Multi-level charging and discharging management control system of photovoltaic energy storage system

Abstract

The invention belongs to the technical field of photovoltaic energy storage, and discloses a multi-level charging and discharging management control system of a photovoltaic energy storage system. Comprising a micro-grid layer which is used for constructing a full-bridge-half-bridge hybrid LLC resonant circuit; original data of a supplier, a demander and a photovoltaic energy storage system are collected through a full-bridge-half-bridge hybrid LLC resonant circuit; block chain communication modules are installed for each supplier, each demander and each photovoltaic energy storage system, and parameters are configured; the configured parameters comprise a node ID, a preset intelligent contract template and a preset communication protocol; the block chain layer is used for adding a timestamp and a device signature to the original data based on a preset intelligent contract template, and then broadcasting the original data to a block chain network of a DAG + PoS consensus mechanism for storage; according to the method, data safety is guaranteed, the energy utilization efficiency is improved, the stability of the power grid is enhanced, and meanwhile, remarkable economic benefits and sustainable development values are created for all participants.

Description

Technical Field

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

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

[0003] The Chinese patent application with publication number CN116388247A discloses a control method of a photovoltaic energy storage system and a photovoltaic energy storage system: the photovoltaic energy storage system includes: a photovoltaic inverter module and multiple energy storage modules, the photovoltaic input end of the photovoltaic inverter module is used to be electrically connected to the photovoltaic assembly, the DC bus port of the photovoltaic inverter module is also connected to each of the energy storage modules, and the AC port of the photovoltaic inverter module is used to be electrically connected to the power grid or load; the photovoltaic inverter module is also 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 working in an off-grid mode, obtaining the current bus voltage on the DC bus port; determining the voltage regulation amount according to the current bus voltage and the reference bus voltage; generating a voltage regulation signal to each energy storage module according to the voltage regulation amount; the voltage regulation 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 regulation amount. The invention improves the accuracy of the first reference current, which is conducive to the subsequent precise control of the charge and discharge current of the energy storage module.

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

[0005] The lack of a decentralized architecture makes it difficult to adapt to distributed energy scenarios and there is a risk of single point failure; user electricity usage data is easily leaked; efficient multi-level energy interaction cannot be achieved; and it is difficult to balance a wide voltage range and high efficiency requirements.

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

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a multi-level charge and discharge management and control system for a photovoltaic energy storage system, comprising:

[0008] Microgrid layer: used to build a full-bridge-half-bridge hybrid LLC resonant circuit; collect raw data from suppliers, demanders and photovoltaic energy storage systems 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 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 that integrates 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 score of the label vectors of the supply and demand parties based on the Jaccard-TOPSIS algorithm, and obtain all the corresponding suppliers and demand parties when the matching score is higher than the preset score threshold;

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

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

[0013] Step 1: Construct 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 score;

[0014] Step 2: Construct an action space: including the many-to-many transaction combinations and transaction parameters between the P suppliers and Q demanders selected by the matching layer. The transaction parameters include the power supply, power supply period, power supply price of each supplier and the corresponding demander. The power supply period meets the preset charge and discharge rate limit of the battery. 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 strategy space: define the supplier strategy set and the demand strategy set. The supplier strategy set includes sealed quotation strategy and dynamic price reduction strategy. The supplier generates sealed quotation based on matching score and marginal cost. The supplier's winning bid criterion is: select the supplier with the smallest sealed quotation, whose final profit is the second lowest price. The dynamic price reduction strategy is obtained based on the marginal cost and current health status of the battery.

[0016] The demand-side 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 power grid status;

[0017] The fixed price strategy is calculated based on the demander's estimated electricity consumption; the demander's triggering condition is The price corresponding to the moment is not less than the reserve price preset by the demander;

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

[0019] 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;

[0020] 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;

[0021] Step 6: Define the reward function based on transaction efficiency, revenue stability, revenue, and supplier price increase penalty;

[0022] Step 7: Use the Q-learing algorithm to update the strategy weights;

[0023] 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.

[0024] Furthermore, the method for obtaining the matching scores of the label vectors of the supply and demand sides includes:

[0025] Extract discrete labels from the supplier label vector and the demander label vector to 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 supply label vector and the demand label vector, and construct the dimension The decision matrix ,in, is the number of suppliers; is the number of consecutive labels;

[0027] Decision Matrix Perform standardization to obtain a standardized matrix ;

[0028] Based on the nature-inspired optimization algorithm, the weights of continuous labels are dynamically adjusted to obtain the weight vector ;

[0029] Normalize the matrix With the weight vector Multiply to get the standard matrix ;

[0030] Get the standard matrix The maximum value of each column in constitutes a positive ideal solution, and the standard matrix is ​​obtained The minimum value of each column in constitutes the negative ideal solution;

[0031] The Euclidean distance formula is used to calculate the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution respectively; the closeness to the positive ideal solution is calculated based on the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution; the matching score is calculated based on the Jaccard similarity and closeness.

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

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

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

[0035] Furthermore, the raw data of the PV energy storage system include output power, voltage, current, and temperature;

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

[0037] The original data of the demand side includes the required electricity, power consumption, voltage and frequency.

[0038] Furthermore, the method of obtaining SOH includes:

[0039] The impedance value of the energy storage device on the supply side is measured by the swept frequency signal generated by the Rogowski coil embedded in the secondary winding of the transformer, and the SOH is calculated.

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

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

[0042] Furthermore, the methods of integrating the DAG+PoS consensus mechanism into the blockchain network include:

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

[0044] Use PoS as the upper global consensus layer of the blockchain network; elect verification nodes through stake staking, responsible for the final transaction confirmation and chain status synchronization between the supplier and the demander;

[0045] The PoS equity weight is embedded in the DAG structure. The verification priority of each node in the blockchain network is determined by the product of the amount of currency held and the activity. The larger the product value, the higher the verification priority. The amount of currency held is obtained through transactions, and the activity is obtained by participating in transaction verification and correct verification. Nodes obtain transaction verification rights by staking a preset number of tokens, and verified transactions are propagated in parallel through DAG.

[0046] The edge weight of DAG is dynamically adjusted through the equity value of PoS.

[0047] Furthermore, when the DAG structure is forked, the branch with the highest weight is selected first, and the remaining branch transactions are rolled back to the pending confirmation pool according to the timestamp.

[0048] Furthermore, the blockchain network adopts the national secret SM4 algorithm to encrypt the original data, and the key is dynamically allocated through a quantum random number generator; the demander's electricity consumption data is processed through homomorphic encryption, and the supplier only obtains the demander's aggregated results.

[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 are as follows:

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

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

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

[0053] Figure 3 A schematic flow chart of a method for obtaining an optimized control strategy according to the present invention;

[0054] Figure 4 It is a closed-loop diagram of the optimization control strategy of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 As shown, the multi-level charging and discharging management and control system of the photovoltaic energy storage system described in this embodiment includes:

[0058] Microgrid layer: used to build a full-bridge-half-bridge hybrid LLC resonant circuit; collect raw data from suppliers, demanders and photovoltaic energy storage systems 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 power transaction fields (including capacity, price, time window and SOH, etc.)) and preset communication protocol;

[0059] Reference Figure 2, the method of 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 tube Q1, a second MOSFET tube Q2, a third MOSFET tube Q3, a fourth MOSFET tube Q4, a fifth MOSFET tube Q5, a sixth MOSFET tube Q6, a transformer T1, a resonant inductor Lr, a resonant capacitor Cr, a first diode D1, a second diode D2 and an excitation inductor Lm; the drain of the first MOSFET tube Q1 is connected to the PV+ input terminal, the source is connected to the drain of the third MOSFET tube Q3, and the gate is connected to the full-bridge drive signal PWM1; the drain of the second MOSFET tube Q2 is connected to the PV+ input terminal, the source is connected to the drain of the fourth MOSFET tube Q4, and the gate is connected to the full-bridge drive signal PWM2; the drain of the third MOSFET tube Q3 is connected to the source of the first MOSFET tube Q1, 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 tube Q4 is connected to the source of the second MOSFET tube Q2, the source is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM4; the resonant inductor Lr is connected to the PV+ input terminal, the source is connected to the PV- input terminal, and the gate is connected to the full-bridge drive signal PWM4; The first end of the transformer T1 is connected to the source of the first MOSFET tube Q1 and the drain of the third MOSFET tube 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 tube Q2 and the drain of the fourth MOSFET tube Q4; the upper end of the primary winding of the transformer T1 is connected to the source of the first MOSFET tube Q1 and the drain of the third MOSFET tube Q3, and the lower end is connected to the source of the second MOSFET tube Q2 and the drain of the fourth MOSFET tube 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 cathode of the first diode D1 and the cathode of the second diode D2 are both connected to one end of the excitation inductor Lm, and the other end of the excitation inductor Lm is the battery Bat+ port; the drain of the fifth MOSFET tube 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 tube 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 realize the comprehensive and accurate collection of supplier data and demander data by establishing three ports: the input terminal of the photovoltaic power supply, the positive and negative ends of the DC bus, and the positive and negative ends of the battery. For example, taking the supplier as an example, the input terminal of the photovoltaic power supply can input the DC power generated by the photovoltaic module into the system as the main energy source of the system, and the positive and negative ends 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 ends of the DC bus can serve as the core hub of the system energy distribution and receive the electric energy input by the photovoltaic or battery; the positive and negative ends of the battery can serve as the energy input interface of the energy storage unit; taking the photovoltaic energy storage system as an example For 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 ends of the DC bus can serve as the intermediate nodes 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 ends of the DC bus, and then to the positive and negative ends of the load / battery; when in the discharge state, the energy flow path is from the positive and negative ends of the battery to the positive and negative ends 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 switch (such as adjusting PWM1-PWM6), thereby achieving a balance between supply and demand among photovoltaic, battery, and load. Each endpoint can also provide real-time status feedback for the system to support the final optimization control. The full-bridge-half-bridge hybrid LLC resonant circuit combines the high power handling capability 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 bidirectional energy flow between photovoltaic cells, energy storage units and the power grid. The LLC resonant characteristics are used to achieve soft switching, reduce switching losses and improve system efficiency, while optimizing the voltage and current waveforms to reduce harmonic pollution and ensure the stability and reliability of the charging and discharging process. Its multi-port design can flexibly adapt to the needs of distributed energy access, and can coordinate multi-level control strategies to improve the overall energy efficiency and operating economy of the energy storage system.

[0062] The raw 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 basic data on energy sources and stability constraints for charge and discharge management, ensuring efficient operation of the system.

[0063] The supplier's original data includes power supply, SOC, SOH, charge and discharge current, and charge and discharge voltage. By collecting power supply, SOC, SOH, charge and discharge current, and voltage, the supplier's available capacity, health status, and charge and discharge capabilities are reflected, providing a basis for optimizing the energy storage regulation strategy.

[0064] Ways to obtain SOH include:

[0065] The impedance value of the energy storage device on the supply side is measured by the swept frequency signal generated by the Rogowski coil embedded in the secondary winding of the transformer T1, and the SOH is calculated, such as ;in, is the battery AC impedance value obtained by the test; is the AC impedance value when the battery is in a brand new state; It 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 charging and discharging strategy of the energy storage system, equipment maintenance planning and reliability analysis, and help optimize the efficiency and safety of the energy storage equipment.

[0066] Methods for obtaining SOC include:

[0067] The SOC is obtained by integrating the charge and discharge current in real time in combination with the rated capacity of the battery; ;in, is the rated capacity of the battery, based on SOH, combined with the initial capacity of the battery Adjust the rated capacity of the battery; ; is the charge and discharge efficiency; It is the real-time charge and discharge current, with negative for charging and positive for discharging. It can also be further combined with the extended Kalman filter algorithm to dynamically correct the SOC based on the Thevenin equivalent circuit model. By integrating the charge and discharge currents and considering the rated capacity adjusted after SOH (health state), the remaining battery power can be quantified in real time, providing a key basis for the formulation of charge and discharge strategies. With the help of the extended Kalman filter algorithm and the equivalent circuit model, the SOC calculation error can be dynamically corrected to avoid integral accumulation deviation and improve the accuracy and reliability of the SOC results.

[0068] The original data of the demander includes the required electricity, power consumption, voltage and frequency. By collecting the required electricity, power consumption, voltage and frequency, real-time feedback of load demand and grid status is provided to drive supply and demand matching and dynamic response, thus achieving precise energy distribution.

[0069] 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 that integrates the DAG+PoS consensus mechanism for storage;

[0070] Methods for blockchain networks to integrate into the DAG+PoS consensus mechanism include:

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

[0072] Use PoS as the upper global consensus layer of the blockchain network; elect verification nodes through stake staking, responsible for the final transaction confirmation and chain status synchronization between the supplier and the demander;

[0073] The equity weight of PoS is embedded in the DAG structure. The verification priority of each node in the blockchain network is determined by the amount of coins held and the activity level. The amount of coins held is obtained through transactions, and the activity level is obtained by participating in transaction verification and correct verification. Nodes obtain transaction verification rights by staking a preset number of tokens. Verified transactions are propagated in parallel through the branch structure of DAG and bound through the PoS mechanism, which can ensure the finality of data on the node and prevent double-spending attacks.

[0074] The edge weight of DAG is dynamically adjusted through the equity value of PoS; the larger the amount of currency held by a node, the higher the weight of the transaction branch it generates, and the easier it is to be referenced by subsequent nodes. This design not only retains the asynchronous concurrency advantage of DAG, but also enhances network stability through the economic incentives of PoS and avoids malicious node manipulation.

[0075] When the DAG structure is forked, the weight of each branch can be calculated by multiplying the number of transactions by the stake value of the verification node. The branch with the highest weight is selected first, and the transactions of other branches are rolled back to the pending confirmation pool according to the timestamp. 50% of the pledged tokens can also be deducted from the verification node with malicious forks (such as more than five consecutive forks).

[0076] In terms of data storage, the DAG structure, as the underlying transaction processing layer, can realize parallel propagation and efficient processing of transactions, and can quickly store the original data of suppliers, demanders and photovoltaic energy storage systems to ensure the timeliness of data records; PoS, as the upper global consensus layer, selects verification nodes through equity pledge to confirm transactions and synchronize chain status, which improves the security and reliability of data storage. In terms of transaction decisions, the verification priority of the node is determined by the amount of currency held and the degree of activity, which encourages all suppliers to actively participate in the system and maintain a good transaction reputation. At the same time, the edge weights of DAG are dynamically adjusted to better adapt to the supply and demand changes and strategy adjustments in charge and discharge management and control, so that the system can achieve more reasonable supply and demand matching and optimize the generation of control strategies based on accurate and timely data, and enhance the stability and efficiency of the entire charge and discharge management and control system.

[0077] The blockchain network uses the national secret SM4 algorithm to encrypt the original data, and the key is dynamically allocated through a quantum random number generator. For example, the NIST post-quantum cryptographic algorithm based on Lattice is used to update the session key every 5 minutes through the trusted execution environment (TEE); the demander's electricity consumption data is processed through homomorphic encryption, and the supplier only obtains the demander's aggregated results. For example, the Paillier algorithm is used to encrypt the demander's electricity consumption, and the supplier can only perform aggregate calculations on the demander's electricity consumption ciphertext. The blockchain network uses the national secret SM4 algorithm to encrypt the original data and combines it with a quantum random number generator and post-quantum cryptography technology. Its role in the management and control of charging and discharging of photovoltaic energy storage systems is reflected in the following aspects: Through the SM4 algorithm and the dynamic session key update mechanism, the confidentiality and integrity of the original data of suppliers, demanders and photovoltaic systems during transmission and storage are ensured to resist the risk of quantum attacks; homomorphic encryption technology (such as the Paillier algorithm) allows the demander's electricity consumption data to be aggregated and calculated in a confidential state, protecting user privacy while supporting suppliers to generate supply and demand matching strategies based on encrypted data; the combination of a trusted execution environment (TEE) and a quantum random number generator ensures the security and randomness of key distribution, enhances the system's anti-attack capabilities, and provides a safe and reliable data foundation for charging and discharging management and control, ensuring that the decision-making layer generates optimization strategies based on real and reliable data, and improving 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, calculate the matching score of the label vectors of the supply and demand parties based on the Jaccard-TOPSIS algorithm, and obtain all the corresponding suppliers and demand parties when the matching score is higher than the preset score threshold;

[0079] Methods for obtaining matching scores of label vectors of both supply and demand parties include:

[0080] Extract discrete labels from the supplier label vector and the demander label vector to form sets A and B respectively; obtain the number of discrete labels in sets A and B respectively, such as device type and SOH type; calculate the Jaccard similarity between sets A and B; ;in, is the supplier's reputation value, which is initially 1. By extracting discrete labels (such as equipment type, SOH type, etc.) from the label vectors of both the supply and demand sides, a set is constructed, the Jaccard similarity between the sets is calculated, and the supplier's reputation value is introduced for weighted adjustment to finally form a matching score. This process provides a data-based basis for the supply and demand matching of photovoltaic energy storage systems, helping the system to screen out transaction combinations with high label attribute fit and reliable supplier reputation, thereby improving the accuracy of resource matching and transaction efficiency, and optimizing the resource allocation logic in multi-level charging and discharging management.

[0081] Extract continuous labels from the supply label vector and the demand label vector, and construct the dimension The decision matrix ,in, is the number of suppliers; is the number of consecutive labels;

[0082] Decision Matrix Perform standardization to obtain a standardized matrix ;

[0083] Based on the nature-inspired optimization algorithm, the weights of continuous labels are dynamically adjusted to obtain the weight vector ;

[0084] Normalize the matrix With the weight vector Multiply to get the standard matrix ;

[0085] Get the maximum value of each column in the standard matrix label to form a positive ideal solution, and get the minimum value of each column in the standard matrix label to form a negative ideal solution;

[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; for example, the distance from the supplier to the positive ideal solution is ;in, For the The supplier's A standardized continuous label; For the positive ideal solution; the distance from the supplier to the negative ideal solution ;in, For the negative 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, the closeness to the positive ideal solution is calculated; such as the closeness ; Calculate the matching score based on Jaccard similarity and closeness; such as the matching score ;in, For the Supplier With Demander Jaccard similarity; For the Supplier With Demander By constructing positive and negative ideal solutions, using the Euclidean distance formula to calculate the gap between the supplier's continuous label and the ideal solution, and then using the proximity formula to quantify the supplier's approach to the positive ideal solution, the matching potential of continuous labels (such as numerical parameters such as electricity and price) is explored; the Jaccard similarity of discrete labels (reflecting attribute matching such as device type and SOH type) is combined with the proximity of continuous labels to form a matching score, which can comprehensively evaluate the fit between the supply and demand sides in attribute characteristics and numerical parameters, provide data support for the optimized matching of supply and demand and the generation of transaction pairs of photovoltaic energy storage systems, and improve the accuracy and rationality of resource allocation in multi-level charging and discharging management.

[0087] The method of calculating the matching score of the label vectors of both the supply and demand sides based on the Jaccard-TOPSIS algorithm combines the advantages of the Jaccard coefficient to measure the similarity of discrete labels and the TOPSIS method to evaluate 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, the supply and demand combination with high adaptability can be quickly screened out, so that the system can allocate resources more targetedly when formulating charging and discharging management strategies, improve energy utilization efficiency, reduce energy waste, and promote more efficient transactions between the supply and demand sides, ensuring that the entire photovoltaic energy storage system achieves supply and demand balance and optimized control during the charging and discharging process, and enhancing the stability and economy of the system operation.

[0088] Decision-making layer: It is used to optimize the matching of all suppliers and demanders obtained by the matching layer based on the competitive game strategy, obtain the optimized control strategy, generate control instructions according to the optimized control strategy, and transmit them to the microgrid layer for execution through the blockchain network.

[0089] Reference Figure 3-Figure 4 , the methods for obtaining the optimal control strategy include:

[0090] Step 1: Construct 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 in different time periods obtained based on the blockchain network.

[0091] Step 2: Construct an action space: including the many-to-many transaction combinations and transaction parameters between the P suppliers and Q demanders selected by the matching layer. The transaction parameters include the power supply, power supply period, power supply price of each supplier and the corresponding demander. The power supply period meets the preset charge and discharge rate limit of the battery. 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 strategy space: define the supplier strategy set and the demand strategy set. The supplier strategy set includes the sealed quotation strategy and the dynamic price reduction strategy. According to The matching score of each demander and marginal cost to generate sealed quotations: such as sealed quotations ;in, For the The marginal cost of a supplier is inversely proportional to the battery SOH state; is the bid elasticity coefficient; by combining the marginal cost of the supplier with the matching degree of supply and demand, a sealed bid is generated, which enables the supplier with a high matching degree to obtain a premium space in the bid, reflecting the impact of the matching degree on the bidding strategy; the supplier's winning criterion is: the supplier with the smallest sealed bid is selected, and its final profit is the second lowest price; if the second lowest price ;in, is the matching reward coefficient; It is the benchmark value for matching. By calculating the final profit of the successful supplier and adding a matching reward item based on the second lowest price (the highest bid of other suppliers), the supplier can be encouraged to improve the matching degree.

[0093] Get dynamic price reduction strategies based on the marginal cost and current health status of the battery; such as calculating the base price ;in, For the Total cost per supplier; For the The remaining power supply of each supplier; the base price of dynamic price reduction is calculated based on the total cost of the supplier, the battery health status SOH and the remaining power supply, so that the price of suppliers with poor battery health status and high cost is more flexible; the dynamically adjusted price is calculated based on the base price ;in, It is the real-time cost coefficient. The supplier's quotation is adjusted dynamically based on the basic price and the real-time cost coefficient to adapt to the real-time cost changes.

[0094] The demand-side 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 power grid status; ;in, is the load sensitivity coefficient; the price decreases exponentially according to the time; by dynamically adjusting the initial auction price, the impact of the grid load on the demander's pricing can be reflected; for The actual load of the power grid at any moment; is the rated capacity of the power grid; ;in, for The price corresponding to the moment; is the decay rate; is a mathematical constant; it makes the demand side price change over time Exponential decrease can encourage suppliers to respond as early as possible and promote transaction efficiency.

[0095] The fixed price strategy is calculated based on the demander's own estimated electricity consumption; for example, the initial fixed price ;in, Budget electricity consumption for demand side; Estimate electricity consumption for the demander; calculate the base price of the fixed price strategy based on the demander's electricity budget and estimated electricity consumption to ensure that the price matches the demand; add a certain price fluctuation space to the initial fixed price to obtain a fixed price ;in, It is the budget elasticity coefficient; adding the budget elasticity coefficient to the basic price creates room for price fluctuations to adapt to changes in the demander's budget.

[0096] The conditions for the demander to trigger the transaction are: The price corresponding to the moment is not less than the reserve price preset by the demander; ;in, For the The reserve price preset by the demander;

[0097] The initial strategy weights of each strategy set are dynamically allocated based on historical transaction data and grid load status; for example, the weight of the supplier strategy set Based on the historical highest returns and return standard deviation, the initial weight of the supply-side strategy is assigned through the softmax function to highlight the advantages of high-return and low-volatility strategies; the weight of the demand-side strategy set ;in, The highest return in history; is the standard deviation of the highest historical returns; is the grid load rate; is the standard deviation of the grid load rate; according to the grid load rate and the standard deviation of the load rate, the initial weight of the demand-side strategy is allocated so that the strategy weight is associated with the grid load state.

[0098] Step 4: Divide suppliers according to SOH, classify suppliers with SOH not lower than the preset threshold as high-responsiveness, and classify suppliers with SOH lower than the preset threshold as high-reliability, and obtain suppliers of different levels; build alliances based on suppliers of different levels ,alliance Includes the supplier alliance formed by each level separately and the supplier alliance formed by all suppliers obtained in the matching layer; calculate the alliance formed The marginal contribution of: ;in, Represents the coalition of all suppliers obtained by the matching layer Remove the supplier After that, the remaining suppliers form an alliance consisting of any subset of ; For the Alliance The number of suppliers included in The total number of suppliers among all suppliers obtained for the matching layer; For the Alliance The value when operating independently is obtained by weighting the matching score; To join the supplier After the Alliance the value of For the Alliance weights; distribute benefits according to the proportion of marginal contributions of different suppliers to the total marginal contributions; In the Alliance The marginal contribution of the alliance is calculated by comparing the value of the alliance before and after the supplier joins. , which can provide a basis for the distribution of alliance profits.

[0099] For suppliers who engage in malicious price-raising or monopolistic behavior, a secondary penalty item is introduced to impose marginal contribution penalties, such as The penalty value that a supplier needs to bear for malicious price-raising or monopolistic behavior ; is the penalty intensity coefficient; For the Sealed quotations from suppliers; Provide all suppliers with sealed quotations and expected values; Calculate the penalty value based on its quote Average bid expectations Deviation, combined with the penalty intensity , and curb bad market behavior.

[0100] Iteratively calculate the change in marginal contribution , if it exceeds the preset balance threshold, the policy weight adjustment of the policy set is triggered;

[0101] Step 5: If the supplier's SOH decay 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; such as the reputation value ;in, is the first of P suppliers The number of successful transactions that have been completed by a supplier in history; is the first of P suppliers The total number of times a supplier bids or quotes for a transaction; is the first of P suppliers The penalty ratio of each supplier; combined with the supplier's historical transaction rate and penalty ratio , updating the supplier's reputation value can motivate the supplier to maintain a good transaction record.

[0102] Step 6: Define a reward function based on transaction efficiency, revenue stability, revenue, and supplier price increase penalty; such as the reward function ;in, , , and They are all weight coefficients, which are dynamically adjusted according to the system optimization focus; For transaction efficiency, , The actual transaction volume; is the total electricity demand; For income stability, , is the standard deviation of returns; is the mean return, which is used to measure return volatility. The closer the mean return value is to 1, the stronger the return stability is; For system benefits, , To reduce the peak power consumption; is the peak electricity price; for valley electricity prices; To punish suppliers for jointly raising prices, , Quote for all suppliers The expected value, i.e. the average value; comprehensive transaction efficiency , income stability 、System Benefits Jointly raise prices with suppliers to punish , forming a multi-objective reward function, which can guide the system to optimize in the direction of efficiency, stability and fairness.

[0103] Step 7: Update the strategy weight using the Q-learing algorithm:

[0104] ;

[0105] in, is the updated Q value; for Environmental status at all times Take action Q value of action express Always select a supplier from the supplier strategy set and a demander from the demander strategy set; is the learning rate; for The reward function value at the moment; is the discount factor; For the next environment state The best action among all possible actions. For the next environment state All possible choices The maximum Q value corresponding to the value in is the maximum expected cumulative reward that can be obtained from all optional actions after transferring from the current environment state to the next environment state; based on the Q-learning algorithm, according to the current Q value ,award and the maximum Q value of the next state , updating the Q value corresponding to the strategy and iteratively optimizing the strategy weight can improve the decision-making ability of the system.

[0106] 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.

[0107] The competitive game strategy fully considers the interests of suppliers and demanders in cooperation and competition, and can achieve the optimal pairing of supply and demand in a complex market environment. The control strategy obtained through this optimized 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 non-tamperability of control command transmission, ensuring that the microgrid layer can accurately execute commands, 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] Example 2

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

[0110] The supplier's reputation value is embedded in the sealed quotation strategy. The higher the reputation, the higher the premium. ;in, is the reputation gain coefficient; For the The reputation value of each supplier. Embedding the supplier reputation value into the sealed quotation strategy can guide suppliers to actively maintain their reputation through economic incentives, prompting them to improve service quality and reduce breach of contract or malicious behavior; high-reputation suppliers receive premium returns, reflecting the market's recognition of honest entities, suppressing low-quality competition, and improving market fairness; in the long term, a virtuous cycle of "high reputation-high returns-more integrity" can be established, optimizing the trading environment of photovoltaic energy storage systems, ensuring supply and demand matching efficiency and stable market operation, and promoting the industry to develop in a more standardized and high-quality direction.

[0111] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0112] Finally: The above description is only a 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Photovoltaic energy storage system multi-level charge and discharge management and control system, characterized by: include: Microgrid layer: used to build a full-bridge-half-bridge hybrid LLC resonant circuit; The original data of suppliers, demanders and photovoltaic energy storage systems are collected through a full-bridge-half-bridge hybrid LLC resonant circuit. A blockchain communication module is installed and parameters are configured for each supplier, demander and photovoltaic energy storage system. 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 that integrates 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, calculate the matching score of the label vectors of the supply and demand parties based on the Jaccard-TOPSIS algorithm, and obtain all the corresponding suppliers and demand parties when the matching score is higher than the preset score threshold; Decision-making layer: It is used to optimize the matching of all suppliers and demanders obtained by the matching layer based on the competitive game strategy, obtain the optimized control strategy, generate control instructions according to the optimized control strategy, and transmit them to the microgrid layer for execution through the blockchain network.

2. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 1, characterized in that: Methods for obtaining an optimized control strategy include: Step 1: Construct 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 score; Step 2: Construct an action space: including the many-to-many transaction combinations and transaction parameters between the P suppliers and Q demanders selected by the matching layer. The transaction parameters include the power supply, power supply period, power supply price of each supplier and the corresponding demander. The power supply period meets the preset charge and discharge rate limit of the battery. 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 strategy space: define the supplier strategy set and the demand strategy set. The supplier strategy set includes sealed quotation strategy and dynamic price reduction strategy. The supplier generates sealed quotation based on matching score and marginal cost. The supplier's winning bid criterion is: select the supplier with the smallest sealed quotation, whose final profit is the second lowest price. The dynamic price reduction strategy is obtained based on the marginal cost and current health status of the battery. The demand-side 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 power grid status; The fixed price strategy is calculated based on the demander's estimated electricity consumption; the demander's triggering condition is The price corresponding to the moment is not less than the reserve price preset by the demander; Dynamically allocate initial strategy weights for 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 is characterized in that: Methods for obtaining matching scores of label vectors of both supply and demand parties include: Extract discrete labels from the supplier label vector and the demander label vector to 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 continuous labels from the supply label vector and the demand label vector, and construct the dimension The decision matrix ,in, is the number of suppliers; is the number of consecutive labels; Decision Matrix Perform standardization to obtain a standardized matrix ; Based on the nature-inspired optimization algorithm, the weights of continuous labels are dynamically adjusted to obtain the weight vector ; Normalize the matrix With the weight vector Multiply to get the standard matrix ; Get the standard matrix The maximum value of each column in constitutes a positive ideal solution, and the standard matrix is ​​obtained The minimum value of each column in constitutes the negative ideal solution; The Euclidean distance formula is used to calculate the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution respectively; the closeness to the positive ideal solution is calculated based on the distance from the supplier to the positive ideal solution and the distance from the supplier to the negative ideal solution; the matching score is calculated based on the Jaccard similarity and closeness.

4. 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 tube, a second MOSFET tube, a third MOSFET tube, a fourth MOSFET tube, a fifth MOSFET tube, a sixth MOSFET tube, a transformer, a resonant inductor, a resonant capacitor, a first diode, a second diode and an excitation inductor; the drain of the first MOSFET tube is connected to the PV+ input terminal, the source is connected to the drain of the third MOSFET tube, and the gate is connected to the full-bridge drive signal PWM1; the drain of the second MOSFET tube is connected to the PV+ input terminal, the source is connected to the drain of the fourth MOSFET tube, and the gate is connected to the full-bridge drive signal PWM2; the drain of the third MOSFET tube is connected to the source of the first MOSFET tube, 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 tube is connected to the source of the second MOSFET tube, 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 The source of the first MOSFET tube is connected to the drain of the third MOSFET tube, 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 tube and the drain of the fourth MOSFET tube; the upper end of the primary winding of the transformer is connected to the source of the first MOSFET tube and the drain of the third MOSFET tube, and the lower end is connected to the source of the second MOSFET tube and the drain of the fourth MOSFET tube; 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 cathode of the first diode and the cathode of the second diode are both connected to one end of the excitation inductor, and the other end of the excitation inductor is the Bat+ port of the battery; the drain of the fifth MOSFET tube is connected to the Bat+ port of the battery, 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 tube is connected to the negative end of the DC bus, the source is connected to the Bat- port of the battery, and the gate is connected to the full-bridge drive signal PWM6.

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

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

7. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 6, characterized in that: Ways to obtain SOH include: The impedance value of the energy storage device on the supply side is measured by the swept frequency signal generated by the Rogowski coil embedded in the secondary winding of the transformer, and the SOH is calculated.

8. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 1, characterized in that: Methods for obtaining SOC include: The SOC is obtained by integrating the charge and discharge current in real time in combination with the rated capacity of the battery, wherein the rated capacity of the battery is adjusted in combination with the initial capacity of the battery according to the SOH.

9. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 1, characterized in that: Methods for blockchain networks to integrate into the DAG+PoS consensus mechanism include: Use the DAG structure as the underlying transaction processing layer of the blockchain network; Use PoS as the upper global consensus layer of the blockchain network; elect verification nodes through stake staking, responsible for the final transaction confirmation and chain status synchronization between the supplier and the demander; The PoS equity weight is embedded in the DAG structure. The verification priority of each node in the blockchain network is determined by the product of the amount of currency held and the activity. The larger the product value, the higher the verification priority. The amount of currency held is obtained through transactions, and the activity is obtained by participating in transaction verification and correct verification. Nodes obtain transaction verification rights by staking a preset number of tokens, and verified transactions are propagated in parallel through DAG. The edge weight of DAG is dynamically adjusted through the equity value of PoS.

10. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 9, characterized in that: When the DAG structure is forked, the branch with the highest weight is selected first, and the transactions of other branches are rolled back to the confirmation pool according to the timestamp.

11. The photovoltaic energy storage system multi-level charge and discharge management and control system according to claim 9, characterized in that: The blockchain network uses the national secret SM4 algorithm to encrypt the original data, and the key is dynamically allocated through a quantum random number generator; the demander's electricity consumption data is processed through homomorphic encryption, and the supplier only obtains the demander's aggregated results.

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