Risk scheduling optimization method and system for power system

Abstract

The invention provides a risk scheduling optimization method and system of a power system. The risk scheduling optimization system for the power system comprises a data acquisition module which is used for collecting power demand, supply, market price, equipment state and other related information in real time, and the module realizes efficient data acquisition through various sensors, data interfaces and communication protocols, the data acquisition module has a data preprocessing function so as to reduce the influence of delay and inaccuracy on subsequent modules, and the data acquisition module further has data verification and error detection functions. The power system risk scheduling optimization method and system of the technical scheme have a plurality of remarkable advantages. Through the efficient data acquisition module, the system can acquire key information such as power demand, power supply, market price and equipment state in real time, and the accuracy and reliability of data are remarkably improved by using data preprocessing and verification functions.

Description

Technical Field

[0001] The present invention relates to the technical field of power system risk dispatching, and in particular to a method and system for optimizing risk dispatching of a power system. Background Art

[0002] The risk dispatch optimization method of the power system mainly consists of the following parts: data acquisition module, risk assessment module, optimization dispatch module and decision support system. The data acquisition module is responsible for collecting real-time data of the power market, including information such as power demand, supply and market price. The risk assessment module uses the collected data to analyze potential risk factors, such as equipment failure, demand fluctuations and market price instability, to assess their impact on the power system. Based on risk assessment, the optimization dispatch module uses optimization algorithms to formulate the best power generation dispatch plan to maximize economic benefits and reduce risks. At the same time, the decision support system provides managers with intuitive decision-making basis through visualization tools, enabling them to adjust dispatch strategies in a timely manner.

[0003] Although the system has certain optimization capabilities in power dispatching, it still has some defects. The data acquisition module is highly dependent on real-time data. If the data update is delayed or inaccurate, it will directly affect the accuracy of risk assessment. The selection of optimization algorithms and parameter settings may also affect the effectiveness of dispatch results. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a risk scheduling optimization method and system for a power system, which solves the problem that the data acquisition module has a strong dependence on real-time data, and data update delays or inaccuracies will directly affect the accuracy of risk assessment; the selection of optimization algorithms and parameter settings may also affect the effectiveness of scheduling results.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method and system for optimizing risk dispatch of a power system, comprising:

[0006] A data acquisition module, which is used to collect power demand, supply, market price, equipment status and other related information in real time. The module realizes efficient data acquisition through multiple sensors, data interfaces and communication protocols, and has a data preprocessing function to reduce the impact of delays and inaccuracies on subsequent modules. The data acquisition module further includes data verification and error detection functions to improve the accuracy and reliability of data and support real-time monitoring and feedback of collected data;

[0007] The risk assessment module uses a dynamic risk assessment model based on the collected data to analyze and evaluate potential risks, generate risk reports and provide decision-making basis to form a decision support system. The calculation formula of the risk value R is:

[0008]

[0009] Among them, w i is the weight of each risk, p i In order to respond to the probability of risk occurrence, the risk assessment module combines an adaptive adjustment mechanism, regularly updates the risk assessment model, uses the fusion technology of historical data and real-time data to enhance the accuracy of risk assessment, and simulates risk situations in different scenarios through scenario analysis functions to help decision makers predict potential risks;

[0010] The optimization scheduling module uses an adaptive optimization algorithm to formulate a scheduling plan for power generation units based on the risk assessment results. The mathematical model of the comprehensive optimization target is:

[0011] minZ = C + α·R + β·E;

[0012] Wherein, Z is the comprehensive target value, C is the power generation cost, R is the risk value, E is the environmental impact, α and β are weight coefficients. The optimization scheduling module has the ability of hierarchical scheduling. Through the multi-objective optimization method, the scheduling strategy can be flexibly adjusted to adapt to the complex power market environment. The optimization scheduling module can use intelligent algorithms to adjust the scheduling strategy according to real-time feedback to cope with market fluctuations;

[0013] The cloud computing platform utilizes distributed computing and storage resources to improve the processing power and response speed of the system under high load conditions, ensure the high efficiency of real-time data updating and analysis, and support concurrent access by multiple users.

[0014] Preferably, the data acquisition module integrates external data sources through an interface with a third-party market information system to improve the comprehensiveness and accuracy of risk assessment, including weather forecasts and power market dynamic information. The optimization scheduling module uses blockchain technology to record important data in the scheduling decision-making process to ensure data security and transparency, enhance the credibility of the system, and ensure the traceability and non-tamperability of historical data.

[0015] Preferably, the decision support system has a management mechanism based on smart contracts to automate the execution of rules in the power dispatching process, improve dispatching efficiency and compliance, automatically generate compliance reports, and reduce management burden. The cloud computing platform utilizes distributed computing and storage resources to improve the system's processing power and response speed under high load conditions, ensure the efficiency of real-time data updating and analysis, and support concurrent access by multiple users.

[0016] Preferably, the optimization scheduling module is combined with a dynamic model to form a dynamic scheduling strategy through real-time feedback and adjustment, thereby ensuring the optimal scheduling result under uncertain conditions and enhancing the flexibility and adaptability of the system.

[0017] Preferably, the risk assessment module also has self-learning capabilities, which optimizes the risk assessment model by continuously learning new data, reduces human intervention, and improves the accuracy of risk prediction. The data acquisition module is equipped with Internet of Things devices to collect device status and operating parameters in real time, thereby enhancing the system's monitoring capabilities for equipment failures and abnormal conditions.

[0018] Preferably, the optimization scheduling module uses intelligent optimization technologies such as genetic algorithms and particle swarm optimization algorithms to perform multi-objective scheduling optimization to seek a balance between different objectives and improve the overall scheduling effect.

[0019] Preferably, the decision support system integrates deep learning technology, which can perform pattern recognition based on historical scheduling data and predict future scheduling needs to enhance the foresight of decision-making.

[0020] A risk dispatch optimization method for a power system comprises the following steps:

[0021] a. Use sensors and data interfaces to collect information such as power demand, supply, market price and equipment status in real time, and reduce delays and inaccuracies through data preprocessing functions, and integrate data from third-party market information systems to improve the comprehensiveness and accuracy of risk assessment;

[0022] b. Use dynamic risk assessment models to analyze potential risks and generate risk reports;

[0023] c. Based on the risk assessment results, use the adaptive optimization algorithm to develop a scheduling plan;

[0024] d. With the help of decision support system, the operation status and scheduling plan are displayed in real time, and deep learning technology is used for pattern recognition to predict future scheduling needs, enhance the foresight of decision-making, implement smart contract management mechanism, automatically execute scheduling rules and generate compliance reports;

[0025] e. Monitor equipment status and operating parameters in real time, detect abnormal situations in a timely manner, and dynamically adjust scheduling strategies based on feedback information to ensure optimized scheduling under market fluctuations and uncertainties.

[0026] The present invention provides a method and system for optimizing risk dispatch of a power system, which has the following beneficial effects:

[0027] The power system risk dispatch optimization method and system of this technical solution have several significant advantages. First, through the efficient data acquisition module, the system can obtain key information such as power demand, supply, market price and equipment status in real time, and use data preprocessing and verification functions to significantly improve the accuracy and reliability of the data. This real-time data integration not only enhances the comprehensiveness of risk assessment, but also timely identifies potential risks through dynamic risk assessment models to ensure the scientific and forward-looking basis of decision-making. At the same time, the system combines third-party market information to further improve the accuracy of the assessment results, thereby enhancing the effectiveness of the overall dispatch plan.

[0028] The optimization scheduling module adopts adaptive optimization algorithms and multi-objective optimization methods, so that the scheduling strategy can flexibly respond to the complex power market environment. Through the use of intelligent algorithms, the system can quickly adjust the scheduling plan according to real-time feedback to ensure the optimal scheduling results under uncertain conditions. In addition, the introduction of the cloud computing platform improves the processing power and response speed of the system, supports concurrent access by multiple users, and effectively responds to challenges under high load conditions. These advantages make this technical solution outstanding in improving power scheduling efficiency, reducing risks and optimizing resource allocation, thereby improving the overall operating efficiency and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the structure of the present invention;

[0030] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1-2As shown, the embodiment of the present invention provides a risk scheduling optimization method and system for a power system, including a data acquisition module for real-time collection of power demand, supply, market price, equipment status and other related information. The module realizes efficient data acquisition through a variety of sensors, data interfaces and communication protocols, and has a data preprocessing function to reduce the impact of delays and inaccuracies on subsequent modules. The data acquisition module further includes data verification and error detection functions. The data verification adopts cyclic redundancy check and hash check and other technologies. The error detection uses parity check and checksum algorithm to realize rapid detection and correction of errors in the data transmission process, improve data integrity, improve data accuracy and reliability, and support real-time monitoring and feedback of collected data. The data acquisition module integrates external data sources through an interface with a third-party market information system to improve the comprehensiveness and accuracy of risk assessment, including weather forecasts and power market dynamic information. The optimization scheduling module uses blockchain technology to record important data in the scheduling decision-making process, ensure data security and transparency, improve the credibility of the system, and ensure the traceability and non-tamperability of historical data. In the data acquisition module, sensors include smart meters, temperature sensors, pressure sensors, flow meters, and support multiple communication protocols, such as Modbus, Profibus, IEC 61850 realizes comprehensive monitoring of all aspects of the power system. The data interface adopts high-speed Ethernet, wireless communication and optical fiber communication to ensure the high speed and reliability of data transmission.

[0033] The risk assessment module uses a dynamic risk assessment model based on the collected data to analyze and evaluate potential risks, generate risk reports and provide decision-making basis to form a decision support system. The calculation formula of the risk value R is:

[0034]

[0035] Among them, w i is the weight of each risk, p iIn order to respond to the probability of risk occurrence, the risk assessment module combines an adaptive adjustment mechanism, regularly updates the risk assessment model, and uses the fusion technology of historical data and real-time data to enhance the accuracy of risk assessment. The scenario analysis function simulates the risk situation in different scenarios to help decision makers predict potential risks. The scenario analysis uses Monte Carlo simulation and scenario tree technology to simulate different scenarios such as market price fluctuations, load changes, and equipment failures, evaluate the risk impact in each scenario, and provide decision makers with comprehensive risk prediction. The decision support system has a management mechanism based on smart contracts to realize the automation of rule execution in the power dispatching process, improve dispatching efficiency and compliance, and automatically Generate compliance reports in a digital way to reduce management burden. The cloud computing platform uses distributed computing and storage resources to improve the system's processing power and response speed under high load conditions, ensure the efficiency of real-time data updating and analysis, and support concurrent access by multiple users. The data preprocessing function uses filtering algorithms such as Kalman filtering, data interpolation, outlier detection and normalization to clean and standardize the collected data, eliminate noise and outliers, and ensure the accuracy of subsequent analysis. The risk assessment model uses a combination of Bayesian networks and fuzzy logic to establish the correlation between risk factors, and realizes dynamic assessment of complex risks by updating conditional probabilities and fuzzy memberships in real time.

[0036] The optimization scheduling module uses an adaptive optimization algorithm to formulate a scheduling plan for power generation units based on the risk assessment results. The mathematical model of the comprehensive optimization target is:

[0037] minZ = C + α·R + β·E;

[0038] Among them, Z is the comprehensive target value, C is the power generation cost, R is the risk value, E is the environmental impact, α and β are weight coefficients. The optimization scheduling module has the ability of hierarchical scheduling. Through the multi-objective optimization method, it can flexibly adjust the scheduling strategy to adapt to the complex power market environment. The optimization scheduling module can use intelligent algorithms to adjust the scheduling strategy according to real-time feedback to cope with market fluctuations. The optimization scheduling module combines dynamic models to form dynamic scheduling strategies through real-time feedback and adjustment to ensure the optimal scheduling results under uncertain conditions and enhance the flexibility and adaptability of the system. The risk assessment module also has self-learning capabilities. It optimizes the risk assessment model by continuously learning new data, reduces human intervention, and improves the accuracy of risk prediction. The data acquisition module is equipped with Internet of Things devices to collect equipment status and operating parameters in real time, and enhance the system's monitoring capabilities for equipment failures and abnormal conditions. The optimization scheduling module uses intelligent optimization technologies such as genetic algorithms and particle swarm optimization algorithms to perform multi-objective scheduling optimization to seek a balance between different goals and improve the overall scheduling effect.

[0039] Cloud computing platform uses distributed computing and storage resources to improve the system's processing power and response speed under high load conditions, ensure the efficiency of real-time data updates and analysis, and support concurrent access by multiple users. The decision support system integrates deep learning technology and can perform pattern recognition based on historical scheduling data and predict future scheduling needs to enhance the foresight of decision-making. The cloud computing platform adopts a microservice architecture and uses containerization technology to achieve elastic scaling of services. Security measures such as identity authentication, access control, and data encryption are used to ensure the security of the system. The adaptive optimization algorithm combines the advantages of genetic algorithms and particle swarm optimization algorithms, uses a variety of swarm intelligence algorithms, dynamically adjusts algorithm parameters, and improves the efficiency and accuracy of the optimization process.

[0040] A risk dispatch optimization method for a power system comprises the following steps:

[0041] a. Use sensors and data interfaces to collect information such as power demand, supply, market price and equipment status in real time, and reduce delays and inaccuracies through data preprocessing functions, and integrate data from third-party market information systems to improve the comprehensiveness and accuracy of risk assessment;

[0042] b. Use dynamic risk assessment models to analyze potential risks and generate risk reports;

[0043] c. Based on the risk assessment results, use the adaptive optimization algorithm to develop a scheduling plan;

[0044] d. With the help of decision support system, the operation status and scheduling plan are displayed in real time, and deep learning technology is used for pattern recognition to predict future scheduling needs, enhance the foresight of decision-making, and implement smart contract management mechanism. Smart contracts are based on blockchain technology, written in Solidity language, and deployed on the Ethereum platform to achieve automatic execution of scheduling rules and non-tampering of results, improve the transparency and trust of the system, automatically execute scheduling rules and generate compliance reports;

[0045] e. Monitor equipment status and operating parameters in real time, detect abnormal situations in a timely manner, and dynamically adjust scheduling strategies based on feedback information to ensure optimized scheduling under market fluctuations and uncertainties.

[0046] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A risk dispatch optimization system for a power system, characterized in that: include: A data acquisition module, which is used to collect power demand, supply, market price, equipment status and other related information in real time. The module realizes efficient data acquisition through multiple sensors, data interfaces and communication protocols, and has a data preprocessing function to reduce the impact of delays and inaccuracies on subsequent modules. The data acquisition module further includes data verification and error detection functions to improve the accuracy and reliability of data and support real-time monitoring and feedback of collected data; The risk assessment module uses a dynamic risk assessment model based on the collected data to analyze and evaluate potential risks, generate risk reports and provide decision-making basis to form a decision support system. The calculation formula of the risk value R is: Among them, w i is the weight of each risk, p i In order to respond to the probability of risk occurrence, the risk assessment module combines an adaptive adjustment mechanism, regularly updates the risk assessment model, uses the fusion technology of historical data and real-time data to enhance the accuracy of risk assessment, and simulates risk situations in different scenarios through scenario analysis functions to help decision makers predict potential risks; The optimization scheduling module uses an adaptive optimization algorithm to formulate a scheduling plan for power generation units based on the risk assessment results. The mathematical model of the comprehensive optimization target is: minZ = C + α·R + β·E; Wherein, Z is the comprehensive target value, C is the power generation cost, R is the risk value, E is the environmental impact, α and β are weight coefficients. The optimization scheduling module has the ability of hierarchical scheduling. Through the multi-objective optimization method, the scheduling strategy can be flexibly adjusted to adapt to the complex power market environment. The optimization scheduling module can use intelligent algorithms to adjust the scheduling strategy according to real-time feedback to cope with market fluctuations; The cloud computing platform utilizes distributed computing and storage resources to improve the system's processing power and response speed under high load conditions, ensure the high efficiency of real-time data updating and analysis, and support concurrent access by multiple users.

2. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The data acquisition module integrates external data sources through an interface with a third-party market information system to improve the comprehensiveness and accuracy of risk assessment, including weather forecasts and power market dynamic information. The optimization scheduling module uses blockchain technology to record important data in the scheduling decision-making process, ensure data security and transparency, enhance the credibility of the system, and ensure the traceability and non-tamperability of historical data.

3. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The decision support system has a management mechanism based on smart contracts, which realizes the automation of rule execution in the power dispatching process, improves dispatching efficiency and compliance, automatically generates compliance reports, and reduces the management burden. The cloud computing platform uses distributed computing and storage resources to improve the system's processing power and response speed under high load conditions, ensures the efficiency of real-time data updating and analysis, and supports concurrent access by multiple users.

4. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The optimization scheduling module combines the dynamic model to form a dynamic scheduling strategy through real-time feedback and adjustment, thereby ensuring the optimal scheduling result under uncertain conditions and enhancing the flexibility and adaptability of the system.

5. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The risk assessment module also has self-learning capabilities, which optimizes the risk assessment model by continuously learning new data, reduces human intervention, and improves the accuracy of risk prediction. The data acquisition module is equipped with Internet of Things devices to collect device status and operating parameters in real time, enhancing the system's monitoring capabilities for equipment failures and abnormal conditions.

6. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The optimization scheduling module uses intelligent optimization technologies such as genetic algorithms and particle swarm optimization algorithms to perform multi-objective scheduling optimization to seek a balance between different objectives and improve the overall scheduling effect.

7. The risk dispatch optimization system for a power system according to claim 1, characterized in that: The decision support system integrates deep learning technology, which can perform pattern recognition based on historical scheduling data and predict future scheduling needs to enhance the forward-looking nature of decision-making.

8. A risk dispatch optimization method for a power system, characterized in that: The following steps are involved: a. Use sensors and data interfaces to collect information such as power demand, supply, market price and equipment status in real time, and reduce delays and inaccuracies through data preprocessing functions, and integrate data from third-party market information systems to improve the comprehensiveness and accuracy of risk assessment; b. Use dynamic risk assessment models to analyze potential risks and generate risk reports; c. Based on the risk assessment results, use the adaptive optimization algorithm to develop a scheduling plan; d. With the help of decision support system, the operation status and scheduling plan are displayed in real time, and deep learning technology is used for pattern recognition to predict future scheduling needs, enhance the foresight of decision-making, implement smart contract management mechanism, automatically execute scheduling rules and generate compliance reports; e. Monitor equipment status and operating parameters in real time, detect abnormal situations in a timely manner, and dynamically adjust scheduling strategies based on feedback information to ensure optimized scheduling under market fluctuations and uncertainties.

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