] Refer to Figures 3 - 4 , the methods for obtaining the optimal control strategy include:

[0090] Step 1: Construct the state space: The state space includes the original data of the photovoltaic energy storage system, the original data of all suppliers obtained by the matching layer, the original data of all demanders obtained by the matching layer, the matching score, and the electricity prices at different time periods obtained through networking based on the blockchain network;

[0091] Step 2: Construct the action space: It includes the many-to-many trading combinations and trading parameters between P suppliers and Q demanders screened by the matching layer. The trading parameters include the power supply quantity, power supply time period, power supply price of each supplier and the corresponding demander. Among them, the power supply time period meets the preset charge and discharge rate limits of the battery, and the lower limit of the power supply price is the marginal cost, and the upper limit is the sum of the electricity price and the preset floating price;

[0092] Step 3: Construct the strategy space: Define the supplier strategy set and the demander strategy set. The supplier strategy set includes the sealed bid strategy and the dynamic price reduction strategy; among them, the supplier According to the The matching degree score of a demander and the marginal cost to generate a sealed bid: such as the sealed bid ; where is the marginal cost of the th supplier, which is inversely proportional to the battery SOH state; is the bid elasticity coefficient; by combining the supplier's marginal cost and the supply-demand matching degree to generate a sealed bid, it can enable suppliers with a high matching degree to obtain a premium space in the bid, reflecting the influence of the matching degree on the bidding strategy; the winning bid determination criterion for the supplier is: select the supplier with the smallest sealed bid, and its final income is the second lowest price; such as the second lowest price ; where is the matching degree reward coefficient; is the matching degree benchmark value; by calculating the final income of the winning bid supplier, on the basis of the second lowest price (the highest bid of other suppliers), adding the matching degree reward item can encourage the supplier to improve the matching degree.

[0093] Obtain a dynamic price reduction strategy according to the marginal cost of the battery and the current health state; such as calculating the base price ; where is the total cost of the th supplier; is the th supplier's remaining available power supply; according to the supplier's total cost, the battery health state SOH and the remaining available power supply, calculate the base price of the dynamic price reduction, making the price of suppliers with a poor battery health state and high costs more flexible; calculate the dynamically adjusted price through the base price ; where is the real-time cost coefficient; dynamically adjusting the supplier's bid based on the base price and the real-time cost coefficient can adapt to the real-time cost change.

[0094] The demander's strategy set includes a dynamic Dutch auction strategy and a fixed price strategy; the dynamic Dutch auction strategy dynamically adjusts the initial price based on the supply-demand ratio and the grid state; such as the initial price ; where is the load sensitivity coefficient; decrease the price exponentially according to time; by dynamically adjusting the auction initial price, it can reflect the influence of the grid load on the demander's pricing; is the time's actual grid load; is the rated capacity of the grid; such as ; where is the time's corresponding price; is the attenuation rate; is a mathematical constant; making the demander's price decrease exponentially with time can encourage the supplier to respond earlier and promote the transaction efficiency.

[0095] The fixed - price strategy is calculated based on the estimated electricity consumption of the demander itself; for example, the initial fixed price ; where is the electricity consumption budget of the demander; is the estimated electricity consumption of the demander; based on the electricity consumption budget and the estimated electricity consumption of the demander, calculate the base price of the fixed - price strategy, which can ensure that the price matches the demand; add a certain price - floating space to the initial fixed price to obtain the fixed price ; where is the budget elasticity coefficient; adding the budget elasticity coefficient to the base price forms a price - fluctuation space to adapt to the change of the demander's budget.

[0096] The condition for the demander to trigger a deal is that the price corresponding to the moment is not less than the reservation price preset by the demander; for example ; where is the th reservation price preset by the demander;

[0097] Dynamically allocate the initial strategy weights of each strategy set based on historical transaction data and grid load status; for example, the weight of the supplier strategy set ; based on the historical highest revenue and the standard deviation of revenue, allocate the initial weight of the supplier strategy through the softmax function to highlight the advantages of high - revenue and low - volatility strategies; the weight of the demander strategy set ; where is the historical highest revenue; is the standard deviation of the historical highest revenue; is the grid load rate; is the standard deviation of the grid load rate; allocate the initial weight of the demander strategy according to the grid load rate and the standard deviation of the load rate, so that the strategy weight is associated with the grid load status.

[0098] Step 4: Divide the suppliers according to the SOH, divide the suppliers not lower than the preset SOH threshold into high - response types, and divide the suppliers lower than the preset SOH threshold into high - reliability types to obtain suppliers at different levels; construct alliances based on suppliers at different levels , the alliance includes the supplier alliance composed of each level separately and the supplier alliance composed of all suppliers obtained from the matching layer; calculate the marginal contribution of the formed alliance : ; where represents the alliance formed by any subset of the remaining suppliers after removing the supplier from the alliance composed of all suppliers obtained from the matching layer ; is the alliance The number of suppliers included; The total number of suppliers among all suppliers obtained for the matching layer; For the alliance The value when operating independently, obtained by weighting the matching score; For the joining supplier After that, the alliance The value; For the alliance The weight; distribute the benefits according to the proportion of the marginal contribution of different suppliers among the suppliers; by quantifying the Marginal contribution of the supplier in the alliance By comparing the value before and after the supplier joins the alliance It can provide a basis for the distribution of alliance benefits.

[0099] For suppliers with malicious price hikes or monopoly behaviors, introduce a secondary penalty term to punish the marginal contribution. For example, the penalty value that the th supplier needs to bear due to malicious price hikes or monopoly behaviors ; The penalty intensity coefficient; For the th supplier's sealed bid; The expected value of all suppliers' sealed bids; for suppliers with malicious price hikes Calculate the penalty value based on its bid And the deviation from the average bid expectation , combined with the penalty intensity To suppress bad market behaviors.

[0100] Iteratively calculate the change value of the marginal contribution , if it exceeds the preset equilibrium threshold, trigger the adjustment of the strategy weights of the strategy set;

[0101] Step 5. If the SOH decay of the supplier exceeds the preset decay threshold, prohibit the corresponding supplier from participating in the matching; update the supplier's credit value by combining the historical transaction success rate and penalty records; such as the credit value ; where Is the number of transactions successfully completed by the th supplier among the P suppliers in history; Is the total number of times the th supplier among the P suppliers participated in the transaction bidding or quotation; Is the penalty ratio of the th supplier among the P suppliers; by combining the supplier's historical transaction success rate And the penalty ratio , update the supplier's credit value, which can encourage the supplier to maintain a good transaction record.

[0102] Step 6: Define the reward function based on transaction efficiency, revenue stability, revenue, and supplier price hike penalty; such as the reward function ; where , , and are all weight coefficients, dynamically adjusted according to the system optimization focus; is the transaction efficiency, , is the actual transaction power; is the total demand power; is the revenue stability, , is the revenue standard deviation; is the revenue mean value, used to measure the revenue fluctuation situation. The closer the value of the revenue mean is to 1, the stronger the revenue stability; is the system revenue, , is the peak shaving and valley filling power; is the peak electricity price; is the valley electricity price; is the supplier joint price hike penalty, , is the expected value of all supplier quotes , that is, the average value; the comprehensive transaction efficiency , revenue stability , system revenue and supplier joint price hike penalty form a multi-objective reward function, which can guide the system to optimize in the direction of high efficiency, stability, and fairness.

[0103] Step 7: Update the policy weights using the Q-learning algorithm:

[0104] ;

[0105] where is the updated Q value; is the Q value of taking action at the environmental state ; action represents selecting a supplier from the supplier strategy set and a demander from the demander strategy set at time ; is the learning rate; is the reward function value at time ; is the next environmental state under the optimal selection action among all possible selection actions; is the next environmental state All possible selection actions below The maximum Q value corresponding to it, that is, after transferring from the current environmental state to the next environmental state, the maximum expected cumulative reward that can be obtained among all optional selection actions; based on the Q-learning algorithm, according to the current Q value , reward and the maximum Q value of the next state , update the Q value corresponding to the policy, and iteratively optimize the policy weights, which can improve the system's decision-making ability.

[0106] Step 8: Repeat updating the Q value function until the Q value converges, obtain the corresponding optimal Q value function, and according to the environmental state, use the optimal Q value function to select the optimal action from the action space as the optimization control strategy.

[0107] The co-opetition game strategy fully considers the interest relationship between the supplier and the demander in cooperation and competition, and can achieve the optimal matching of both supply and demand in a complex market environment. The control strategy obtained through this optimal matching can accurately guide the charging and discharging operations of the photovoltaic energy storage system, reasonably allocate energy resources, and improve energy utilization efficiency. At the same time, the blockchain network ensures the security, reliability, and immutability of the transmission of control instructions, ensuring that the microgrid layer can accurately execute the instructions, thereby effectively balancing the supply and demand relationship, reducing operating costs, and enhancing the stability and economic benefits of the entire photovoltaic energy storage system.

[0108] Embodiment 2

[0109] This embodiment provides a dynamic reputation fusion strategy, including:

[0110] Embed the supplier reputation value into the sealed-bid strategy, and the higher the reputation, the higher the premium can be obtained; such as ; where is the reputation gain coefficient; is the th supplier's reputation value. Embedding the supplier reputation value into the sealed-bid strategy can guide the supplier to actively maintain its reputation through economic incentives, prompting it to improve service quality, reduce defaults or malicious behaviors; high-reputation suppliers obtain premium returns, reflecting the market's recognition of honest entities, suppressing low-quality competition, and enhancing market fairness; in the long run, a virtuous cycle of "high reputation - high income - more integrity" can be constructed, optimizing the trading environment of the photovoltaic energy storage system, ensuring the efficiency of supply and demand matching and the stable operation of the market, and promoting the industry to develop in a more standardized and high-quality direction.

[0111] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the said claims.

[0112] Finally: The above description is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Multi-level charge and discharge management and control system for a photovoltaic energy storage system, characterized in that, Including: Microgrid layer: used to build a full-bridge - half-bridge hybrid LLC resonant circuit; Collect the original data of the supplier, demander, and photovoltaic energy storage system through the full-bridge - half-bridge hybrid LLC resonant circuit; also install blockchain communication modules for each supplier, demander, and photovoltaic energy storage system and configure parameters; the configured parameters include node ID, preset smart contract template, and preset communication protocol; Blockchain layer: used to add timestamps and device signatures to the original data based on the preset smart contract template, and then broadcast it to the blockchain network integrated with the DAG+PoS consensus mechanism for storage; Matching layer: extract and decrypt the original data from the preset smart contract template of the blockchain, splice the original data as a label vector, and calculate the matching degree score of the label vectors of the supply and demand sides based on the Jaccard-TOPSIS algorithm: Extract discrete labels from the supplier label vector and the demander label vector, and form sets A and B respectively; obtain the number of discrete labels in sets A and B respectively; calculate the Jaccard similarity between sets A and B; Extract consecutive tags from the supplier label vector and the demander label vector to construct a decision matrix with a dimension of The decision matrix , is the number of suppliers; is the number of consecutive tags; For the decision matrix perform normalization to obtain the normalized matrix ; The weights of continuous tags are dynamically adjusted based on a nature-inspired optimization algorithm to obtain a weight vector ; Multiply the normalization matrix by the weight vector to obtain the standard matrix ; Obtain the standard matrix The maximum value of each column in constitutes the positive ideal solution, and the minimum value of each column in Use the Euclidean distance formula to calculate the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution respectively; calculate the closeness to the positive ideal solution based on the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution; calculate the matching degree score according to the Jaccard similarity and the closeness; Obtain all suppliers and demanders corresponding to the matching degree score higher than the preset score threshold; Decision-making layer: used to optimize the matching of all suppliers and demanders obtained by the matching layer based on the competition and cooperation game strategy, obtain the optimized control strategy, generate control instructions according to the optimized control strategy, and transmit them to the microgrid layer through the blockchain network for execution.

2. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 1, wherein, The method for obtaining the optimized control strategy includes: Step 1, construct the state space: the state space includes the original data of the photovoltaic energy storage system, the original data of the supplier, the original data of the demander, and the matching degree score; Step 2, construct the action space: including the many-to-many transaction combinations and transaction parameters between P suppliers and Q demanders screened by the matching layer, and the transaction parameters include the power supply amount, power supply period, power supply price of each supplier and the corresponding demander, where the power supply period satisfies the preset charge and discharge rate limit of the battery, and the lower limit of the power supply price is the marginal cost, and the upper limit is the sum of the electricity price and the preset floating price; Step 3, construct the strategy space: define the supplier strategy set and the demander strategy set, the supplier strategy set includes the sealed bid strategy and the dynamic price reduction strategy; among them, the supplier generates a sealed bid according to the matching degree score and the marginal cost: the winning bid judgment standard for the supplier is: select the supplier with the smallest sealed bid, and its final income is the second lowest price; obtain the dynamic price reduction strategy according to the marginal cost of the battery and the current health state; The demander strategy set includes the dynamic Dutch auction strategy and the fixed price strategy; the dynamic Dutch auction strategy dynamically adjusts the initial price based on the supply-demand ratio and the grid state; The fixed price strategy is calculated based on the expected electricity consumption of the demander itself; the condition for the demander to trigger a deal is The price corresponding to the moment is not less than the reservation price preset by the demander; Dynamically allocate the initial strategy weights of each strategy set based on historical transaction data and grid load status; Step 4: Divide suppliers according to SOH, divide suppliers whose SOH is not lower than the preset SOH threshold into high-responsive type, and divide suppliers whose SOH is lower than the preset SOH threshold into high-reliability type, and obtain suppliers of different levels; build alliances based on suppliers of different levels, and the alliances include the supplier alliances formed by each level and the supplier alliances formed by all suppliers obtained in the matching layer; calculate the marginal contribution of the formed alliance: distribute the benefits according to the proportion of the marginal contribution of different suppliers in the alliance to the total marginal contribution; introduce a secondary penalty term to punish marginal contributions of suppliers with malicious price-raising or monopolistic behavior; iteratively calculate the change value of the marginal contribution, and if it exceeds the preset equilibrium threshold, it triggers the adjustment of the strategy weight of the strategy set; Step 5: If the SOH decay of the supplier exceeds the preset decay threshold, the corresponding supplier is prohibited from participating in the matching; the supplier's reputation value is updated based on the historical transaction rate and penalty records; Step 6: Define the reward function based on transaction efficiency, revenue stability, revenue, and supplier price increase penalty; Step 7: Use the Q-learing algorithm to update the strategy weights; Step 8: Repeat the update of the Q value function until the Q value converges to obtain the corresponding optimal Q value function. According to the environmental state, use the optimal Q value function to select the optimal action from the action space as the optimization control strategy.

3. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 1, characterized in that, Methods for building a full-bridge-half-bridge hybrid LLC resonant circuit include: The full-bridge half-bridge hybrid LLC resonant circuit includes a first MOSFET, a second MOSFET, a third MOSFET, a fourth MOSFET, a fifth MOSFET, a sixth MOSFET, a transformer, a resonant inductor, a resonant capacitor, a first diode, a second diode and an excitation inductor; the drain of the first MOSFET is connected to the PV+ input terminal, the source is connected to the drain of the third MOSFET, and the gate is connected to the full-bridge drive signal PWM1; the drain of the second MOSFET is connected to the PV+ input terminal, the source is connected to the drain of the fourth MOSFET, and the gate is connected to the full-bridge drive signal PWM2; the drain of the third MOSFET is connected to the source of the first MOSFET and the drain is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM3; the drain of the fourth MOSFET is connected to the source of the second MOSFET, the source is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM4; one end of the resonant inductor is connected to the source of the first MOSFET and the drain of the third MOSFET, and the other end is connected to one end of the resonant capacitor; the other end of the resonant capacitor is connected to the source of the second MOSFET and the drain of the fourth MOSFET; the upper end of the primary winding of the transformer is connected to the source of the first MOSFET and the drain of the third MOSFET, and the lower end is connected to the source of the second MOSFET and the drain of the fourth MOSFET; the upper end of the secondary winding of the transformer is connected to the anode of the first diode, the center tap is the positive end of the DC bus, and the lower end is connected to the anode of the second diode; the cathodes of the first diode and the second diode are both connected to one end of the excitation inductor, and the other end of the excitation inductor is the battery Bat+ port; the drain of the fifth MOSFET is connected to the battery Bat+ port, the source is connected to the negative end of the DC bus, and the gate is connected to the full-bridge drive signal PWM5; the drain of the sixth MOSFET is connected to the negative end of the DC bus, the source is connected to the battery Bat- port, and the gate is connected to the full-bridge drive signal PWM6.

4. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 3, characterized in that, The first MOSFET, the second MOSFET, the third MOSFET, the fourth MOSFET, the fifth MOSFET, and the sixth MOSFET are all N-channel types; a Rogowski coil is embedded in the secondary winding of the transformer.

5. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 1, characterized in that The original data of the photovoltaic energy storage system includes output power, voltage, current and temperature; The original data of the supplier includes power supply, SOC, SOH, charge and discharge current and charge and discharge voltage; The original data of the demander includes required power, power consumption, voltage and frequency.

6. The multi-level charge and discharge management and control system for a photovoltaic energy storage system according to claim 5, characterized in that, The method for obtaining SOH includes: Measuring the impedance value of the supplier's energy storage device through the sweep signal generated by the Rogowski coil embedded in the secondary winding of the transformer, and calculating to obtain SOH.

7. The multi-level charge and discharge management control system of the photovoltaic energy storage system according to claim 1, wherein The method for obtaining SOC includes: Combining the rated capacity of the battery to obtain SOC by integrating the charge and discharge current in real time, wherein, according to SOH, the rated capacity of the battery is adjusted by combining the initial capacity of the battery.

8. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 1, wherein The method for integrating the blockchain network into the consensus mechanism of DAG+PoS includes: Taking the DAG structure as the underlying transaction processing layer of the blockchain network; Use PoS as the upper-layer global consensus layer of the blockchain network; verify nodes through staking, which are responsible for the transaction confirmation between the final supplier and the demander and the chain state synchronization; Embed the stake weight of PoS in the DAG structure; the verification priority of each node in the blockchain network is determined by the product value of the coin holding amount and the activity, and the larger the product value, the higher the verification priority; among them, the coin holding amount is obtained through transactions, and the activity is obtained by participating in transaction verification and correct verification; nodes obtain the transaction verification right by staking a preset number of tokens, and the verified transactions are propagated in parallel through the DAG; Dynamically adjust the edge weight of the DAG through the stake value of PoS.

9. The multi-level charge and discharge management control system of the photovoltaic energy storage system according to claim 8, characterized in that, When a fork occurs in the DAG structure, the branch with the highest weight is preferentially selected, and the transactions of the remaining branches are rolled back to the pending confirmation pool according to the timestamp.

10. The multi-level charge and discharge management and control system of the photovoltaic energy storage system according to claim 8, wherein, The blockchain network encrypts the original data using the national cryptography SM4 algorithm, and the key is dynamically allocated through a quantum random number generator; the electricity consumption data of the demander is processed through homomorphic encryption, and the supplier only obtains the aggregated result of the demander.

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