Virtual power plant energy scheduling and management system based on artificial intelligence

Abstract

According to the virtual power plant energy scheduling and management system based on artificial intelligence, dispersed energy resources are integrated through an intelligent technology, and energy distribution is optimized. The system adopts a machine learning algorithm and data analysis, accurately predicts supply and demand, and improves efficiency. The system comprises an information sharing module, a safety guarantee module, a communication deployment module, a data management module, an intelligent optimization scheduling module, a user interaction interface module and the like, and supports multi-energy network convergence. An encryption technology is adopted to guarantee data security, a user interface simplifies operation, and experience is improved. The system provides an efficient and safe platform for energy transaction and management, supports global energy internet construction, and has important social and economic values.

Description

Technical Field

[0001] The present invention relates to the field of energy management and allocation, and in particular to an artificial intelligence-based virtual power plant energy dispatching and management system and an operation method thereof. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection awareness, the traditional energy consumption model dominated by fossil energy is facing unprecedented challenges. Countries around the world are promoting the transformation of energy structure from dependence on fossil energy to clean energy. In this process, cross-regional energy management and allocation technology has become particularly critical, which can promote the efficient use and optimal allocation of energy.

[0003] However, existing energy management systems mostly adopt local and decentralized management strategies, lacking cross-regional and cross-industry coordination and integration capabilities. This has led to problems such as imbalanced energy supply and demand, low transmission efficiency, and insufficient utilization of energy storage resources. In addition, with the large-scale access to renewable energy, how to effectively manage the intermittent and distributed characteristics of these energy sources and ensure the stability and security of energy supply has become an urgent problem to be solved.

[0004] In order to overcome these challenges, an intelligent energy management system is needed that can process and analyze large amounts of energy data, predict changes in energy supply and demand, optimize energy distribution strategies, and ensure the security of energy transmission. At the same time, the system should also support the deep integration of multiple energy networks, including power grids, natural gas grids, heating systems, etc., to achieve energy complementarity and sharing. Therefore, the artificial intelligence-based virtual power plant energy dispatching and management system proposed in the present invention aims to improve the efficiency and safety of energy management through intelligent technology, support the construction of the global energy Internet, and provide technical guarantees for achieving sustainable development of global energy. Summary of the invention

[0005] The artificial intelligence-based virtual power plant energy dispatching and management system proposed in this invention aims to achieve effective integration and optimized dispatching of decentralized energy resources through intelligent technology to improve energy utilization efficiency and system stability. The system is designed with a modular architecture to facilitate the integration of new technologies and functions while ensuring the scalability and maintainability of the system. The following are the specific technical solutions and operation procedures of the system: Preferably, system initialization and configuration: when the system starts, initialization settings are performed, including network parameter configuration, security protocol setting, initialization of energy network model, etc., and key parameters of the system are defined, such as data collection frequency, encryption key length, time scale for optimized scheduling, etc.

[0006] Preferably, data collection and preprocessing: collect raw data from energy networks such as power grids, natural gas grids, and heating systems in various regions, including electricity consumption, natural gas flow, heating demand, etc. Perform preprocessing operations such as quality inspection, denoising, and standardization on the collected data to ensure the accuracy and consistency of the data.

[0007] Optimally, unified modeling and information sharing are the core links in the virtual power plant energy dispatch and management system, which involves using artificial intelligence algorithms to conduct in-depth analysis of pre-processed data and build a comprehensive energy network model. The following is a detailed description of the process:

[0008] 1. Feature extraction: Extract key features from preprocessed data using algorithms such as neural networks or support vector machines (SVM). A neural network can be represented as a function f θ (x), where x is the input data, θ is the network parameter, and f is the network structure. SVM finds the optimal hyperplane in the data set, which can be expressed as the following convex optimization problem: subjectto Where w is the weight vector, b is the bias term, is a nonlinear mapping of data points x, y i It's a mark.

[0009] 2. Data fusion: Fusion of data from different energy networks to form a unified data set. Data fusion can be expressed as an integration function g(D1, D2, ..., D n ), where D i are datasets from different energy networks, and g is the fusion operation.

[0010] 3. Construct a unified energy network model: Based on the results of feature extraction and data fusion, a unified energy network model M is constructed. The model M can be expressed as M = h(D processed ), where h is the model building function, D processed It is the data after feature extraction and data fusion. 4. State evaluation and performance prediction: Use the constructed model M to evaluate the state of the energy network and predict the performance. State evaluation can be expressed as S = M (s, p), where s is the current state parameter and p is the performance parameter. Performance prediction can be expressed as P = M (S, t), where t is the time parameter and P is the predicted performance.

[0011] Preferably, security measures are implemented: In the virtual power plant energy dispatching and management system, data security is of vital importance, so the system uses end-to-end encryption and role-based access control to ensure data security and the legality of access: End-to-end encryption ensures that data is always encrypted during transmission between the sender and the receiver, preventing data from being intercepted and tampered with during transmission.

[0012] Encryption process:

[0013] S1. Use a symmetric encryption algorithm (such as AES) to encrypt data D and generate ciphertext C, C = E K (D) Among them, E K is the encryption function and K is the symmetric key.

[0014] S2. The sender sends the ciphertext C to the receiver through an insecure network.

[0015] S3. The receiver uses the same symmetric key K to decrypt the ciphertext C and restore the original data D, D = D K (C) Among them, D K is the decryption function.

[0016] Role-based access control is a common security mechanism that limits access to sensitive data based on the user's role.

[0017] Access control process:

[0018] S1. Define roles R and permissions P, as well as the relationship between roles and permissions RPRP = {(r1, p1), (r2, p2), ..., (r n , p n )} Among them, r i It's a role, p i It's permissions.

[0019] S2. Assign one or more roles to each user U. in, It is the role of user U. S3. When user U requests to access sensitive data SD, the system checks whether the user's role has the corresponding permission. Among them, Permissions(SD) is a set of permissions required to access data SD. S4. If the role of user U has access rights, access is allowed; otherwise, access is denied.

[0020] Through the above-mentioned end-to-end encryption and role-based access control mechanism, the virtual power plant energy dispatching and management system can ensure the security of data during transmission, while limiting access to sensitive data and allowing only authorized users to perform operations, thereby protecting the data security and integrity of the system.

[0021] Optimally, cross-industry communication and data exchange: Establish communication links between electricity, natural gas and heating networks according to pre-defined communication protocols. Through the data exchange platform, real-time data sharing and exchange are realized, providing real-time data support for energy trading and scheduling.

[0022] Optimization, intelligent optimization and scheduling: In the virtual power plant energy scheduling and management system, genetic algorithms (GA) and ant colony algorithms (ACO) are used to formulate energy scheduling formulas. Genetic algorithms iteratively generate better solutions from a set of candidate solutions by simulating the natural selection process. It first randomly generates an initial population and then evaluates each individual based on a fitness function (such as minimizing cost or maximizing energy efficiency). Through selection, crossover, and mutation operations, the algorithm iterates continuously and gradually evolves better solutions. until the optimal or satisfactory energy allocation is found. For example, the fitness function can be defined as f(x) = ∑ i， j c ij x ij , where c ij represents the cost from node i to j, and x ij Represents the corresponding energy flow.

[0023] The ant colony algorithm imitates the path selection behavior of ants looking for food, and guides the path selection through the concentration of pheromones. At the beginning of the algorithm, the amount of pheromones on each path is the same. As the algorithm progresses, ants choose paths based on heuristic information and pheromone concentration, and leave pheromones on the paths they travel. The concentration of pheromones will gradually evaporate over time, but the pheromones on excellent paths will increase as more ants choose them. This process is constantly iterated, and eventually the optimal path from the energy supply point to the demand point can be found. When optimizing the calculation, the algorithm will take into account the transmission line capacity C line and the charge and discharge limits of the energy storage device C storagc And other constraints to ensure the feasibility and efficiency of the deployment plan.

[0024] Preferably, user interaction and energy transaction execution: through the user interaction interface, users can view energy usage, make transaction requests and receive energy management suggestions. The transaction management unit executes energy transactions, updates energy allocation plans, and feeds back transaction results to users based on user requests and optimization scheduling results.

[0025] Preferably, system monitoring and evaluation: the system monitors the operation status of the energy network and the execution of energy transactions in real time. Regularly evaluate system performance, such as energy allocation efficiency, user satisfaction, etc., and adjust system parameters and optimization strategies based on the evaluation results.

[0026] Preferably, emergency response and security monitoring: real-time monitoring of the security status of the system, including the security of data transmission and the stability of the energy network. Establish an emergency response mechanism, and once an abnormality or security threat is detected, immediately activate the emergency plan to restore the system and protect data.

[0027] Optimize performance evaluation and system optimization: Regularly evaluate system performance, including indicators such as energy allocation accuracy, response speed, and user satisfaction. Based on the evaluation results, continuously optimize system parameters and algorithms to improve the overall system performance and user experience.

[0028] The present invention solves a series of key technical problems to improve the performance and reliability of the virtual power plant energy dispatching and management system. First, by establishing a unified energy network model and information sharing mechanism, the system effectively solves the information island problem existing in the cross-regional energy network, and improves the efficiency of energy data integration and the optimal configuration of energy resources. Secondly, by adopting advanced encryption technology and security protocols, the system ensures the security of energy data during transmission and storage, enhances the privacy protection of data, and thus improves the security of the system and the trust of users. In addition, by formulating and implementing a unified cross-industry communication protocol, the system establishes an efficient data exchange and communication mechanism, and solves the communication efficiency problem caused by different communication standards and incompatible protocols between different energy network operators and service providers. The system also significantly improves the accuracy of energy supply and demand forecasting by applying machine learning algorithms, and provides reliable decision support for energy optimization and dispatching. At the same time, the design of the intelligent optimization and dispatching module takes into account multiple factors such as energy type, supply and demand changes, and energy storage capacity, and effectively responds to the complexity of energy optimization and dispatching. Finally, the friendly design of the user interface simplifies energy trading and management operations, improves the user's operational convenience and satisfaction, and thus encourages wider social participation and the use of clean energy. In summary, the present invention not only brings technological innovation to the energy industry, but also provides strong support for the construction of the global energy Internet and the promotion of the widespread application of clean energy, and has significant social and economic value.

[0029] The virtual power plant energy dispatching and management system of the present invention has significant beneficial effects, which are mainly reflected in the following aspects: Through unified modeling and information sharing mechanisms, the system greatly improves the integration efficiency and utilization value of data between different energy networks, thereby promoting the efficient allocation and optimized management of energy resources.

[0030] The advanced encryption technology and security protocols used effectively protect the security and privacy of energy data, enhance the reliability of the system and the trust of users. Thirdly, the establishment of cross-industry communication protocols solves the compatibility problem of communications between different energy industries, realizes the seamless sharing of energy data, and provides a solid foundation for cross-regional energy transactions.

[0031] The application of intelligent optimization and scheduling algorithms has significantly improved the accuracy of energy supply and demand forecasts and the efficiency of energy allocation, optimized the use of energy storage resources, and enhanced the stability and risk resistance of the energy system.

[0032] The friendly design of the user interface simplifies energy trading and management operations, improves user experience, and encourages wider social participation and the use of clean energy. In summary, the present invention not only brings technological innovation to the energy industry, but also provides strong support for the construction of the global energy Internet and the promotion of the widespread application of clean energy, and has important social and economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The system operation flow chart of the present invention is DETAILED DESCRIPTION

[0034] like Figure 1 As shown, the implementation of the virtual power plant energy dispatching and management system of the present invention can be carried out according to the following detailed steps: S1. System initialization and configuration phase: Before the system is put into use, detailed initialization and configuration work is carried out. This includes but is not limited to the configuration of network parameters to ensure that the system can communicate smoothly with the energy network in each region; setting security protocols and using 256-bit AES encryption technology to provide high-standard encryption protection for the data transmission process; initializing the energy network model and defining the data collection frequency to be once every 5 minutes to achieve real-time monitoring of energy consumption dynamics.

[0035] S2. Data collection and preprocessing stage: Deploy a data collection system to collect raw data such as electricity consumption, natural gas flow, and heating demand from energy networks such as power grids, natural gas grids, and heating systems in various regions. These data are then subject to strict quality checks, including denoising to eliminate outliers in the data and standardization to ensure consistency in data format, laying a solid foundation for subsequent analysis and processing.

[0036] S3. Unified modeling and information sharing stage: Using artificial intelligence algorithms, such as neural networks or support vector machines, to perform in-depth feature extraction and data fusion on pre-processed data, a comprehensive, multi-dimensional energy network model is constructed. This model can not only reflect the state of the energy network in real time, but also predict system performance, providing scientific decision support for subsequent optimization and scheduling.

[0037] S4. Security measures implementation phase: Start the data encryption unit to perform end-to-end encryption on all data in transmission to ensure that the data is secure at any stage of transmission. At the same time, implement a role-based access control system to strictly manage access rights to energy data. Only authorized users can access sensitive data, thereby effectively preventing data leakage and unauthorized access.

[0038] S5. Cross-industry communication and data exchange stage: Based on the pre-defined communication protocol, communication links between different energy networks are established to ensure seamless data exchange between electricity, natural gas and heating networks. The establishment of a data exchange platform provides a stable and reliable environment for real-time data sharing, thereby supporting the smooth execution of energy trading and scheduling.

[0039] S6. Intelligent optimization and dispatching stage: Use advanced optimization algorithms such as genetic algorithms or ant colony algorithms, combined with energy supply and demand forecast results and real-time energy network status, to formulate detailed energy allocation plans. When formulating the plan, fully consider constraints such as transmission line capacity and charging and discharging restrictions of energy storage equipment, and obtain the optimal solution for energy allocation through optimization calculation.

[0040] S7. User interaction and energy trading execution phase: Through the user interaction interface, users can easily view their energy usage, initiate trading requests, and receive energy management suggestions provided by the system. The trading management unit will execute energy trading, update the energy allocation plan, and feedback the trading results to the user based on the user's request and the results of the optimization scheduling.

[0041] S8. System monitoring and evaluation phase: The system will monitor the operation status of the energy network and the execution of energy transactions in real time to ensure the stable operation of the system. At the same time, the system performance will be evaluated regularly, including but not limited to key indicators such as energy allocation efficiency and user satisfaction, and the system parameters and optimization strategies will be adjusted according to the evaluation results to achieve continuous performance improvement.

[0042] Specific parameters and results: The frequency of data collection is set at once every 5 minutes, ensuring the system’s real-time response capability to energy consumption dynamics.

[0043] The encryption key length uses 256-bit AES encryption, which provides strong security for data transmission.

[0044] The optimized scheduling time scale is divided into short-term (1 hour) and long-term (24 hours) to adapt to the energy management needs at different time scales.

[0045] The constraints include the maximum capacity of the transmission line of 1000MW and the charging and discharging efficiency of the energy storage equipment of 90%. These parameters are crucial to the formulation of the optimization algorithm.

[0046] Performance evaluation indicators show that energy allocation efficiency has increased by 20% and user satisfaction has increased by 30%. These significant improvements demonstrate the effectiveness of the system and its high recognition by users.

[0047] Through this specific implementation method, the virtual power plant energy dispatching and management system of the present invention can realize efficient management and intelligent allocation of large-scale, multi-type energy networks, ensure the stability and security of energy supply, while providing a user-friendly interactive experience and promoting the intelligence and sustainable development of the energy industry. DETAILED DESCRIPTION

[0048] S1. System deployment and integration: Deploy the system in energy control centers in multiple regions, ensuring that each center can communicate with the central management system. Achieve seamless integration with existing energy infrastructure through highly customizable configuration options.

[0049] S2. Real-time data monitoring and collection: Using the Internet of Things (IoT) technology, real-time monitoring of key parameters of each energy network, such as voltage, current, pressure and temperature, etc. Set data collection points and automatically upload data to the central database every 15 minutes to ensure the timeliness and integrity of the data.

[0050] S3. Energy data analysis and processing: Apply big data analysis technology to process the massive amount of energy data collected to identify abnormal patterns and potential risks. Use machine learning algorithms such as random forests or gradient boosting machines to extract features and recognize patterns in the data to predict energy demand and supply trends.

[0051] S4. Optimization model establishment and solution: Establish a multi-objective energy optimization model that comprehensively considers multiple dimensions such as energy type, price, environmental impact, and user needs. Apply optimization techniques such as linear programming or mixed integer programming to solve the optimal energy allocation plan to minimize costs and maximize efficiency.

[0052] S5. Energy dispatch strategy formulation and execution: Based on the optimization results, formulate detailed energy dispatch strategies, including energy distribution, charging and discharging plans for energy storage equipment, etc. Through the automated control system, the dispatch strategies are sent to various energy networks to ensure accurate execution of the strategies.

[0053] S6. Security monitoring and emergency response: Real-time monitoring of the security status of the system, including the security of data transmission and the stability of the energy network. Establish an emergency response mechanism to immediately initiate emergency plans to restore the system and protect data once an anomaly or security threat is detected.

[0054] S7. User interface and interaction design: Design an intuitive user interface to provide real-time data display, energy trading operations, system status monitoring and other functions. Through user behavior analysis, optimize the interface layout and interaction process to improve user convenience and satisfaction.

[0055] S8. Performance evaluation and system optimization: Regularly evaluate system performance, including indicators such as accuracy of energy allocation, response speed, and user satisfaction. Based on the evaluation results, continuously optimize system parameters and algorithms to improve the overall system performance and user experience.

[0056] Specific parameters and results: At least 100 in each area, ensuring the extensiveness and meticulousness of data collection. Once every 15 minutes, balancing real-time performance and efficient use of system resources. No more than 5 minutes, demonstrating the efficiency of the optimization algorithm. Emergency response time: Started within 1 minute after anomaly detection, ensuring the system's rapid response capability. Feedback is given within 1 second after user operation, providing a smooth user experience.

[0057] Through this specific implementation method, the cross-regional energy management and allocation system of the present invention can realize efficient management and intelligent allocation of large-scale, multi-type energy networks, ensure the stability and security of energy supply, while providing a user-friendly interactive experience and promoting the intelligence and sustainable development of the energy industry.

Claims

1. An artificial intelligence-based virtual power plant energy dispatching and management system, characterized by Integrate the following modules: Information sharing and unified modeling module, which realizes data integration and standardization of different energy networks, connects and interacts with power grid, natural gas grid, heating system, etc. through energy network interface unit, integrates and processes data through energy data fusion unit, and provides unified data view; The security module uses a data encryption unit to encrypt the transmitted and stored energy data, and an access control unit to manage and control access rights to energy data, ensuring that only authorized users can access sensitive data; The communication and deployment module includes a cross-industry communication protocol unit for developing and implementing communication standards and protocols, and a data exchange platform for supporting data exchange and information sharing between different energy business systems; Energy data management module, including energy data center for centralized storage and management of energy-related data, and data analysis engine for analysis and mining of collected energy data to provide decision support; Intelligent optimization and scheduling module, using the supply and demand forecasting unit to predict energy supply and demand changes based on historical data and real-time information, the optimization algorithm unit to formulate energy allocation plans based on the forecast results and energy network status, and the energy storage management unit to monitor and manage the status of energy storage equipment and optimize the efficiency of energy storage resources; The user interaction interface provides a transaction management unit for processing user energy transaction requests and orders, a visualization unit for graphically displaying energy data and system status, and a self-service unit for allowing users to self-query energy usage and self-manage energy transactions. The transaction management unit allows users to easily submit and track energy transaction requests, view order status in real time, and make adjustments or cancellations; Visual display unit, which clearly displays energy data and system status to users through charts and graphical interfaces, including supply and demand curves, energy flows and real-time usage, allowing users to intuitively grasp market dynamics; The self-service unit provides users with a convenient platform to query energy usage and manage energy transactions by themselves, such as adjusting purchase volume or setting automatic purchase strategies. These functions are designed to enhance user experience and ensure efficiency, transparency and personalization of energy trading and management processes.

2. The system according to claim 1, characterized in that The intelligent optimization and scheduling module further includes: Environmental Impact Assessment Unit: This unit is responsible for evaluating the potential impact of different energy deployment options on the environment. It may use environmental impact assessment models such as life cycle assessment (LCA) or carbon footprint calculations to quantify the impact of energy deployment options on ecosystems, greenhouse gas emissions and other environmental factors. Cost-Benefit Analysis Unit: This unit is used to calculate the economic costs and benefits of different energy deployment options. It may use cost-benefit analysis (CBA) to compare the total costs and total benefits of different options to ensure that the option with the greatest cost-effectiveness is selected. User behavior analysis unit: This unit is used to analyze the energy usage patterns of users. It may apply machine learning algorithms, such as cluster analysis or association rule learning, to identify the energy usage behavior patterns of users and provide personalized energy management recommendations to users based on these patterns. These units work together to ensure that the energy allocation plan not only maximizes economic benefits, but also minimizes the impact on the environment, while meeting the user's personalized energy management needs. By comprehensively considering these factors, the intelligent optimization and scheduling module can develop a more comprehensive and sustainable energy management strategy.

3. The system according to claim 1, characterized in that Further optimization of the security module includes: The security event monitoring unit is a key component in the virtual power plant energy dispatch and management system. It is responsible for real-time monitoring and identification of potential security threats and abnormal behaviors within the system. The unit collects system operation data, such as access logs, operation records, and network traffic, and uses advanced machine learning models or rule engines for analysis to identify abnormal patterns that do not match normal behavior patterns. Once abnormal behavior is detected, the unit will immediately issue an alarm and pass the information to the emergency response unit or system administrator for further action. The emergency response unit is another key component of the system, which automatically initiates the emergency plan after the security event monitoring unit issues an alarm. The purpose of this unit is to respond quickly to security incidents and protect systems and data and reduce potential losses by performing a series of predetermined operations. These operations may include system isolation, data backup, encryption protection, and system recovery. The design of the emergency response unit ensures that the system can quickly resume normal operation when faced with security threats while protecting critical data from damage. Through the close collaboration of these two units, the virtual power plant energy dispatching and management system can ensure its stability and reliability in the face of various security challenges.

4. The system according to claim 1, characterized in that The user interaction interface further includes: a feedback and support unit for collecting user feedback and providing user support services to continuously improve system performance and user experience.

5. A cross-regional energy management and allocation method using the system described in any one of claims 1 to 4, characterized in that: The following steps are involved: S1. Establish and maintain a unified energy network model: Generally, the energy network model can be represented by a graph G = (N, E), where N is a set of nodes, representing various energy points in the energy network (such as power stations, energy storage equipment, etc.), and E is a set of edges, representing the path of energy flow. S2. Deploy and implement security measures: The encryption algorithm can be expressed as E(K, M)→C, where E is the encryption function, K is the key, M is the plaintext, and c is the ciphertext. S3. Formulate and follow cross-industry communication protocols: The communication protocol can be abstracted into a series of rules P = {p1, p2, ..., p n }, these rules define how data is encapsulated, transmitted, and decoded. S4. Collect, store and process energy data: Data preprocessing can be expressed as D processed =f(D raw ), where D raw is the original data, D processed is the preprocessed data, and f is the preprocessing function. S5. Use artificial intelligence algorithms to intelligently predict and optimize energy supply and demand: Supply and demand forecasts may use time series analysis, such as the autoregressive integrated moving average (ARIMA) model: t = c + Optimal scheduling may use linear programming: max{c T x|Ax≤b,x≥0}, where c is the payoff vector, x is the decision variable, A is the constraint matrix, and b is the constraint vector. S6. Provide real-time energy management and trading services through a user interaction interface: User interaction can be measured by the response time R, and the goal is to minimize user waiting time.

6. The method according to claim 5, characterized in that Further including: Dynamically adjust energy allocation plans based on energy supply and demand forecasts and energy network status to adapt to changes in energy markets and consumption patterns; Use energy storage equipment to regulate energy in the short and long term to improve the stability and reliability of the energy system; provide personalized energy management recommendations and optimization solutions to improve energy efficiency and reduce energy costs.

7. A computer-readable storage medium comprising a software implementation of any system or method of claim 1, characterized in that The storage medium is a carrier of non-transient signals, such as a hard disk, solid state drive, optical disk, USB flash drive or other digital storage medium.

8. An embodiment of a cross-regional energy management and allocation system and method thereof, characterized in that The invention comprises the system described in any one of claim 1 and the method described in any one of claims 5 to 6. Through the detailed description of the above claims, we provide a comprehensive scope of protection for the system and method, while ensuring the detail and specificity of each claim. Each claim elaborates on the components, functions, and operating methods of the system in detail to meet the requirements of patent protection.

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