Intelligent optimization scheduling method of informatization energy management system

Abstract

The invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to an intelligent optimization dispatching method for an information-based energy management system. Background Art

[0002] In the field of modern energy management, with the rapid growth of energy demand and the complexity of energy supply structure, traditional energy management systems face many challenges. In existing technologies, energy scheduling usually relies on centralized control strategies. This method may achieve certain results in a single energy system, but in the case of coexistence of multiple energy types and widespread distribution of distributed energy resources, centralized scheduling is difficult to cope with the complexity and dynamic changes of the system.

[0003] Traditional energy scheduling methods are usually based on local optimization or single-objective optimization, and it is difficult to consider multiple objectives such as cost, carbon emissions, equipment utilization, and user comfort at the same time. Therefore, it is often impossible to achieve the optimal scheduling of the entire system. Especially in distributed energy management systems, each energy unit is highly independent, and the lack of global coordination capabilities can easily lead to waste of resources, scheduling conflicts, and reduced system efficiency.

[0004] Existing energy management systems mostly focus on optimizing the energy supply side, while neglecting the optimization of energy usage behavior on the user side, which leads to rigid and inefficient energy consumption strategies.

[0005] Based on the above-mentioned deficiencies of the prior art, the present invention provides an intelligent optimization scheduling method for an information-based energy management system. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an intelligent optimization scheduling method for an information-based energy management system, which solves the problem that traditional energy scheduling methods are usually based on local optimization or single-objective optimization, and it is difficult to simultaneously consider multiple objectives such as cost, carbon emissions, equipment utilization and user comfort.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent optimization scheduling method for an information-based energy management system, comprising the following steps:

[0008] Data collection and fusion: Through distributed IoT devices and multimodal sensor networks, real-time data from each node in the energy system is collected, and the collected data is fused and processed to generate high-quality data sets in a unified format;

[0009] Predictive modeling and demand response: Based on user behavior analysis and machine learning algorithms, establish a demand response model and build a multi-level energy demand and supply forecasting model covering short-term, medium-term and long-term time dimensions;

[0010] Multi-objective optimization and scheduling: Adopting adaptive multi-objective optimization algorithm, we can generate optimized energy scheduling schemes by considering multiple objectives such as cost, carbon emission, equipment utilization and user comfort.

[0011] Dynamic control and resource allocation: Dynamically adjust the allocation ratio of each energy type based on real-time monitoring data and the output of the prediction model, and manage energy equipment in real time through a flexible control mechanism;

[0012] Fault diagnosis and self-healing: Through deep learning and fault tree analysis methods, potential faults in the energy system are monitored in real time, and the self-healing mechanism is automatically triggered to ensure stable operation of the system.

[0013] Preferably, the data collection and fusion step includes:

[0014] Environmental data, including temperature, humidity, light intensity and wind speed, are collected through a multimodal sensor network, and these data are analyzed in temporal and spatial correlation with energy consumption data to improve the accuracy of data processing.

[0015] Preferably, the predictive modeling and demand response steps include:

[0016] By analyzing users’ historical energy usage behaviors and real-time user status data, future fluctuations in users’ energy demand are predicted, and these predictions are combined to make real-time adjustments in scheduling.

[0017] Preferably, the multi-objective optimization and scheduling step includes: using an adaptive genetic algorithm to dynamically adjust the parameters of the optimization algorithm during the energy scheduling process to simultaneously optimize the cost, carbon emissions and equipment utilization rate; during the energy scheduling process, using an adaptive genetic algorithm to optimize the cost, carbon emissions and equipment utilization rate through the following objective function:

[0018] min f(x)=w1·C(x)+w2·E(x)+w3·U(x)

[0019] in:

[0020] ·x is the scheduling decision variable; ·C(x) represents the total cost of the scheduling solution, ·E(x) represents the carbon emissions of the scheduling solution; ·U(x) represents the equipment utilization rate; ·w1,w2,w3 are the weights of cost, carbon emissions and equipment utilization rate, satisfying w1+w2+w3=1. During the iteration process, the adaptive genetic algorithm dynamically adjusts the algorithm parameters according to the convergence of the objective function, including the crossover probability P c and mutation probability P m , in order to improve the optimization efficiency.

[0021] Preferably, the dynamic control and resource allocation step includes: using a model predictive control (MPC) method to dynamically adjust the allocation ratio of each energy type according to real-time supply and demand changes to ensure efficient use of resources, using a model predictive control method to dynamically adjust the allocation ratio of each energy type, and defining the state equation of the system as:

[0022] x(k+1)=Ax(k)+Bu(k)

[0023] in:

[0024] ·x(k) is the system state variable at time k, including the current allocation ratio of each energy type; ·u(k) is the control input variable at time k, that is, the adjustment amount of each energy type; ·A and B are the state transfer matrix and control input matrix of the system.

[0025] By predicting the system state in the next N steps, solve the following optimization problem:

[0026]

[0027] in:

[0028] ·x ref (i) is the reference state variable;

[0029] Q and R are the weight matrices of state error and control input respectively. The optimal control input u is obtained by optimization. * (k) and use it to dynamically adjust the allocation ratio of each energy type to ensure efficient use of resources.

[0030] Preferably, the elastic control mechanism includes:

[0031] During peak energy demand periods, backup energy equipment is automatically enabled and the system balance is maintained by adjusting the working status of non-critical loads.

[0032] Preferably, the fault diagnosis and self-healing steps include:

[0033] A deep learning model is used to perform pattern recognition on the equipment operation data of the energy system, thereby providing early warning of possible failures.

[0034] Preferably, the self-healing mechanism includes:

[0035] Automatically switch to backup energy equipment, record fault information and notify maintenance personnel for follow-up processing to ensure continuous operation of the system.

[0036] Preferably, the multi-objective optimization and scheduling step further includes:

[0037] Collaborative optimization and distributed decision-making: In the distributed energy management system, collaborative optimization algorithms are used to optimize the scheduling decisions of each distributed energy unit to achieve the global optimal energy scheduling solution.

[0038] Preferably, the scheduling method also includes: intelligent interaction on the user side: generating personalized energy usage recommendations through an interactive system between the smart terminal and the user side, and optimizing the user's energy consumption strategy by adjusting the operating time and power settings of the user's home appliances in real time.

[0039] The present invention provides an intelligent optimization scheduling method for an information-based energy management system. It has the following beneficial effects:

[0040] 1. The present invention combines a collaborative optimization algorithm with a distributed decision-making mechanism. On the basis of local optimization of each distributed energy unit, the present invention further realizes a globally optimal energy scheduling solution, ensuring that in a complex energy network, each energy unit can achieve a high degree of coordination and optimal allocation of resources, thereby improving the energy utilization efficiency and operational stability of the overall system, and avoiding resource waste and system bottleneck problems that may occur in traditional centralized scheduling.

[0041] 2. The present invention not only realizes efficient optimization and scheduling on the energy supply side, but also generates personalized energy usage recommendations through the interactive system between the intelligent terminal and the user side, and adjusts the operation strategy of the user's home appliances in real time, so that the user can deeply participate in energy management and optimize their personal energy consumption behavior. This user-side intelligent interaction mechanism significantly improves the flexibility and response speed of the energy system, realizes full-chain intelligent management from energy supply to final use, and effectively promotes the economy and sustainability of energy utilization.

[0042] 3. The present invention uses a deep learning model to perform pattern recognition on the equipment operation data of the energy system. The present invention can provide early warning of possible failures, automatically trigger a self-healing mechanism to switch to backup energy equipment, and notify maintenance personnel to handle it, thereby greatly reducing the risk of system interruption due to equipment failure, significantly improving the overall reliability and continuous operation capability of the energy management system, and providing solid technical support for the stable operation of complex energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the present invention DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the specification 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.

[0045] Please see attached Figure 1 The embodiment of the present invention provides an intelligent optimization scheduling method for an information-based energy management system, comprising the following steps:

[0046] Data collection and fusion: Through distributed IoT devices and multimodal sensor networks, real-time data from each node in the energy system is collected, and the collected data is fused and processed to generate high-quality data sets in a unified format;

[0047] Predictive modeling and demand response: Based on user behavior analysis and machine learning algorithms, establish a demand response model and build a multi-level energy demand and supply forecasting model covering short-term, medium-term and long-term time dimensions;

[0048] Multi-objective optimization and scheduling: Adopting adaptive multi-objective optimization algorithm, we can generate optimized energy scheduling schemes by considering multiple objectives such as cost, carbon emission, equipment utilization and user comfort.

[0049] Dynamic control and resource allocation: Dynamically adjust the allocation ratio of each energy type based on real-time monitoring data and the output of the prediction model, and manage energy equipment in real time through a flexible control mechanism;

[0050] Fault diagnosis and self-healing: Through deep learning and fault tree analysis methods, potential faults in the energy system are monitored in real time, and the self-healing mechanism is automatically triggered to ensure stable operation of the system.

[0051] Specifically, first, through distributed Internet of Things devices and multimodal sensor networks, real-time data of each node in the energy system is comprehensively collected, and combined with advanced data fusion and processing technologies, multi-source heterogeneous data are deeply integrated and standardized, so as to generate a high-quality data set in a unified format to support subsequent modeling and optimization; then, based on user behavior analysis and cutting-edge machine learning algorithms, an accurate demand response model is established, and a multi-level energy demand and supply forecasting model with short-term, medium-term and long-term time dimensions is constructed to accurately capture the volatility and seasonal characteristics of energy demand, and provide a basis for optimized scheduling; on this basis, an adaptive multi-objective optimization algorithm is used to comprehensively consider multiple key objectives such as energy cost, carbon emissions, equipment utilization and user comfort, and generate a balanced demand response model. An economical and environmentally friendly optimized energy scheduling scheme, in which the adaptive optimization strategy can flexibly adjust the optimization parameters according to the dynamic changes of the system, improve the robustness and execution effect of the scheduling scheme; at the same time, based on the real-time monitoring data and the output of the prediction model, the allocation ratio of various energy resources is dynamically adjusted, and the model predictive control (MPC) method is used to achieve accurate resource scheduling, and various energy equipment are managed in real time through a flexible control mechanism to ensure the stable operation of the system under load fluctuations and external interference; finally, through deep learning and fault tree analysis methods, the operating status of the energy system is monitored in real time and pattern recognition is carried out, potential equipment failures are warned in advance, and the self-healing mechanism is automatically triggered, including switching backup energy equipment and notifying maintenance personnel, to ensure the continuous and stable operation of the system.

[0052] The data collection and fusion steps include:

[0053] Environmental data, including temperature, humidity, light intensity and wind speed, are collected through a multimodal sensor network, and these data are analyzed in temporal and spatial correlation with energy consumption data to improve the accuracy of data processing.

[0054] The predictive modeling and demand response steps include:

[0055] By analyzing users’ historical energy usage behaviors and real-time user status data, future fluctuations in users’ energy demand are predicted, and these predictions are combined to make real-time adjustments in scheduling.

[0056] The multi-objective optimization and scheduling steps include: using an adaptive genetic algorithm to dynamically adjust the parameters of the optimization algorithm during the energy scheduling process to simultaneously optimize the cost, carbon emissions and equipment utilization rate. During the energy scheduling process, the adaptive genetic algorithm is used to optimize the cost, carbon emissions and equipment utilization rate through the following objective functions:

[0057] min f(x)=w1·C(x)+w2·E(x)+w3·U(x)

[0058] in:

[0059] ·x is the scheduling decision variable; ·C(x) represents the total cost of the scheduling solution, ·E(x) represents the carbon emissions of the scheduling solution; ·U(x) represents the equipment utilization rate; ·w1,w2,w3 are the weights of cost, carbon emissions and equipment utilization rate, satisfying w1+w2+w3=1. During the iteration process, the adaptive genetic algorithm dynamically adjusts the algorithm parameters according to the convergence of the objective function, including the crossover probability P c and mutation probability P m , in order to improve the optimization efficiency.

[0060] Specifically, in the data collection and fusion step, a multimodal sensor network is used to comprehensively collect environmental data including temperature, humidity, light intensity and wind speed, and a deep spatiotemporal correlation analysis is performed on it with energy consumption data to improve the accuracy and reliability of data processing and provide a solid data foundation for subsequent modeling. In the predictive modeling and demand response step, by analyzing the user's historical energy usage behavior and real-time user status data in detail, advanced machine learning algorithms are used to accurately predict the user's future energy demand fluctuations, and these prediction results are applied in real time to the scheduling process to dynamically adjust the energy allocation strategy. In the multi-objective optimization and scheduling step, , an adaptive genetic algorithm is used to comprehensively optimize the cost, carbon emissions and equipment utilization in the energy scheduling process through the following objective function, where is set as the scheduling decision variable, represents the total cost of the scheduling plan, represents the carbon emissions of the scheduling plan, represents the equipment utilization, and is the weight of the cost, carbon emissions and equipment utilization, satisfying the constraint that the total weight is 1. During the iteration process, the adaptive genetic algorithm dynamically adjusts the algorithm parameters, including the crossover probability and the mutation probability, according to the convergence of the objective function, so as to significantly improve the optimization efficiency and ensure that the scheduling plan achieves the best balance among cost-effectiveness, carbon emission control and equipment utilization.

[0061] According to the intelligent optimization scheduling method of the information-based energy management system of claim 1, it is characterized in that the dynamic control and resource allocation step includes: using the model predictive control (MPC) method to dynamically adjust the allocation ratio of each energy type according to real-time supply and demand changes to ensure efficient utilization of resources, using the model predictive control method to dynamically adjust the allocation ratio of each energy type, and defining the state equation of the system as:

[0062] x(k+1)=Ax(k)+Bu(k)

[0063] in:

[0064] ·x(k) is the system state variable at time k, including the current allocation ratio of each energy type; ·u(k) is the control input variable at time k, that is, the adjustment amount of each energy type; ·A and B are the state transfer matrix and control input matrix of the system.

[0065] By predicting the system state in the next N steps, solve the following optimization problem:

[0066]

[0067] in:

[0068] ·x ref (i) is the reference state variable;

[0069] Q and R are the weight matrices of state error and control input respectively. The optimal control input u is obtained by optimization. * (k) and use it to dynamically adjust the allocation ratio of each energy type to ensure efficient use of resources.

[0070] Specifically, the dynamic control and resource allocation steps adopt the model predictive control (MPC) method to dynamically adjust the allocation ratio of various energy types according to real-time changes in supply and demand to ensure efficient use of resources, and define the state equation of the system through the model predictive control method, where is set as the system state variable at the moment, including the current allocation ratio of each energy type, is set as the control input variable at the moment, represents the adjustment amount of each energy type, and and are the state transfer matrix and control input matrix of the system, respectively. By predicting the system state in the future step, the following optimization problem is solved, that is, minimizing the weighted sum of state error and control input, where represents the reference state variable, and are the weight matrices of state error and control input, respectively. By solving the optimization problem, the optimal control input can be obtained and applied to dynamically adjust the allocation ratio of various energy types, thereby realizing efficient utilization and scheduling optimization of energy resources under different load conditions, and ensuring that the overall operation efficiency and stability of the system are maximized.

[0071] The elastic control mechanism includes:

[0072] During peak energy demand periods, backup energy equipment is automatically enabled and the system balance is maintained by adjusting the working status of non-critical loads.

[0073] The fault diagnosis and self-healing steps include:

[0074] A deep learning model is used to perform pattern recognition on the equipment operation data of the energy system, thereby providing early warning of possible failures.

[0075] The self-healing mechanism includes:

[0076] Automatically switch to backup energy equipment, record fault information and notify maintenance personnel for follow-up processing to ensure continuous operation of the system.

[0077] Specifically, the elastic control mechanism automatically enables backup energy equipment during peak energy demand periods, combined with flexible adjustments to the working status of non-critical loads, to effectively maintain the overall balance of the system, thereby ensuring that users' energy needs can still be met when resources are tight; in the fault diagnosis and self-healing steps, the system uses deep learning models to perform high-precision pattern recognition on the equipment operation data of the energy system, which can provide early warning of possible equipment failures. With the help of the self-healing mechanism, the system automatically switches to backup energy equipment to ensure continuous energy supply. At the same time, the system will record fault information in real time and automatically notify maintenance personnel for subsequent processing, ultimately achieving continuous and stable operation of the system. Even in the event of a fault, the impact on the overall energy supply can be minimized, ensuring the high reliability and availability of the system.

[0078] The multi-objective optimization and scheduling step also includes:

[0079] Collaborative optimization and distributed decision-making: In the distributed energy management system, collaborative optimization algorithms are used to optimize the scheduling decisions of each distributed energy unit to achieve the global optimal energy scheduling solution.

[0080] The scheduling method also includes: intelligent interaction on the user side: generating personalized energy usage recommendations through an interactive system between the smart terminal and the user side, and optimizing the user's energy consumption strategy by adjusting the operating time and power settings of the user's home appliances in real time.

[0081] Specifically, the multi-objective optimization and scheduling steps further introduce the mechanism of collaborative optimization and distributed decision-making. In the distributed energy management system, the scheduling decisions of each distributed energy unit are comprehensively optimized through the collaborative optimization algorithm, so as to achieve the global optimal energy scheduling plan. This plan not only takes into account the optimization of multiple objectives such as cost, carbon emissions and equipment utilization, but also ensures that each energy unit achieves global coordination while optimizing locally through a distributed decision-making mechanism, thereby improving the energy utilization efficiency of the entire system; in addition, the scheduling method also includes a user-side intelligent interaction function, which uses the interaction system between the smart terminal and the user side to generate personalized energy usage recommendations, and optimizes the energy consumption strategy from the user level by adjusting the operating time and power settings of the user's home appliances in real time, so that energy management not only stays at the optimization of the supply side, but also goes deep into the intelligent adjustment of the user side, realizing the energy optimization and scheduling of the entire process, thereby further improving the overall operation efficiency of the system and user satisfaction, and ensuring the dual goals of economic benefits and user comfort in a complex and changeable energy demand environment.

[0082] 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. An intelligent optimization scheduling method for an information-based energy management system, characterized in that: The following steps are involved: Data collection and fusion: Through distributed IoT devices and multimodal sensor networks, real-time data from each node in the energy system is collected, and the collected data is fused and processed to generate high-quality data sets in a unified format; Predictive modeling and demand response: Based on user behavior analysis and machine learning algorithms, establish a demand response model and build a multi-level energy demand and supply forecasting model covering short-term, medium-term and long-term time dimensions; Multi-objective optimization and scheduling: Adopting adaptive multi-objective optimization algorithm, we can generate optimized energy scheduling schemes by considering multiple objectives such as cost, carbon emission, equipment utilization and user comfort. Dynamic control and resource allocation: Dynamically adjust the allocation ratio of each energy type based on real-time monitoring data and the output of the prediction model, and manage energy equipment in real time through a flexible control mechanism; Fault diagnosis and self-healing: Through deep learning and fault tree analysis methods, potential faults in the energy system are monitored in real time, and the self-healing mechanism is automatically triggered to ensure stable operation of the system.

2. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The data collection and fusion steps include: Environmental data, including temperature, humidity, light intensity and wind speed, are collected through a multimodal sensor network, and these data are analyzed in temporal and spatial correlation with energy consumption data to improve the accuracy of data processing.

3. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The predictive modeling and demand response steps include: By analyzing users’ historical energy usage behaviors and real-time user status data, future fluctuations in users’ energy demand are predicted, and these predictions are combined to make real-time adjustments in scheduling.

4. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The multi-objective optimization and scheduling steps include: using an adaptive genetic algorithm to dynamically adjust the parameters of the optimization algorithm during the energy scheduling process to simultaneously optimize the cost, carbon emissions and equipment utilization rate. During the energy scheduling process, the adaptive genetic algorithm is used to optimize the cost, carbon emissions and equipment utilization rate through the following objective functions: min f(x)=w1·C(x)+w2·E(x)+w3·U(x) in: ·x is the scheduling decision variable; ·C(x) represents the total cost of the scheduling solution, ·E(x) represents the carbon emissions of the scheduling solution; ·U(x) represents the equipment utilization rate; ·w1,w2,w3 are the weights of cost, carbon emissions and equipment utilization rate, satisfying w1+w2+w3=1. During the iteration process, the adaptive genetic algorithm dynamically adjusts the algorithm parameters according to the convergence of the objective function, including the crossover probability P c and mutation probability P m , in order to improve the optimization efficiency.

5. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The dynamic control and resource allocation step includes: using a model predictive control (MPC) method to dynamically adjust the allocation ratio of each energy type according to real-time supply and demand changes to ensure efficient use of resources, using a model predictive control method to dynamically adjust the allocation ratio of each energy type, and defining the state equation of the system as: x(k+1)=Ax(k)+Bu(k) in: ·x(k) is the system state variable at time k, including the current allocation ratio of each energy type; ·u(k) is the control input variable at time k, that is, the adjustment amount of each energy type; ·A and B are the state transfer matrix and control input matrix of the system. By predicting the system state in the next N steps, solve the following optimization problem: in: ·x ref (i) is the reference state variable; Q and R are the weight matrices of state error and control input respectively. The optimal control input u is obtained by optimization. * (k) and use it to dynamically adjust the allocation ratio of each energy type to ensure efficient use of resources.

6. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The elastic control mechanism includes: During peak energy demand periods, backup energy equipment is automatically enabled and the system balance is maintained by adjusting the working status of non-critical loads.

7. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The fault diagnosis and self-healing steps include: A deep learning model is used to perform pattern recognition on the equipment operation data of the energy system, thereby providing early warning of possible failures.

8. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The self-healing mechanism includes: Automatically switch to backup energy equipment, record fault information and notify maintenance personnel for follow-up processing to ensure continuous operation of the system.

9. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The multi-objective optimization and scheduling step also includes: Collaborative optimization and distributed decision-making: In the distributed energy management system, collaborative optimization algorithms are used to optimize the scheduling decisions of each distributed energy unit to achieve the global optimal energy scheduling solution.

10. The intelligent optimization scheduling method of the information-based energy management system according to claim 1 is characterized in that: The scheduling method also includes: intelligent interaction on the user side: generating personalized energy usage recommendations through an interactive system between the smart terminal and the user side, and optimizing the user's energy consumption strategy by adjusting the operating time and power settings of the user's home appliances in real time.

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