Elastic evaluation method for integrated energy distribution network

Abstract

The invention discloses an elastic evaluation method for a comprehensive energy distribution network in the field of power systems and energy management, and the method comprises the following steps: S10, constructing a dynamic coupling model and a comprehensive evaluation index system of the comprehensive energy distribution network, the dynamic coupling model of the comprehensive energy distribution network comprises energy flow equations and equipment operation constraints of a power network, a natural gas network and a thermal network, and the comprehensive evaluation index system comprises an energy coupling index, a recovery capability index and an uncertainty index. According to the invention, through multi-energy coupling and uncertainty comprehensive evaluation, the coupling relationship of multiple energies and various uncertainty factors are comprehensively incorporated into an elastic evaluation system of the comprehensive energy distribution network, a set of complete comprehensive evaluation index system is established, and the limitation that a traditional evaluation method only pays attention to single energy or ignores uncertainty is broken through; and the actual elasticity condition of the system can be reflected more truly.

Description

Technical Field

[0001] The present invention relates to the fields of power systems and energy management, and in particular to a method for elasticity assessment of an integrated energy distribution network. Background Art

[0002] With the profound adjustment of energy structures and the widespread development of integrated energy systems, integrated energy distribution networks have become increasingly complex, integrating multiple energy sources such as electricity, heat, and natural gas. Traditional distribution network assessment methods are no longer sufficient to meet these needs.

[0003] Most existing assessment methods analyze only a single energy network, ignoring the coupling relationships between multiple energy sources. In an integrated energy distribution network, energy sources such as electricity, heat, and natural gas are interconnected. For example, an electric heat pump can convert electricity into heat, and a gas turbine can generate both electricity and heat simultaneously. However, existing methods fail to fully account for these coupling characteristics, resulting in assessment results that fail to truly reflect the overall resilience of the system. Some assessment methods use overly simplistic indicators, focusing only on traditional metrics such as outage duration and energy loss, and are unable to comprehensively measure the resilience of the integrated energy distribution network. The resilience of an integrated energy distribution network is not only reflected in the recovery time and energy loss after a fault, but also involves multiple aspects such as the stability of energy supply, the ability of different energy sources to coordinate recovery, and the ability to cope with various uncertainties.

[0004] Existing computational methods often suffer from high computational complexity and low efficiency. Integrated energy distribution networks involve a large number of devices and complex energy conversion processes. Traditional computational methods consume significant time and computing resources to process this complex data, making it difficult to meet the real-time assessment and rapid decision-making requirements of practical projects. Furthermore, existing assessment methods fail to adequately account for uncertainties inherent in integrated energy distribution networks, such as the intermittent nature of renewable energy generation, random fluctuations in load demand, and volatile energy market prices. These uncertainties significantly impact the resilience of integrated energy distribution networks, yet most existing assessment methods fail to incorporate them into their systems, resulting in discrepancies between the assessment results and actual conditions.

[0005] Therefore, those skilled in the art provide a method for elasticity assessment of an integrated energy distribution network to solve the problems raised in the above background technology. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for elasticity assessment of an integrated energy distribution network to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for elasticity assessment of an integrated energy distribution network comprises the following steps: Step S10: Constructing a dynamic coupling model and a comprehensive evaluation index system for the integrated energy distribution network. The dynamic coupling model of the integrated energy distribution network includes energy flow equations and equipment operation constraints for the power network, natural gas network, and thermal network. The comprehensive evaluation index system includes energy coupling indicators, resilience indicators, and uncertainty indicators. Step S20: Establish an uncertainty assessment model by combining Monte Carlo simulation with scenario analysis and applying fuzzy logic to process fuzzy information. Based on historical disaster data and real-time monitoring data, a set of multiple disturbance scenarios is generated. The disturbance scenarios include extreme weather, equipment failure, and load mutation. Step S30: For each disturbance scenario in step S20, calculate the resilience indicators of the distribution network, including system recovery time, energy loss rate, and critical load protection rate; Step S40: Generate a multi-level recovery strategy set based on the elasticity indicator priority, and optimize the strategy execution order through a dynamic game algorithm; Step S50: Based on the optimized recovery strategy, output a resilience assessment report and weak link improvement suggestions and design assessment process. The assessment process covers data collection and preprocessing, scenario generation and simulation calculation, indicator calculation and evaluation, and result analysis and decision support.

[0008] As a further solution of the present invention, the dynamic coupling model in step S10 is established in the following manner, specifically comprising the following steps: S11. The power network is modeled using the improved DistFlow equation and embedded with the output constraints of distributed energy resources; S12, the natural gas network is modeled using the Weymouth equation and coupled with the gas-to-electricity conversion efficiency of the gas turbine; S13. The thermal network is modeled using thermodynamic equations and integrates multi-energy conversion constraints of heat pumps and heat storage tanks.

[0009] As a further solution of the present invention: the energy coupling indicators in step S10 include power-heat coupling, power-natural gas coupling, and heat-natural gas coupling, which are quantified by calculating the energy transmission ratio between different energy networks. Among the recovery capability indicators, the recovery speed is measured by calculating the time it takes for the system to recover to more than 80% of its normal operating state from the occurrence of a fault. The recovery quality is evaluated based on the power quality, heat supply stability, and natural gas supply reliability.

[0010] As a further solution of the present invention: the uncertainty indicator in step S20 uses a probability density function to describe the uncertainty of renewable energy power generation and load demand, evaluates the impact by calculating the expectation and variance, and establishes a price fluctuation coefficient indicator to consider the uncertainty of energy market price fluctuations. The fuzzy logic fuzzifies fuzzy information such as the degree of equipment aging and external environmental impact, establishes a fuzzy rule base, and obtains evaluation results through fuzzy reasoning. The method for generating a disturbance scenario set further includes: S21. Generate probability distribution of equipment failures under extreme weather conditions through Monte Carlo simulation; S22, Time series characteristics of load mutation prediction based on LSTM neural network; S23. Perform spatiotemporal clustering of historical disaster data to build a typical disaster scenario database.

[0011] As a further solution of the present invention: the calculation of the elasticity index in step S30 includes: S31, System recovery time: The time threshold from the occurrence of the disturbance to the restoration of power / gas / heat to the critical load; S32, Energy loss rate: the proportion of unmet energy demand during the disturbance to total demand; S33. Critical load guarantee rate: the continuous energy supply coverage rate of critical loads such as hospitals and transportation hubs.

[0012] As a further solution of the present invention: the method for generating the multi-level recovery strategy set in step S40 includes: a first-level strategy, a second-level strategy and a third-level strategy; the first-level strategy gives priority to repairing trunk lines and energy hub equipment; the second-level strategy activates the black start capability of distributed energy storage and backup generator sets; the third-level strategy adjusts the energy supply priority of non-critical loads through demand response.

[0013] As a further solution of the present invention: the optimization objectives of the dynamic game algorithm using the Nash equilibrium model in step S40 include minimizing the weighted sum of system recovery time and energy loss rate and maximizing the critical load guarantee rate and equipment utilization rate.

[0014] As a further solution of the present invention, the elasticity assessment report in step S50 includes: S51. Visualization map of weak links based on geographic information system; S52. Improvement plan for equipment redundancy and network topology; S53. Early warning information on resilience levels at different disaster levels.

[0015] As a further solution of the present invention: the data acquisition and preprocessing in step S50 collects the topological structure, equipment parameters, energy supply data, load demand data, and energy market price data of the integrated energy distribution network, and performs cleaning, denoising and standardization processing. The result analysis and decision support analyzes the evaluation results, identifies the system's weak links and potential risks, and provides decision support for power grid planning, operation and maintenance, including optimizing equipment configuration, adjusting energy supply strategies, and formulating emergency plans.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Through comprehensive evaluation of multi-energy coupling and uncertainty, the present invention comprehensively incorporates the coupling relationship of multiple energy sources and various uncertainty factors into the elasticity evaluation system of the integrated energy distribution network, and establishes a complete set of comprehensive evaluation index systems. This breaks through the limitations of traditional evaluation methods that only focus on a single energy source or ignore uncertainty, and more realistically reflects the actual elasticity of the system.

[0017] 2. This invention introduces fuzzy logic to process fuzzy information that is difficult to accurately quantify in the integrated energy distribution network, making the evaluation model closer to the actual situation, improving the accuracy and reliability of the evaluation results, and combining real-time monitoring data to achieve a "prediction-evaluation-optimization" closed-loop feedback; 3. The present invention transforms the restoration strategy priority problem into a multi-objective game model to improve decision-making efficiency and operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a step diagram of the present invention; DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 In an embodiment of the present invention, a method for elasticity assessment of an integrated energy distribution network includes the following steps: Step S10: Constructing a dynamic coupling model and a comprehensive evaluation index system for the integrated energy distribution network. The dynamic coupling model of the integrated energy distribution network includes energy flow equations and equipment operation constraints for the power network, natural gas network, and thermal network. The comprehensive evaluation index system includes energy coupling indicators, resilience indicators, and uncertainty indicators. Step S20: Establish an uncertainty assessment model by combining Monte Carlo simulation with scenario analysis and applying fuzzy logic to process fuzzy information. Based on historical disaster data and real-time monitoring data, a set of multiple disturbance scenarios is generated. The disturbance scenarios include extreme weather, equipment failure, and load mutation. Step S30: For each disturbance scenario in step S20, calculate the resilience indicators of the distribution network, including system recovery time, energy loss rate, and critical load protection rate; Step S40: Generate a multi-level recovery strategy set based on the elasticity indicator priority, and optimize the strategy execution order through a dynamic game algorithm; Step S50: Based on the optimized recovery strategy, output a resilience assessment report and weak link improvement suggestions and design assessment process. The assessment process covers data collection and preprocessing, scenario generation and simulation calculation, indicator calculation and evaluation, and result analysis and decision support.

[0021] By adopting the above technical solutions and building a comprehensive evaluation index system that fully considers the coupling relationship of multiple energy sources, uncertainty factors, and recovery capabilities, it is possible to more comprehensively and accurately evaluate the resilience level of the integrated energy distribution network, providing a more scientific basis for the management and optimization of the power grid. Based on the evaluation results, the planning and operation strategies of the integrated energy distribution network can be optimized in a targeted manner, such as rationally configuring equipment, optimizing the energy supply structure, and formulating effective emergency plans, thereby improving the resilience and reliability of the power grid, reducing operating costs, and ensuring a stable supply of energy.

[0022] The dynamic coupling model in step S10 is established in the following manner, specifically including the following steps: S11. The power network is modeled using the improved DistFlow equation and embedded with the output constraints of distributed energy resources; S12, the natural gas network is modeled using the Weymouth equation and coupled with the gas-to-electricity conversion efficiency of the gas turbine; S13. The thermal network is modeled using thermodynamic equations and integrates multi-energy conversion constraints of heat pumps and heat storage tanks.

[0023] Among them, the energy coupling indicators in step S10 include electricity-heat coupling, electricity-natural gas coupling, and heat-natural gas coupling, which are quantified by calculating the energy transmission ratio between different energy networks. Among the recovery capacity indicators, the recovery speed is measured by calculating the time from the occurrence of the fault to the recovery of the system to more than 80% of the normal operating state. The recovery quality is evaluated from the aspects of power quality, heat supply stability, and natural gas supply reliability. Through the comprehensive evaluation of multi-energy coupling and uncertainty, the coupling relationship of multiple energy sources and various uncertainty factors are fully incorporated into the elasticity evaluation system of the integrated energy distribution network, and a complete set of comprehensive evaluation indicator system is established, breaking through the limitations of traditional evaluation methods that only focus on a single energy or ignore uncertainty, and more realistically reflecting the actual elasticity of the system.

[0024] In step S20, the uncertainty index uses a probability density function to describe the uncertainty of renewable energy power generation and load demand, evaluates the impact by calculating the expectation and variance, and establishes a price fluctuation coefficient index to consider the uncertainty of energy market price fluctuations. The fuzzy logic fuzzifies fuzzy information such as equipment aging and external environmental impact, establishes a fuzzy rule base, and obtains evaluation results through fuzzy reasoning. The method for generating a disturbance scenario set further includes: S21. Generate probability distribution of equipment failures under extreme weather conditions through Monte Carlo simulation; S22, Time series characteristics of load mutation prediction based on LSTM neural network; S23. Perform spatiotemporal clustering of historical disaster data to build a typical disaster scenario database.

[0025] By adopting the above technical solution, fuzzy logic is introduced to process fuzzy information that is difficult to accurately quantify in the integrated energy distribution network, making the evaluation model closer to the actual situation, improving the accuracy and reliability of the evaluation results, and combining with real-time monitoring data to achieve a "prediction-evaluation-optimization" closed-loop feedback.

[0026] The calculation of the elasticity index in step S30 includes: S31, System recovery time: The time threshold from the occurrence of the disturbance to the restoration of power / gas / heat to the critical load; S32, Energy loss rate: the proportion of unmet energy demand during the disturbance to total demand; S33. Critical load guarantee rate: the continuous energy supply coverage rate of critical loads such as hospitals and transportation hubs.

[0027] Among them, the method for generating the multi-level recovery strategy set in step S40 includes: a first-level strategy, a second-level strategy and a third-level strategy; the first-level strategy prioritizes repairing trunk lines and energy hub equipment; the second-level strategy activates the black start capability of distributed energy storage and backup generator sets; the third-level strategy adjusts the energy supply priority of non-critical loads through demand response.

[0028] The optimization objectives of the Nash equilibrium model adopted by the dynamic game algorithm in step S40 include minimizing the weighted sum of system recovery time and energy loss rate and maximizing the critical load guarantee rate and equipment utilization rate.

[0029] The elasticity assessment report in step S50 includes: S51. Visualization map of weak links based on geographic information system; S52. Improvement plan for equipment redundancy and network topology; S53. Early warning information on resilience levels at different disaster levels.

[0030] Among them, the data acquisition and preprocessing in step S50 collects the topological structure, equipment parameters, energy supply data, load demand data, and energy market price data of the integrated energy distribution network, and performs cleaning, denoising and standardization processing. The result analysis and decision support analyzes the evaluation results, identifies the system's weak links and potential risks, and provides decision support for power grid planning, operation and maintenance, including optimizing equipment configuration, adjusting energy supply strategies, and formulating emergency plans. The restoration strategy priority problem is converted into a multi-objective game model to improve decision-making efficiency and operability.

[0031] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for elasticity assessment of an integrated energy distribution network, characterized by: The steps include: Step S10: Constructing a dynamic coupling model and a comprehensive evaluation index system for the integrated energy distribution network. The dynamic coupling model of the integrated energy distribution network includes energy flow equations and equipment operation constraints for the power network, natural gas network, and thermal network. The comprehensive evaluation index system includes energy coupling indicators, resilience indicators, and uncertainty indicators. Step S20: Establish an uncertainty assessment model by combining Monte Carlo simulation with scenario analysis and applying fuzzy logic to process fuzzy information. Based on historical disaster data and real-time monitoring data, a set of multiple disturbance scenarios is generated. The disturbance scenarios include extreme weather, equipment failure, and load mutation. Step S30: For each disturbance scenario in step S20, calculate the resilience indicators of the distribution network, including system recovery time, energy loss rate, and critical load protection rate; Step S40: Generate a multi-level recovery strategy set based on the elasticity indicator priority, and optimize the strategy execution order through a dynamic game algorithm; Step S50: Based on the optimized recovery strategy, output a resilience assessment report and weak link improvement suggestions and design assessment process. The assessment process covers data collection and preprocessing, scenario generation and simulation calculation, indicator calculation and evaluation, and result analysis and decision support.

2. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: The dynamic coupling model in step S10 is established in the following manner, specifically including the following steps: S11. The power network is modeled using the improved DistFlow equation and embedded with the output constraints of distributed energy resources; S12, the natural gas network is modeled using the Weymouth equation and coupled with the gas-to-electricity conversion efficiency of the gas turbine; S13. The thermal network is modeled using thermodynamic equations and integrates multi-energy conversion constraints of heat pumps and heat storage tanks.

3. The elasticity assessment method for an integrated energy distribution network according to claim 1, characterized in that: The energy coupling indicators in step S10 include power-heat coupling, power-natural gas coupling, and heat-natural gas coupling, which are quantified by calculating the energy transmission ratio between different energy networks. Among the recovery capability indicators, the recovery speed is measured by calculating the time it takes for the system to recover to more than 80% of its normal operating state from the occurrence of a fault. The recovery quality is evaluated based on the power quality, heat supply stability, and natural gas supply reliability.

4. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: In step S20, the uncertainty index uses a probability density function to describe the uncertainty of renewable energy power generation and load demand, evaluates the impact by calculating the expectation and variance, and establishes a price fluctuation coefficient index to consider the uncertainty of energy market price fluctuations. The fuzzy logic fuzzifies the fuzzy information of equipment aging and external environmental impact, establishes a fuzzy rule base, and obtains the evaluation results through fuzzy reasoning. The method for generating the disturbance scenario set further includes: S21. Generate probability distribution of equipment failures under extreme weather conditions through Monte Carlo simulation; S22, Time series characteristics of load mutation prediction based on LSTM neural network; S23. Perform spatiotemporal clustering of historical disaster data to build a typical disaster scenario database.

5. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: The calculation of the elasticity index in step S30 includes: S31, System recovery time: The time threshold from the occurrence of the disturbance to the restoration of power / gas / heat to the critical load; S32, Energy loss rate: the proportion of unmet energy demand during the disturbance to total demand; S33. Critical load guarantee rate: the continuous energy supply coverage rate of critical loads in hospitals and transportation hubs.

6. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: The method for generating the multi-level recovery strategy set in step S40 includes: a first-level strategy, a second-level strategy, and a third-level strategy; the first-level strategy prioritizes repairing trunk lines and energy hub equipment; the second-level strategy activates the black start capability of distributed energy storage and backup generator sets; and the third-level strategy adjusts the energy supply priority of non-critical loads through demand response.

7. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: The optimization objectives of the dynamic game algorithm using the Nash equilibrium model in step S40 include minimizing the weighted sum of system recovery time and energy loss rate and maximizing the critical load guarantee rate and equipment utilization rate.

8. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: The elasticity assessment report in step S50 includes: S51. Visualization map of weak links based on geographic information system; S52. Improvement plan for equipment redundancy and network topology; S53. Early warning information on resilience levels at different disaster levels.

9. The elasticity assessment method of a comprehensive energy distribution network according to claim 1, characterized in that: In step S50, the data acquisition and preprocessing collects the topology structure, equipment parameters, energy supply data, load demand data, and energy market price data of the integrated energy distribution network, and performs cleaning, denoising, and standardization. The result analysis and decision support analyzes the evaluation results, identifies system weaknesses and potential risks, and provides decision support for grid planning, operation, and maintenance, including optimizing equipment configuration, adjusting energy supply strategies, and formulating emergency plans.

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