User side resource flexible adjustment management and control system

Abstract

The invention discloses a user side resource flexible adjustment management and control system. Comprising a system layer, an application layer and a data layer, the data layer is used for gathering data to the system layer, the system layer is used for managing the data, and the application layer is used for providing support service for demand response and marketization transaction business of a virtual power plant. The load control architecture separates data storage, application logic and interface display functions through a database server, an application server and a Web end, the distributed B / S architecture undertakes the interface display function through the Web end, the application server is responsible for business logic processing, and the database server is responsible for data storage and management; according to the invention, reliable authentication of the identity of an operator is realized through a digital authentication technology, a full-life-cycle management mechanism of a digital capability certificate is established, a digital capability certificate query service is provided for a market operation mechanism, basic information and adjustable capability information of the operator are obtained, and adjustable resources are supported to participate in various marketization transactions.

Description

Technical Field

[0001] The present invention belongs to the technical field of power regulation and management, and more specifically, particularly relates to a user-side resource flexible regulation and control system. Background Art

[0002] With the transformation of energy structure and the deepening of intelligent application, the demand for energy on the user side (i.e., consumer side) is not only passive consumption, but also transformed into active demand response, flexible adjustment and resource optimization. In this context, the user side resource flexible adjustment and control system came into being, aiming to better adjust, manage and optimize the use of these resources.

[0003] Changes in energy consumption patterns: Traditional energy consumption patterns mainly rely on centralized power generation and one-way power supply. With the development of distributed energy systems (such as solar energy, wind energy, small-scale combined heat and power, etc.), the user side has gradually become diversified, and the energy supply and demand pattern is no longer a simple one-way flow, but a two-way and dynamic one. This makes energy regulation, distribution and management more complicated.

[0004] Volatility and intermittency of renewable energy: The production of renewable energy such as wind and solar energy is volatile and intermittent, requiring flexible regulation and control systems to help balance system loads and ensure the stability and security of the power grid.

[0005] The rise of smart grid and IoT technologies: The promotion of smart grid technologies and the application of technologies such as IoT, big data, and artificial intelligence (AI) have greatly improved the accuracy and real-time performance of user-side resource regulation. Through real-time data collection, analysis, and feedback, users can flexibly adjust their energy usage and even participate in grid load regulation.

[0006] However, there are some problems with the existing technology: the user resource management in the power demand side management business is not unified, the intelligent management level is not high, and there are barriers to data interaction. By aggregating distributed power sources, customer-side energy storage, adjustable loads and other flexible adjustment resources, we provide the necessary basic capacity construction for user resources to quickly and compliantly access the new power load management system, reduce the construction cost of the load-side resource management-related system, formulate unified data interaction specifications with dispatching, trading, and operators, and lower the threshold for user resources to participate in market-oriented businesses from the operational level, ensuring unified management, unified regulation, and unified service of demand-side load resources. Therefore, we propose a user-side resource flexible adjustment and control system. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide a user-side resource flexible adjustment and control system, which provides resource access authentication and adjustment capability authentication services to operators, realizes reliable authentication of operator identity through digital authentication technology, issues digital capability certificates to operators online, and establishes a full life cycle management mechanism for digital capability certificates. On the one hand, it is necessary to apply for the issuance of digital certificates to operators who have passed the access review. After the operator submits the application information, the platform will review and issue the certificate online. On the other hand, it provides digital capability certificate query services for market operating institutions, obtains basic operator information and adjustable capability information, and supports adjustable resources to participate in various market transactions.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: A user-side resource flexible regulation and control system, comprising a system layer, an application layer and a data layer, wherein the data layer is used to aggregate data to the system layer, the system layer is used to manage data, and the application layer is used to provide support services for demand response and market-based trading business of virtual power plants;

[0009] The system layer is used to manage, analyze and process data to achieve the core business support of demand response and virtual power plants. The system layer adopts a load control architecture and a distributed B / S architecture to ensure the efficiency of data processing and the security of the system; the system layer mainly includes an access management module, an aggregate resource management module, a capability verification module, an operation monitoring module, a demand response transaction support module and a digital certificate module;

[0010] The access management module is used to manage the data sources of all access systems, ensuring that the access, modification and deletion of data are effectively monitored and managed; and to control the scope and authority of the access system through identity authentication and authority management;

[0011] The aggregate resource management module is responsible for aggregating the user's resources to form an overall resource pool for subsequent unified scheduling and management;

[0012] The capacity verification module is used to verify the capacity of resources participating in the demand response or virtual power plant market to ensure that the resource's adjustment capacity, process steps and response speed meet market demand;

[0013] The operation monitoring module is used to monitor the user's electricity usage in real time, collect electricity usage data, and promptly feed it back to the dispatching system to provide data support for demand response operations;

[0014] The demand response transaction support module provides technical support for the calculation, analysis and processing of demand response events, mainly including load forecasting and scheduling support for participants in the demand response market;

[0015] The digital certificate module is used to ensure the electronic certificate authentication in all power transactions and data exchange processes, ensure the security and legitimacy of the system, authenticate the identities of both parties to the transaction through digital certificates, and prevent fraud;

[0016] The data layer is used to collect data from the electricity collection system and the marketing system to achieve real-time synchronization of user profile information; the data layer is also used to collect data from the dispatching system and the trading system to achieve the construction of unified data interaction capabilities for market transactions and provide standardized access services for operator platforms;

[0017] The application layer is for operators to conduct market subject capability verification and platform testing after unified access to the system layer, and provide support services for market-based trading businesses such as demand response, virtual power plants, air conditioning loads and energy census. The application layer is accessed through the Web terminal;

[0018] The load-control architecture separates data storage, application logic and interface display functions through a database server, an application server and a Web terminal. The distributed B / S architecture assumes the interface display function through the Web terminal, the application server is responsible for business logic processing, and the database server is responsible for data storage and management.

[0019] Specifically, the access management module includes operator management, access application, access review, change management and exit management; the operator management is used to manage the access rights, contracts, and service levels of different operators or partners, to authenticate the accessed operators, and to provide services according to the agreement; the access application is used to allow access requests from external systems or devices; the access review is used to approve and verify the access application; the change management is used to process change requests from operators or devices that have already accessed the system; the exit management is used to process exit requests from operators or devices;

[0020] The capability verification includes test appointment application, test appointment review, test result management, test task management and test progress management; the test appointment application is used to obtain test requirements from external users, and is responsible for collecting and organizing all test requirements; the test appointment review is used to review the test appointment application submitted by the user, and is used to detect the rationality, feasibility and resource matching of the test appointment application; the test result management is responsible for recording, storing and managing all data and results in the test process; the test task management is used to coordinate and monitor all test activities, and is used to realize the allocation, execution and monitoring of tasks; the test progress management is used to track the real-time progress of each test task, and the test activities are carried out according to the predetermined plan;

[0021] The aggregate resource management module includes operator profile management, virtual power plant profile management, user profile management and branch profile management; the operator profile management is used to connect with suppliers and energy companies to ensure contract fulfillment and service quality; the virtual power plant profile management is used to manage the operation and optimization of virtual power plants and provide intelligent scheduling; the user profile management is used to analyze customer demand response and electricity consumption behavior; the branch profile management is used for stable operation and efficient scheduling management of various branches of the power system;

[0022] The operation monitoring module includes user monitoring, branch monitoring and equipment monitoring. The user monitoring is used to monitor the user's electricity usage behavior, demand response and energy-saving optimization; the branch monitoring is used to monitor each branch of the power grid or virtual power plant in real time to achieve the stability of power distribution and timely discover branch problems; the equipment monitoring is used to perform health monitoring and fault warning on key equipment in the system.

[0023] Specifically, the digital certificate module includes digital certificate application, digital certificate management and authentication rule configuration; the digital certificate application is used to implement the process of applying for a certificate, which includes the generation of a certificate signing request, user identity authentication and the issuance of a certificate; the digital certificate management is used to manage the life cycle of the application certificate, including certificate renewal, revocation, validity check, backup and recovery; the authentication rule configuration is used by the administrator to set appropriate certificate issuance, verification and use policies;

[0024] The demand response transaction support module includes baseline algorithm configuration, user channel statistics, real-time load monitoring and data quality monitoring; the baseline algorithm configuration is used to calculate and determine the user's load baseline; the user channel statistics are used to analyze the user's participation in different channels; the real-time load monitoring is used to monitor user load changes in real time, and compare them with baseline data to evaluate the response effect; the data quality monitoring is used to ensure the integrity, accuracy, consistency and timeliness of the collected data.

[0025] Specifically, the generation of the certificate signing request first determines the public and private keys, and the calculation formulas of the public and private keys are as follows:

[0026] Generate RSA key pair:

[0027] Choose two large prime numbers p and q,

[0028] Calculate the modulus n = p × q,

[0029] Calculate Euler function

[0030] Select the public key exponent e,

[0031] Calculate the private key exponent d so that

[0032] The public key is (e,n) and the private key is (d,n);

[0033] Calculate the hash value:

[0034] H(M)=SHA-256(M)

[0035] Where M is the content of the CSR data, H(M) is the hash value of the CSR data, and SHA is the hash algorithm;

[0036] Signature generation:

[0037] Sign H(M) using private key (d,n):

[0038] S=H(M) d (modn),

[0039] Among them: S is the signature value, d and n are private keys, and the final generated signature S will be included in the CSR data.

[0040] Specifically, the user identity verification is calculated as follows:

[0041] Certificate signature verification: Use the public key (e,n) of the certificate authority to verify the certificate signature.

[0042] Verify(Signature,CA'sPublicKey)=True / False,

[0043] If the verification is successful, the certificate is valid;

[0044] Calculate hash value and compare: Use a hash algorithm to calculate the hash value of the public key information in the certificate and match it with the public key provided by the server or client.

[0045] H(PublicKey)=hash(PublicKey)

[0046] If the hash values ​​are consistent, it proves that the public key has not been tampered with.

[0047] Specifically, the calculation formula of the baseline algorithm configuration is as follows:

[0048] The load baseline of the data is calculated by weighted moving average;

[0049]

[0050] Of which: WMA t is the weighted moving average at time t, i.e., the load baseline; L i is the actual load data at time point i; wi is the weight of time point i, each data point L i is assigned a weight, with closer time points being assigned higher weights, and the weight w i is an arbitrary positive number; N is the time window size used to calculate the weighted moving average;

[0051] Exponential smoothing smoothes the load curve by weighting the historical data;

[0052] S t =αL t +(1-α)S t-1 ,

[0053] Where: S t is the smoothed load value at time t, i.e., the load baseline; L t is the actual load at time t; S t-1 is the smoothing value at time t-1; α is the smoothing factor, and its value range is 0<α<1.

[0054] Specifically, the active power in the real-time load monitoring is as follows:

[0055]

[0056] Among them, P represents active power, U represents voltage, and I represents current. Expressed as power factor, it represents the phase difference between voltage and current;

[0057] The reactive power calculated by real-time load is as follows:

[0058]

[0059] Among them, Q represents reactive power, U represents voltage, and I represents current. Expressed as the phase difference between current and voltage.

[0060] Specifically, the real-time load monitoring uses a long short-term memory network for prediction, and the calculation formula of the long short-term memory network is as follows:

[0061] Forget Gate:

[0062]

[0063] Among them, f t Represented as the output of the forget gate, the value is between 0 and 1, 1 means that the information of the previous moment is completely retained, and 0 means that it is completely forgotten; W f Represented as the weight matrix of the forget gate; b f Represented as the bias term of the forget gate; Represented as Sigmoid activation function; ht-1 Represented as the hidden state of the previous moment, that is, the output of the previous moment; x t It is expressed as the input at the current moment and as the historical real-time load data;

[0064] Input Gate:

[0065]

[0066] Among them, i t Represented as the output of the input gate, the value is between 0 and 1, indicating the importance of the input at the current moment; W i Represented as the weight matrix of the input gate; b i Represented as the bias term of the input gate; Represented as candidate cell state; W C Represented as a weight matrix, responsible for connecting the hidden state h of the previous moment t-1 and the current input x t Perform linear transformation; b C It is represented as a bias term, which is used to adjust the calculation process of candidate cell states; tanh is represented as a hyperbolic tangent activation function, which maps the linear combination of candidate cell states to the range of [-1,1];

[0067] Update cell status:

[0068]

[0069] Among them, C t Represents the cell state at the current moment; C t-1 Represents the cell state at the last moment; f t Represented as the output of the forget gate; i t The output of the input gate; It is represented as the candidate cell state, which is the calculated active power and reactive power;

[0070] Output Gate:

[0071]

[0072] The hidden state is calculated as:

[0073] h t =o t tanh(C t ),

[0074] Among them, t Represents the output of the output gate; h t It is represented as the hidden state at the current moment, that is, the output of LSTM, which will be used as the input at the next moment.

[0075] Specifically, the data quality monitoring first cleans the data to obtain abnormal values ​​and missing values ​​of the data, and the calculation formula is as follows:

[0076] Handling outliers For each time point, calculate the standardized deviation of the data from the mean and use the Z-Score formula:

[0077]

[0078] Among them, X t is the data at the current time point, μ is the mean value of the data, σ is the standard deviation of the data, and Z represents the standardized deviation of the data from the mean value;

[0079] And when Z exceeds the set threshold, it is marked as an outlier, that is, Z ≥ |3|, and the outlier is deleted;

[0080] Perform statistics on outliers, missing values, and data values ​​in the data:

[0081]

[0082] Among them, X represents the total amount of statistical outliers, missing values, and data values, which are X y , X q and X s , x i Outliers, missing values, and data values ​​represented as statistics;

[0083] The data quality monitoring is calculated by accuracy and completeness:

[0084]

[0085] Specifically, the dispatching system predicts the power dispatching through a prediction model, and the algorithm adopted by the prediction model is ridge regression, which is used to predict continuous numerical outputs and predict a continuous target variable according to input data;

[0086]

[0087] Among them, Loss represents the loss function in ridge regression, y i It represents the actual observation of the i-th sample.

[0088] Measured value, It represents the predicted value of the i-th sample. It represents the square error of the i-th sample. It represents the sum of squared errors of samples, and λ represents the regularization parameter, which controls the influence of the regularization term on the model. represents the square of the L2 norm of the regression coefficient, m is the number of input variables in the data set, It represents the L2 regularization term, which penalizes the size of the regression coefficient to avoid overfitting, and β represents the regression coefficient vector.

[0089] Technical effects and advantages of the present invention:

[0090] The present invention provides online capability verification services, configures verification indicators according to transaction types, and realizes accurate verification of access resources; constructs a full life cycle management mechanism for digital capability certificates to realize reliable authentication of the identity of demand-side management service agencies; adopts a read-write separation system architecture design, and the complex data calculation logic is independent of the system, which will not affect system performance when large-scale calculations are involved, reduces the difficulty of business module development, increases the speed of function development, and improves user experience; based on the time-series database and micro-application design concept, it improves project calculation efficiency and data calculation accuracy;

[0091] The capacity verification module ensures that the resource adjustment capacity of the demand response or virtual power plant market meets the market demand, avoiding the risk of resource overload or shortage. Through accurate capacity verification, the efficiency of demand response and the accuracy of market regulation can be improved. The operation monitoring module combines real-time load monitoring with baseline algorithms to ensure that the user's electricity usage can be grasped in real time, providing real-time data support for demand response operations. Through accurate prediction of load changes and monitoring of real-time loads, the timeliness and accuracy of demand response can be improved.

[0092] The access management module manages, reviews and controls the rights of different operators and external systems to ensure the access security and service quality of the system. It helps to efficiently manage and control the access rights of users and devices; the aggregate resource management module can aggregate scattered resources to form a unified resource pool, which is convenient for subsequent resource scheduling and optimization. The formation of a resource pool helps to improve the overall resource utilization efficiency and reduce resource waste;

[0093] The application layer provides unified access through the Web to facilitate operations and management by operators and users; this layer supports the development of market-based trading businesses such as demand response and virtual power plants, and helps promote the openness and competition of the electricity market; the demand response trading support module supports load scheduling and response to market entities through baseline algorithms and load forecasts. These forecasting and scheduling services provide a more accurate basis for decision-making in the electricity market and promote its efficient operation.

[0094] Using LSTM for load forecasting can efficiently predict future loads based on historical data and real-time inputs. The advantages of the LSTM model in processing time series data can effectively capture the laws of load changes and enhance the intelligence level of demand response decisions. The dispatching system uses the ridge regression algorithm to predict power dispatch and avoids overfitting problems by controlling the regularization term, thereby making the prediction model more generalized and improving the accuracy of power dispatch predictions.

[0095] It fully considers the needs of electricity market transactions and provides technical support for demand response and market transactions of virtual power plants through multiple functional modules such as capacity verification, load forecasting, and resource scheduling. Users can participate in market transactions more effectively and improve the efficiency of market-based allocation of electricity resources.

[0096] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a schematic diagram of the system structure provided by the present invention;

[0098] Figure 2 It is a schematic diagram of the functional architecture provided by the present invention. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0100] like Figure 1 to Figure 2 As shown, a user-side resource flexible regulation and control system provided by an embodiment of the present invention includes a system layer, an application layer and a data layer, wherein the data layer is used to aggregate data to the system layer, the system layer is used to manage data, and the application layer is used to provide support services for demand response and market-based trading business of virtual power plants;

[0101] The system layer is used to manage, analyze and process data to achieve the core business support of demand response and virtual power plants. The system layer adopts a load control architecture and a distributed B / S architecture to ensure the efficiency of data processing and the security of the system; the system layer mainly includes an access management module, an aggregate resource management module, a capability verification module, an operation monitoring module, a demand response transaction support module and a digital certificate module;

[0102] The access management module is used to manage the data sources of all access systems, ensuring that the access, modification and deletion of data are effectively monitored and managed; and to control the scope and authority of the access system through identity authentication and authority management;

[0103] The aggregate resource management module is responsible for aggregating the user's resources to form an overall resource pool for subsequent unified scheduling and management;

[0104] The capacity verification module is used to verify the capacity of resources participating in the demand response or virtual power plant market to ensure that the resource's adjustment capacity, process steps and response speed meet market demand;

[0105] The operation monitoring module is used to monitor the user's electricity usage in real time, collect electricity usage data, and promptly feed it back to the dispatching system to provide data support for demand response operations;

[0106] The demand response transaction support module provides technical support for the calculation, analysis and processing of demand response events, mainly including load forecasting and scheduling support for participants in the demand response market;

[0107] The digital certificate module is used to ensure the electronic certificate authentication in all power transactions and data exchange processes, ensure the security and legitimacy of the system, authenticate the identities of both parties to the transaction through digital certificates, and prevent fraud;

[0108] The data layer is used to collect data from the electricity collection system and the marketing system to achieve real-time synchronization of user profile information; the data layer is also used to collect data from the dispatching system and the trading system to achieve the construction of unified data interaction capabilities for market transactions and provide standardized access services for operator platforms;

[0109] The application layer is for operators to conduct market subject capability verification and platform testing after unified access to the system layer, and provide support services for market-based trading businesses such as demand response, virtual power plants, air conditioning loads and energy census. The application layer is accessed through the Web terminal;

[0110] The load-control architecture separates data storage, application logic and interface display functions through a database server, an application server and a Web terminal. The distributed B / S architecture assumes the interface display function through the Web terminal, the application server is responsible for business logic processing, and the database server is responsible for data storage and management.

[0111] In this embodiment, preferably, the access management module includes operator management, access application, access review, change management and exit management; the operator management is used to manage the access rights, contracts, and service levels of different operators or partners, to authenticate the accessed operators, and to provide services according to the agreement; the access application is used to allow access requests from external systems or devices; the access review is used to approve and verify the access application; the change management is used to process change requests from operators or devices that have already accessed the system; the exit management is used to process exit requests from operators or devices;

[0112] The capability verification includes test appointment application, test appointment review, test result management, test task management and test progress management; the test appointment application is used to obtain test requirements from external users, and is responsible for collecting and organizing all test requirements; the test appointment review is used to review the test appointment application submitted by the user, and is used to detect the rationality, feasibility and resource matching of the test appointment application; the test result management is responsible for recording, storing and managing all data and results in the test process; the test task management is used to coordinate and monitor all test activities, and is used to realize the allocation, execution and monitoring of tasks; the test progress management is used to track the real-time progress of each test task, and the test activities are carried out according to the predetermined plan;

[0113] The aggregate resource management module includes operator profile management, virtual power plant profile management, user profile management and branch profile management; the operator profile management is used to connect with suppliers and energy companies to ensure contract fulfillment and service quality; the virtual power plant profile management is used to manage the operation and optimization of virtual power plants and provide intelligent scheduling; the user profile management is used to analyze customer demand response and electricity consumption behavior; the branch profile management is used for stable operation and efficient scheduling management of various branches of the power system;

[0114] The operation monitoring module includes user monitoring, branch monitoring and equipment monitoring. The user monitoring is used to monitor the user's electricity consumption behavior, demand response and energy-saving optimization; the branch monitoring is used to monitor each branch of the power grid or virtual power plant in real time to achieve the stability of power distribution and timely discover branch problems; the equipment monitoring is used to perform health monitoring and fault warning on key equipment in the system;

[0115] It should be noted that operator management is used to manage the access rights, contracts, service levels of operators or partners, and to perform authentication; access application is used for external systems or devices to submit access requests to the system; access review is used to approve and verify access applications; change management is used to process change requests from operators or devices that have already accessed the system; and exit management is used to process exit requests from operators or devices.

[0116] Test appointment application is used for external users to propose test requirements and collect and organize them; test appointment review is used to review the test applications submitted by users to ensure their rationality, feasibility and resource matching; test result management is used to record, store and manage all data and results in the test process; test task management is used to coordinate and monitor the task allocation and execution of test activities; test progress management is used to track the real-time progress of test tasks to ensure that activities proceed as planned;

[0117] Operator profile management is used to manage the contract performance and service quality of suppliers and energy companies; virtual power plant profile management is used to manage the operation and optimization of virtual power plants and provide intelligent scheduling; user profile management is used to analyze customer demand response and electricity consumption behavior; branch profile management is used to ensure the stable operation and efficient scheduling management of power system branches;

[0118] User monitoring is used to monitor users' electricity usage behavior, demand response and energy-saving optimization; branch monitoring is used to monitor the various branches of the power grid or virtual power plant to ensure the stability of power distribution and detect problems in a timely manner; equipment monitoring is used to monitor the health status of key equipment in the system and provide fault warnings.

[0119] In this embodiment, preferably, the digital certificate module includes digital certificate application, digital certificate management and authentication rule configuration; the digital certificate application is used to implement the process of applying for a certificate, and the process of applying for a certificate includes the generation of a certificate signing request, user identity authentication and the issuance of a certificate; the digital certificate management is used to manage the life cycle of the application certificate, including certificate renewal, revocation, validity check, backup and recovery; the authentication rule configuration is used by the administrator to set appropriate certificate issuance, verification and use policies;

[0120] The demand response transaction support module includes baseline algorithm configuration, user channel statistics, real-time load monitoring and data quality monitoring; the baseline algorithm configuration is used to calculate and determine the user's load baseline; the user channel statistics are used to analyze the user's participation in different channels; the real-time load monitoring is used to monitor the user's load changes in real time and compare them with the baseline data to evaluate the response effect; the data quality monitoring is used to ensure the integrity, accuracy, consistency and timeliness of the collected data;

[0121] It should be noted that safe and reliable user authentication and certificate management are ensured, while demand response and transaction data are managed efficiently and accurately; the digital certificate module focuses on the complete life cycle management of identity authentication to ensure the security and compliance of communications; the demand response transaction support module focuses on the precise management of user loads in the power system, real-time monitoring and evaluation of response effects, ensuring the efficiency and data quality of demand response; it can provide a more complete power demand response solution while ensuring the safe, stable and efficient operation of the system.

[0122] In this embodiment, preferably, the generation of the certificate signing request first determines the public and private keys, and the calculation formulas of the public and private keys are as follows:

[0123] Generate RSA key pair:

[0124] Choose two large prime numbers p and q,

[0125] Calculate the modulus n = p × q,

[0126] Calculate Euler function

[0127] Select the public key exponent e,

[0128] Calculate the private key exponent d so that

[0129] The public key is (e,n) and the private key is (d,n);

[0130] Calculate the hash value:

[0131] H(M)=SHA-256(M)

[0132] Where M is the content of the CSR data, H(M) is the hash value of the CSR data, and SHA is the hash algorithm;

[0133] Signature generation:

[0134] Sign H(M) using private key (d,n):

[0135] S=H(M) d (modn),

[0136] Where: S is the signature value, d and n are private keys, and the final generated signature S will be included in the CSR data;

[0137] It should be noted that a certificate signing request is a request containing a public key and some identity information, which is used to apply for a digital certificate from a certificate authority (CA). The CSR file is usually generated by the applicant and sent to the certificate authority. The CA generates a digital certificate based on the request information and public key, generates a hash value with the public key and identity information, signs the hash value with the private key, and finally generates a certificate signing request containing a signature, subject information and public key.

[0138] In this embodiment, preferably, the user identity verification is calculated as follows:

[0139] Certificate signature verification: Use the public key (e,n) of the certificate authority to verify the certificate signature.

[0140] Verify(Signature,CA'sPublicKey)=True / False,

[0141] If the verification is successful, the certificate is valid;

[0142] Calculate hash value and compare: Use a hash algorithm to calculate the hash value of the public key information in the certificate and match it with the public key provided by the server or client.

[0143] H(PublicKey)=hash(PublicKey)

[0144] If the hash values ​​are consistent, it proves that the public key has not been tampered with;

[0145] It should be noted that by combining certificate signature verification and hash value comparison verification, the integrity of the digital certificate and the security of the public key are ensured. Both are indispensable and important steps in digital identity authentication, which can effectively prevent information from being tampered with or forged and ensure the security of the user identity authentication process.

[0146] In this embodiment, preferably, the calculation formula of the baseline algorithm configuration is as follows:

[0147] The load baseline of the data is calculated by weighted moving average;

[0148]

[0149] Of which: WMA t is the weighted moving average at time t, i.e., the load baseline; L i is the actual load data at time point i; w i is the weight of time point i, each data point L i is assigned a weight, with closer time points being assigned higher weights, and the weight w i is an arbitrary positive number; N is the time window size used to calculate the weighted moving average;

[0150] Exponential smoothing smoothes the load curve by weighting the historical data;

[0151] S t =αL t +(1-α)S t-1 ,

[0152] Where: S t is the smoothed load value at time t, i.e., the load baseline; L t is the actual load at time t; S t-1 is the smoothing value at time t-1; α is the smoothing factor, and its value range is 0<α<1;

[0153] It should be noted that the load baseline is the normal load of the user over a period of time. The baseline algorithm helps identify abnormal loads and predict future loads. By using the weighted moving average, more recent load data is given a greater weight during calculation, which can better reflect recent load changes.

[0154] In this embodiment, preferably, the active power in the real-time load monitoring is as follows:

[0155]

[0156] Among them, P represents active power, U represents voltage, and I represents current. Expressed as power factor, it represents the phase difference between voltage and current;

[0157] The reactive power calculated by real-time load is as follows:

[0158]

[0159] Among them, Q represents reactive power, U represents voltage, and I represents current. Expressed as the phase difference between current and voltage;

[0160] It should be noted that real-time load monitoring is used to monitor the load in real time to ensure that it operates within a reasonable load range and avoid overload or failure. It can help managers understand the load status of the system in real time and avoid system overload and failure. At the same time, the load forecasting model and the calculation of load change rate also help optimize the system operation efficiency and stability.

[0161] In this embodiment, preferably, the real-time load monitoring adopts a long short-term memory network for prediction, and the calculation formula of the long short-term memory network is as follows:

[0162] Forget Gate:

[0163]

[0164] Among them, f t Represented as the output of the forget gate, the value is between 0 and 1, 1 means that the information of the previous moment is completely retained, and 0 means that it is completely forgotten; W f Represented as the weight matrix of the forget gate; b f Represented as the bias term of the forget gate; Represented as Sigmoid activation function; h t-1 Represented as the hidden state of the previous moment, that is, the output of the previous moment; x t It is expressed as the input at the current moment and as the historical real-time load data;

[0165] Input Gate:

[0166]

[0167] Among them, i t Represented as the output of the input gate, the value is between 0 and 1, indicating the importance of the input at the current moment; W i Represented as the weight matrix of the input gate; b i Represented as the bias term of the input gate; Represented as candidate cell state; W C Represented as a weight matrix, responsible for connecting the hidden state h of the previous moment t-1 and the current input x t Perform linear transformation; b C It is represented as a bias term, which is used to adjust the calculation process of candidate cell states; tanh is represented as a hyperbolic tangent activation function, which maps the linear combination of candidate cell states to the range of [-1,1];

[0168] Update cell status:

[0169]

[0170] Among them, C t Represents the cell state at the current moment; C t-1 Represents the cell state at the last moment; f t Represented as the output of the forget gate; i t The output of the input gate; It is represented as the candidate cell state, which is the calculated active power and reactive power;

[0171] Output Gate:

[0172]

[0173] The hidden state is calculated as:

[0174] h t =ot tanh(C t ),

[0175] Among them, t Represents the output of the output gate; h t Represented as the hidden state at the current moment, that is, the output of LSTM, will be used as the input at the next moment;

[0176] It should be noted that the load state can be predicted through the long short-term memory network, and the real-time active power and reactive power can be used as real-time update data to facilitate the prediction of the load and realize the regulation control.

[0177] In this embodiment, preferably, the data quality monitoring first cleans the data to obtain abnormal values ​​and missing values ​​of the data, and the calculation formula is as follows:

[0178] Handling outliers For each time point, calculate the standardized deviation of the data from the mean and use the Z-Score formula:

[0179]

[0180] Among them, X t is the data at the current time point, μ is the mean value of the data, σ is the standard deviation of the data, and Z represents the standardized deviation of the data from the mean value;

[0181] And when Z exceeds the set threshold, it is marked as an outlier, that is, Z ≥ |3|, and the outlier is deleted;

[0182] Perform statistics on outliers, missing values, and data values ​​in the data:

[0183]

[0184] Among them, X represents the total amount of statistical outliers, missing values, and data values, which are X y , X q and X s , x i Outliers, missing values, and data values ​​represented as statistics;

[0185] The data quality monitoring is calculated by accuracy and completeness:

[0186]

[0187] It should be noted that by detecting outliers in the data and monitoring the quality of the data through the accuracy and completeness of the data, the quality of the data can be improved.

[0188] In this embodiment, preferably, the dispatching system predicts the power dispatching through a prediction model, and the algorithm adopted by the prediction model is ridge regression, which is used to predict continuous numerical outputs and predict a continuous target variable according to input data;

[0189]

[0190] Among them, Loss represents the loss function in ridge regression, y i represents the actual observed value of the i-th sample. It represents the predicted value of the i-th sample. It represents the square error of the i-th sample. It represents the sum of squared errors of samples, and λ represents the regularization parameter, which controls the influence of the regularization term on the model. represents the square of the L2 norm of the regression coefficient, m is the number of input variables in the data set, It represents the L2 regularization term, which penalizes the size of the regression coefficient to avoid overfitting, and β represents the regression coefficient vector;

[0191] It should be noted that ridge regression is used to minimize the sum of squared errors of the data. The introduction of the L2 regularization term avoids overfitting of the model when there are many features and high noise. The introduction of the regularization term enables the prediction model to balance the fitting accuracy of the data and the complexity of the regression coefficient, thereby improving the generalization ability of the prediction model and effectively realizing the scheduling management of peak load.

[0192] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. 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. A user-side resource flexible adjustment and control system, characterized in that: It includes a system layer, an application layer and a data layer. The data layer is used to aggregate data to the system layer. The system layer is used to manage data. The application layer is used to provide support services for demand response and market-based trading business of virtual power plants. The system layer is used to manage, analyze and process data to achieve the core business support of demand response and virtual power plants. The system layer adopts a load control architecture and a distributed B / S architecture to ensure the efficiency of data processing and the security of the system; the system layer mainly includes an access management module, an aggregate resource management module, a capability verification module, an operation monitoring module, a demand response transaction support module and a digital certificate module; The access management module is used to manage the data sources of all access systems, ensuring that the access, modification and deletion of data are effectively monitored and managed; and to control the scope and authority of the access system through identity authentication and authority management; The aggregate resource management module is responsible for aggregating the user's resources to form an overall resource pool to facilitate subsequent unified scheduling and management; The capacity verification module is used to verify the capacity of resources participating in the demand response or virtual power plant market to ensure that the resource's adjustment capacity, process steps and response speed meet market demand; The operation monitoring module is used to monitor the user's electricity usage in real time, collect electricity usage data, and promptly feed it back to the dispatching system to provide data support for demand response operations; The demand response transaction support module provides technical support for the calculation, analysis and processing of demand response events, mainly including load forecasting and scheduling support for participants in the demand response market; The digital certificate module is used to ensure the electronic certificate authentication in all power transactions and data exchange processes, ensure the security and legitimacy of the system, authenticate the identities of both parties to the transaction through digital certificates, and prevent fraud; The data layer is used to collect data from the electricity collection system and the marketing system to achieve real-time synchronization of user profile information; the data layer is also used to collect data from the dispatching system and the trading system to achieve the construction of unified data interaction capabilities for market transactions and provide standardized access services for operator platforms; The application layer is for operators to conduct market subject capability verification and platform testing after unified access to the system layer, and provide support services for market-based trading businesses such as demand response, virtual power plants, air conditioning loads and energy census. The application layer is accessed through the Web terminal; The load-control architecture separates data storage, application logic and interface display functions through a database server, an application server and a Web terminal. The distributed B / S architecture assumes the interface display function through the Web terminal, the application server is responsible for business logic processing, and the database server is responsible for data storage and management.

2. A user-side resource flexible adjustment and control system according to claim 1, characterized in that: The access management module includes operator management, access application, access review, change management and exit management; the operator management is used to manage the access rights, contracts, and service levels of different operators or partners, to authenticate the accessed operators, and to provide services according to the agreement; the access application is used to allow access requests from external systems or devices; the access review is used to approve and verify the access application; the change management is used to process change requests from operators or devices that have already accessed the system; the exit management is used to process exit requests from operators or devices; The capability verification includes test appointment application, test appointment review, test result management, test task management and test progress management; the test appointment application is used to obtain test requirements from external users, and is responsible for collecting and organizing all test requirements; the test appointment review is used to review the test appointment application submitted by the user, and is used to detect the rationality, feasibility and resource matching of the test appointment application; The test result management is responsible for recording, storing and managing all data and results during the test process; The detection task management is used to coordinate and monitor all detection activities, and to realize the assignment, execution and monitoring of tasks; the detection progress management is used to track the real-time progress of each detection task, and the detection activities are carried out according to the predetermined plan; The aggregate resource management module includes operator profile management, virtual power plant profile management, user profile management and branch profile management; the operator profile management is used to connect with suppliers and energy companies to ensure contract fulfillment and service quality; the virtual power plant profile management is used to manage the operation and optimization of virtual power plants and provide intelligent scheduling; the user profile management is used to analyze customer demand response and electricity consumption behavior; the branch profile management is used for stable operation and efficient scheduling management of various branches of the power system; The operation monitoring module includes user monitoring, branch monitoring and equipment monitoring. The user monitoring is used to monitor the user's electricity usage behavior, demand response and energy-saving optimization; the branch monitoring is used to monitor each branch of the power grid or virtual power plant in real time to achieve the stability of power distribution and timely discover branch problems; the equipment monitoring is used to perform health monitoring and fault warning on key equipment in the system.

3. According to claim 1, a user-side resource flexible adjustment and control system is characterized in that: The digital certificate module includes digital certificate application, digital certificate management and authentication rule configuration; the digital certificate application is used to implement the process of applying for a certificate, and the process of applying for a certificate includes the generation of a certificate signing request, user identity verification and the issuance of a certificate; The digital certificate management is used to manage the life cycle of the application certificate, including certificate renewal, revocation, validity check, backup and recovery; The authentication rule configuration is used by the administrator to set appropriate certificate issuance, verification and use policies; The demand response transaction support module includes baseline algorithm configuration, user channel statistics, real-time load monitoring and data quality monitoring; the baseline algorithm configuration is used to calculate and determine the user's load baseline; the user channel statistics are used to analyze the user's participation in different channels; the real-time load monitoring is used to monitor user load changes in real time, and compare them with baseline data to evaluate the response effect; the data quality monitoring is used to ensure the integrity, accuracy, consistency and timeliness of the collected data.

4. A user-side resource flexible adjustment and control system according to claim 3, characterized in that: The generation of the certificate signing request first determines the public and private keys, and the calculation formulas of the public and private keys are as follows: Generate RSA key pair: Choose two large prime numbers p and q, Calculate the modulus n = p × q, Calculate Euler function Select the public key exponent e, Calculate the private key exponent d so that The public key is (e,n) and the private key is (d,n); Calculate the hash value: H(M)=SHA-256(M) Where M is the content of the CSR data, H(M) is the hash value of the CSR data, and SHA is the hash algorithm; Signature generation: Sign H(M) using private key (d,n): S=H(M) d (modn), Among them: S is the signature value, d and n are private keys, and the final generated signature S will be included in the CSR data.

5. A user-side resource flexible adjustment and control system according to claim 4, characterized in that: The user authentication is calculated as follows: Certificate signature verification: Use the public key (e,n) of the certificate authority to verify the certificate signature. Verify(Signature,CA , sPublicKey)=True / False, If the verification is successful, the certificate is valid; Calculate hash value and compare: Use a hash algorithm to calculate the hash value of the public key information in the certificate and match it with the public key provided by the server or client. H(PublicKey)=hash(PublicKey) If the hash values ​​are consistent, it proves that the public key has not been tampered with.

6. A user-side resource flexible adjustment and control system according to claim 3, characterized in that: The calculation formula of the baseline algorithm configuration is as follows: The load baseline of the data is calculated by weighted moving average; Of which: WMA t is the weighted moving average at time t, i.e., the load baseline; L i is the actual load data at time point i; w i is the weight of time point i, each data point L i is assigned a weight, with closer time points being assigned higher weights, with the weight w i is an arbitrary positive number; N is the time window size used to calculate the weighted moving average; Exponential smoothing smoothes the load curve by weighting the historical data; S t =αL t +(1-α)S t-1 , Where: S t is the smoothed load value at time t, i.e., the load baseline; L t is the actual load at time t; S t-1 is the smoothing value at time t-1; α is the smoothing factor, and its value range is 0<α<1.

7. A user-side resource flexible adjustment and control system according to claim 3, characterized in that: The active power in the real-time load monitoring is as follows: Among them, P represents active power, U represents voltage, and I represents current. Expressed as power factor, it represents the phase difference between voltage and current; The reactive power calculated by real-time load is as follows: Among them, Q represents reactive power, U represents voltage, and I represents current. Expressed as the phase difference between current and voltage.

8. A user-side resource flexible adjustment and control system according to claim 7, characterized in that: The real-time load monitoring uses a long short-term memory network for prediction. The calculation formula of the long short-term memory network is as follows: Forget Gate: Among them, f t It is represented as the output of the forget gate, with a value between 0 and 1, 1 means that the information of the previous moment is completely retained, and 0 means that it is completely forgotten; W f Represented as the weight matrix of the forget gate; b f Represented as the bias term of the forget gate; Represented as Sigmoid activation function; h t-1 Represented as the hidden state of the previous moment, that is, the output of the previous moment; x t It is expressed as the input at the current moment and as the historical real-time load data; Input Gate: Among them, i t Represented as the output of the input gate, the value is between 0 and 1, indicating the importance of the input at the current moment; W i Represented as the weight matrix of the input gate; b i Represented as the bias term of the input gate; Represented as candidate cell state; W C Represented as a weight matrix, responsible for connecting the hidden state h of the previous moment t-1 and the current input x t Perform linear transformation; b C It is represented as a bias term, which is used to adjust the calculation process of candidate cell states; tanh is represented as a hyperbolic tangent activation function, which maps the linear combination of candidate cell states to the range of [-1,1]; Update cell status: Among them, C t Represents the cell state at the current moment; C t-1 Represents the cell state at the last moment; f t Represented as the output of the forget gate; i t The output of the input gate; It is represented as the candidate cell state, which is the calculated active power and reactive power; Output Gate: The hidden state is calculated as: h t =o t ·tanh(C t ), Among them, t Represents the output of the output gate; h t Represented as the hidden state at the current moment, that is, the output of LSTM, will be used as the input at the next moment.

9. A user-side resource flexible adjustment and control system according to claim 3, characterized in that: The data quality monitoring first cleans the data to obtain abnormal values ​​and missing values ​​of the data, and the calculation formula is as follows: Handling outliers For each time point, calculate the standardized deviation of the data from the mean and use the Z-Score formula: Among them, X t is the data at the current time point, μ is the mean value of the data, σ is the standard deviation of the data, and Z represents the standardized deviation of the data from the mean value; And when Z exceeds the set threshold, it is marked as an outlier, that is, Z ≥ |3|, and the outlier is deleted; Perform statistics on outliers, missing values, and data values ​​in the data: Among them, X represents the total amount of statistical outliers, missing values, and data values, which are X y , X q and X s , x i Outliers, missing values, and data values ​​represented as statistics; The data quality monitoring is calculated by accuracy and completeness:

10. A user-side resource flexible adjustment and control system according to claim 1, characterized in that: The dispatching system predicts the power dispatching through a prediction model. The prediction model uses a ridge regression algorithm to predict continuous numerical outputs and predict a continuous target variable based on input data. Among them, Loss represents the loss function in ridge regression, y i represents the actual observed value of the i-th sample. It represents the predicted value of the i-th sample. It represents the square error of the i-th sample. It represents the sum of squared errors of samples, and λ represents the regularization parameter, which controls the influence of the regularization term on the model. represents the square of the L2 norm of the regression coefficient, m is the number of input variables in the data set, It represents the L2 regularization term, which penalizes the size of the regression coefficient to avoid overfitting, and β represents the regression coefficient vector.

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