Virtual power plant energy storage control system

Abstract

The invention provides a virtual power plant energy storage control system, and relates to the technical field of virtual power plants. The virtual power plant energy storage control system comprises a user resource access module, a collaborative scheduling decision module, a market transaction execution module, a contribution degree block chain unit and a communication topology unit. In the aspect of resource integration scheduling, a user resource access module is connected with various devices, collects integrated data, predicts a schedulable window and improves the resource utilization rate, a contribution degree block chain unit guarantees energy contribution point recording and verification, tampering and attack prevention and fairness and security maintenance, and a collaborative scheduling decision module accurately predicts user and device states. The method has the advantages that the optimization scheduling promotes participation, the communication topology unit maintains multi-protocol network connection and has the network disconnection self-healing capability, in addition, federated learning and penalty formulas are adopted, parameter mutation is avoided, malicious behaviors are inhibited, various conditions are processed through a closed-loop mechanism, and the overall performance and reliability of the system are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology, specifically to a virtual power plant energy storage control system. Background Technology

[0002] Virtual power plant energy storage is a technology that integrates and manages distributed energy storage resources (such as battery energy storage systems, electric vehicles, and distributed generation equipment) through advanced communication and information technologies. It is not a physical power plant in the traditional sense, but rather a "virtual aggregate" that aggregates energy storage units of different locations and types into a unified, dispatchable entity using software systems and control algorithms. Virtual power plant energy storage can precisely control the charging and discharging processes of each energy storage unit according to grid demand, achieving functions such as peak shaving and valley filling, improving power quality, and enhancing grid stability. It can also participate in electricity market transactions to improve the utilization efficiency of electricity resources and promote the large-scale integration and consumption of renewable energy.

[0003] Existing virtual power plant energy storage control systems suffer from several shortcomings. First, they lack transparency and security in user resource access and management, failing to effectively integrate and schedule distributed energy storage devices, electric vehicle charging stations, and temperature control equipment, resulting in low resource utilization. Second, the calculation and verification of energy contribution points in existing systems lack transparency and security, making them vulnerable to data tampering and Sybil attacks, affecting the fairness and reliability of the system. Third, existing systems lack accurate prediction of user response willingness and equipment health status during scheduling decisions, leading to unsatisfactory scheduling effects and low user participation. Furthermore, existing systems are susceptible to network outages and lack effective self-healing mechanisms, impacting stable operation. Finally, existing systems lack objectivity and completeness in parameter settings and process design, making them susceptible to subjective factors and abnormal situations, resulting in overall system performance instability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a virtual power plant energy storage control system, which solves the problems of insufficient integration and poor scheduling in existing technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a virtual power plant energy storage control system, comprising a user resource access module, a collaborative scheduling decision module, a market transaction execution module, a contribution-based blockchain unit, and a communication topology unit, wherein the user resource access module is configured as follows:

[0006] Connect distributed energy storage devices, electric vehicle charging piles, and temperature control devices via an IoT gateway;

[0007] Energy Contribution Indicator (EPIS):

[0008]

[0009] Where: t start ,t end Here, P(t) represents the start and end times of the dispatch period (in seconds), P(t) represents the discharge power at time t (in kW), ρ(t) represents the real-time electricity price coefficient (in yuan / kWh), α represents the environmental protection factor (dimensionless, ranging from 0.1 to 0.5), and f... h (t) represents the equipment health status (calculated as: f) h (t) = 0.6 × SOH + 0.4 × SoC(t), where SOH is updated every 24 hours via the battery management system.

[0010] Based on the Bluetooth sniffing protocol, the charging status of mobile devices is obtained, and the schedulable time window is predicted.

[0011] The contribution blockchain unit is configured to: receive EPIS data from the user resource access module, record and verify user contributions, and output verification results to the market transaction execution module. When the number of user hourly contribution records N... rec When the value is greater than 50, the zero-knowledge proof (ZKP) verification mechanism is activated;

[0012] The communication topology unit is configured to maintain multi-protocol network connections through a LoRaWAN gateway and 4G / 5G base stations.

[0013] Preferably, the collaborative scheduling decision module includes: a user response willingness prediction unit, a contract optimization algorithm (CTOA) unit, an electric vehicle scheduling unit, and a comfort compensation unit;

[0014] The user response willingness prediction unit generates a suitability index AI:

[0015] AI = α × R h +β×W c +γ×(1-B d )

[0016] Where: R h W represents the historical response rate (range [0,1]). c Meteorological comfort index (calculated as follows: T out (Outdoor temperature), B d Mobile device battery deviation

[0017] α, β, γ are dynamic weights (updated every 6 hours through federated learning; privacy data is homomorphically encrypted to satisfy α+β+γ=1; new scheduling instructions are paused during federated learning updates and resumed 30 seconds after the update is complete).

[0018] Preferably, the market transaction execution module:

[0019] Based on the power grid congestion index C i Dynamically divide the number of bid pools:

[0020] Where: C i Let C be the blocking index of the i-th node (range [0,1]). th This is the blocking threshold (default 0.8). This is the floor function.

[0021] Perform price adjustment using the asymmetric auction algorithm (AAA):

[0022] Valid quote = P base ×[1+λ×V c ], where λ×V c ≤0.2

[0023] Where: P base Based on the basic cost price (unit: yuan / kWh), V c λ represents the user's historical contribution value (range [0, 100]), and λ is the adjustment factor (range [0.1, 0.3]).

[0024] Preferably, the electric vehicle scheduling unit calculates the schedulable confidence level:

[0025]

[0026] Where: I(·) is an indicator function (value is 1 when the condition is true, otherwise it is 0), D is the set of high-frequency destinations for users, D nav For the current navigation destination, T dep Estimated departure time (in hours)

[0027] When a user changes their destination during scheduling, the Conf is recalculated in real time. e If 0.5 ≤ Conf e <0.6, providing a basic compensation of 0.3×S base If Conf e If the value is less than 0.5, the scheduling will be terminated.

[0028] Preferably, the comfort compensation unit calculates the compensation amount:

[0029]

[0030] like Compensation will be suspended and manual review will be initiated.

[0031] Wherein: T set,k ,T act,k For the set temperature and actual temperature (in °C) in the k-th time period, Δt kThe duration of the k-th time period (in hours) is η, which is a compensation factor (η = 0.3 for elderly users, η = 0.5 for families with infants and young children, and η = 0.1 by default).

[0032] Preferably, the contribution blockchain unit implements:

[0033] Response timeliness verification:

[0034] Where Δt tol = 8 minutes (compatible with GB / T36572-2018 and device response delay);

[0035] Equipment health certificate: When SOH < 80%, a degraded scheduling instruction is automatically generated.

[0036] Preferably, the communication topology unit enables self-healing after network outage:

[0037]

[0038] Among them: ot sustain oSOC is the sustainable operating time (in minutes). init Initial battery level (in %) during network outage, C bat =1500 / N cycle Battery capacity factor (N) cycle (This refers to the cumulative number of battery cycles).

[0039] A virtual power plant energy storage control method specifically includes the following steps:

[0040] S1: Decompose grid demand D grid Sub-demand vector

[0041] S2: Generate user incentive sets using the CTOA algorithm

[0042] S3: Resource scheduling must meet the following requirements: Conf e >0.8∧AI>0.6 (∧ represents logical AND);

[0043] If user resources do not meet the criteria, users will be reselected in descending order using AI. cmd Complete the reselection within 5 minutes before issuing the order until D is covered. grid 95% of the total demand, and the continuous scheduling interval for the same user is ≥2 hours;

[0044] S4: Verify the actual discharge quantity E actual ≥0.9D i Then, the scheduling results will be... <t cmd ,t act E actual>Write to the blockchain: Hash new =SHA256(Hash) prev ||Sig data ).

[0045] This invention provides a virtual power plant energy storage control system. It has the following beneficial effects:

[0046] This invention provides a virtual power plant energy storage control system. Through a user resource access module, the system achieves efficient integration and scheduling of distributed energy storage devices, electric vehicle charging piles, and temperature control equipment, significantly improving resource utilization. The contribution blockchain unit ensures transparent recording and verification of energy contribution points, effectively preventing data tampering and Sybil attacks, enhancing system fairness and security. The collaborative scheduling decision module accurately predicts user response intentions and equipment health status, optimizing scheduling effects and increasing user participation. The market transaction execution module dynamically divides the bidding pool based on grid demand and executes an asymmetric auction algorithm, ensuring fair and efficient transactions. The communication topology unit ensures stable system operation through multi-protocol network connections and a self-healing mechanism for network outages. Furthermore, the system avoids parameter mutations and malicious behavior through a federated learning update mechanism and a compensation calculation gradient penalty formula, ensuring system security and fairness. This technology, through closed-loop scheduling, verification, and compensation mechanisms, handles timeouts, conflicts, and abnormal situations, significantly improving the overall performance and reliability of the system. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, this embodiment of the invention provides a virtual power plant energy storage control system, including a user resource access module, a collaborative scheduling decision module, a market transaction execution module, a contribution blockchain unit, and a communication topology unit. The user resource access module is configured as follows:

[0050] Connect distributed energy storage devices, electric vehicle charging piles, and temperature control devices via an IoT gateway;

[0051] Energy Contribution Indicator (EPIS):

[0052]

[0053] Where: t start ,t end Here, P(t) represents the start and end times of the dispatch period (in seconds), P(t) represents the discharge power at time t (in kW), ρ(t) represents the real-time electricity price coefficient (in yuan / kWh), α represents the environmental protection factor (dimensionless, ranging from 0.1 to 0.5), and f... h (t) represents the equipment health status (calculated as: f) h (t) = 0.6 × SOH + 0.4 × SoC(t), where SOH is updated every 24 hours via the battery management system.

[0054] Based on the Bluetooth sniffing protocol, the charging status of mobile devices is obtained, and the schedulable time window is predicted.

[0055] The contribution blockchain unit is configured as follows: it receives EPIS data from the user resource access module, records and verifies user contributions, and outputs the verification results to the market transaction execution module. When the number of user hourly contribution records N... rec When the value is greater than 50, the zero-knowledge proof (ZKP) verification mechanism is activated;

[0056] The communication topology unit is configured to maintain multi-protocol network connectivity via a LoRaWAN gateway and 4G / 5G base stations.

[0057] The collaborative scheduling decision-making module includes: a user response willingness prediction unit, a contract optimization algorithm (CTOA) unit, an electric vehicle scheduling unit, and a comfort compensation unit;

[0058] The user response willingness prediction unit generates an AI-generated fit index:

[0059] AI = α × R h +β×W c +γ×(1-B d )

[0060] Where: R h W represents the historical response rate (range [0,1]). c Meteorological comfort index (calculated as follows: T out (Outdoor temperature), B d Mobile device battery deviation

[0061] α, β, γ are dynamic weights (updated every 6 hours through federated learning; privacy data is homomorphically encrypted to satisfy α+β+γ=1; new scheduling instructions are paused during federated learning updates and resumed 30 seconds after the update is complete).

[0062] Market transaction execution module:

[0063] Based on the power grid congestion index C i Dynamically divide the number of bid pools:

[0064] Where: C i Let C be the blocking index of the i-th node (range [0,1]). th This is the blocking threshold (default 0.8). This is the floor function.

[0065] Perform price adjustment using the asymmetric auction algorithm (AAA):

[0066] Valid quote = P base ×[1+λ×V c ], where λ×V c ≤0.2

[0067] Where: P base Based on the basic cost price (unit: yuan / kWh), V c λ represents the user's historical contribution value (range [0, 100]), and λ is the adjustment factor (range [0.1, 0.3]).

[0068] The electric vehicle dispatching unit calculates the dispatchability confidence level:

[0069]

[0070] Where: I(·) is an indicator function (value is 1 when the condition is true, otherwise it is 0), D is the set of high-frequency destinations for users, D nav For the current navigation destination, T dep Estimated departure time (in hours)

[0071] When a user changes their destination during scheduling, the Conf is recalculated in real time. e If 0.5 ≤ Conf e <0.6, providing a basic compensation of 0.3×S base If Conf e If the value is less than 0.5, the scheduling will be terminated.

[0072] The comfort compensation unit calculates the compensation amount:

[0073]

[0074] like Compensation will be suspended and manual review will be initiated.

[0075] Wherein: T set,k ,T act,k For the set temperature and actual temperature (in °C) in the k-th time period, Δt kThe duration of the k-th time period (in hours) is η, which is a compensation factor (η = 0.3 for elderly users, η = 0.5 for families with infants and young children, and η = 0.1 by default).

[0076] Contribution-based blockchain unit implementation:

[0077] Response timeliness verification:

[0078] Where Δt tol = 8 minutes (compatible with GB / T36572-2018 and device response delay);

[0079] Equipment health certificate: When SOH < 80%, a degraded scheduling instruction is automatically generated.

[0080] Communication topology unit enables network outage self-healing:

[0081]

[0082] Among them: ot sustain oSOC is the sustainable operating time (in minutes). init Initial battery level (in %) during network outage, C bat =1500 / N cycle Battery capacity factor (N) cycle (This refers to the cumulative number of battery cycles).

[0083] A virtual power plant energy storage control method specifically includes the following steps:

[0084] S1: Decompose grid demand D grid Sub-demand vector

[0085] S2: Generate user incentive sets using the CTOA algorithm

[0086] S3: Resource scheduling must meet the following requirements: Conf e >0.8∧AI>0.6 (∧ represents logical AND);

[0087] If user resources do not meet the criteria, users will be reselected in descending order using AI. cmd Complete the reselection within 5 minutes before issuing the order until D is covered. grid 95% of the total demand, and the continuous scheduling interval for the same user is ≥2 hours;

[0088] S4: Verify the actual discharge quantity E actual ≥0.9D i Then, the scheduling results will be... <t cmd ,t act E actual >Write to the blockchain: Hashnew =SHA256(Hash) prev ||Sig data ).

[0089] First, this invention achieves efficient integration and scheduling of distributed energy storage devices, electric vehicle charging piles, and temperature control equipment through a user resource access module, significantly improving resource utilization. Second, this technology achieves transparent recording and verification of energy contribution points through a contribution blockchain unit, effectively preventing data tampering and Sybil attacks, and enhancing the fairness and security of the system. Third, this technology achieves accurate prediction of user response intentions and equipment health status through a collaborative scheduling decision module, optimizing scheduling effects and increasing user participation. Furthermore, this invention achieves stable maintenance of multi-protocol network connections and self-healing from network outages through a communication topology unit, ensuring stable system operation. Finally, this technology emphasizes objectivity and completeness in parameter settings and process design, avoiding the influence of subjective factors by associating physical indicators and rule constraints, and effectively handling timeouts, conflicts, and abnormal situations through closed-loop scheduling, verification, and compensation mechanisms, improving the overall performance and reliability of the system.

[0090] The implementation of this invention first connects distributed energy storage devices, electric vehicle charging piles, and temperature control equipment through a user resource access module. An IoT gateway is used to achieve real-time data collection and integration from these devices. Simultaneously, based on a Bluetooth sniffing protocol, the charging status of mobile devices is acquired to predict the available scheduling time window and generate energy contribution points. These points are calculated using parameters such as discharge power, real-time electricity price coefficient, environmental factors, and device health, ensuring objectivity and fairness. Next, the contribution blockchain unit receives the energy contribution points data from the user resource access module, records and verifies user contributions. When a user's hourly contribution records exceed a set threshold, a zero-knowledge proof verification mechanism is automatically activated to prevent Sybil attacks and data forgery, ensuring data authenticity and security. The collaborative scheduling decision module optimizes scheduling decisions based on the adaptability index generated by the user response willingness prediction unit, combined with historical response rates, weather comfort indices, and mobile device battery deviation. A user incentive set is generated through a contract optimization algorithm to ensure the rationality of scheduling and the enthusiasm of user participation. In the market transaction execution module, the system executes resource scheduling according to the instructions of the collaborative scheduling decision module, ensuring that the actual discharge meets the grid demand. The scheduling results are written to the blockchain, and a hash algorithm is used for integrity verification to ensure the immutability and traceability of the data. The communication topology unit maintains multi-protocol network connectivity through LoRaWAN gateways and 4G / 5G base stations, and calculates sustainable operating time through a self-healing mechanism in the event of a network outage, ensuring system stability and continuity. Throughout the implementation process, the system also suspends scheduling instructions through a federated learning update mechanism to avoid service interruptions caused by parameter mutations, and suppresses malicious temperature control fraud through a compensation calculation gradient penalty formula, ensuring system security and fairness. Finally, the system handles timeouts, conflicts, and abnormal situations through a closed-loop scheduling, verification, and compensation mechanism, ensuring the overall stability and reliability of performance.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A virtual power plant energy storage control system, comprising a user resource access module, a collaborative scheduling decision module, a market transaction execution module, a contribution-based blockchain unit, and a communication topology unit, characterized in that: The user resource access module is configured as follows: Connect distributed energy storage devices, electric vehicle charging piles, and temperature control devices via an IoT gateway; Energy Contribution Indicator (EPIS): Based on the Bluetooth sniffing protocol, the charging status of mobile devices is obtained, and the schedulable time window is predicted. The contribution blockchain unit is configured to: receive EPIS data from the user resource access module, record and verify user contributions, and output verification results to the market transaction execution module. When the number of user hourly contribution records N... rec When the value is greater than 50, the zero-knowledge proof (ZKP) verification mechanism is activated; The communication topology unit is configured to maintain multi-protocol network connections through a LoRaWAN gateway and 4G / 5G base stations.

2. The virtual power plant energy storage control system according to claim 1, characterized in that: The collaborative scheduling decision module includes: a user response willingness prediction unit, a contract optimization algorithm (CTOA) unit, an electric vehicle scheduling unit, and a comfort compensation unit; The user response willingness prediction unit generates a suitability index AI: AI = α×R h +β×W c +γ×(1-B d ) 3. The virtual power plant energy storage control system according to claim 1, characterized in that: The market transaction execution module: Based on the power grid congestion index C i Dynamically divide the number of bid pools: Perform price adjustment using the asymmetric auction algorithm (AAA): Valid quote = P base ×[1+λ×V c ], where λ×V c ≤0.

2.

4. The virtual power plant energy storage control system according to claim 2, characterized in that: The electric vehicle scheduling unit calculates the schedulability confidence level: When a user changes their destination during scheduling, the Conf is recalculated in real time. e If 0.5 ≤ Conf e <0.6, provide basic compensation of 0.3×S base If Conf e If the value is less than 0.5, the scheduling will be terminated.

5. The virtual power plant energy storage control system according to claim 2, characterized in that: The comfort compensation unit calculates the compensation amount: like Compensation will be suspended and manual review will be initiated.

6. The virtual power plant energy storage control system according to claim 1, characterized in that: The contribution blockchain unit implements: Response timeliness verification:

7. The virtual power plant energy storage control system according to claim 1, characterized in that: The communication topology unit enables self-healing after network outage:

8. A virtual power plant energy storage control method, applied to any one of the systems of claims 1-7, characterized in that, Specifically including The following steps: S1: Decompose grid demand D grid Sub-demand vector S2: Generate user incentive sets using the CTOA algorithm S3: Resource scheduling must meet the following requirements: Conf e >0.8∧AI>0.6; S4: Verify the actual discharge quantity E actual ≥0.9D i Then, the scheduling results will be... <t cmd ,t act E actual >Write to the blockchain: Hash new =SHA256(Hash) prev ||Sig data ).

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