Novel power system energy storage optimization and load loss evaluation method in extreme weather

Abstract

The invention discloses a novel power system energy storage optimization and load loss evaluation method in extreme weather, and relates to the technical field of power systems and energy storage optimization. In order to solve the technical defect of lack of a comprehensive extreme weather scene construction method in the existing power system and energy storage optimization technology in the prior art, the technical scheme provided by the invention comprises the following steps: establishing an extreme weather scene; establishing a power distribution network digital model, and simulating a power distribution network operation scene under an extreme meteorological condition; constructing a fault rate model of the electrical element, and generating a comprehensive time-varying fault probability model; obtaining operation parameters of the power distribution network, and analyzing response characteristics of the power distribution network under different operation conditions; calculating a load loss probability and a power shortage expected value, and comprehensively evaluating the load loss risk of the power grid; and the configuration and scheduling strategy of the energy storage equipment is optimized. The method can be applied to planning, operation scheduling and emergency management work of a power system under extreme weather conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and energy storage optimization. Background Art

[0002] With the intensification of global climate change, the frequent occurrence of extreme weather events (such as typhoons, heavy rains, ice and snow disasters, etc.) poses unprecedented challenges to the safety and stability of power systems. Currently, the research on power systems mainly focuses on aspects such as new energy access, energy storage optimization, and smart grid construction to improve the adaptability and resistance of the system to external environmental changes.

[0003] During the operation of power systems, extreme weather has a significant impact on transmission lines, substation equipment, and new energy generation equipment (such as wind power and photovoltaic power generation). For example, typhoons can cause transmission line breaks, and ice and snow disasters can cause line icing overloads, resulting in a decrease or complete interruption of transmission capacity. These problems not only increase the maintenance costs of grid equipment but also significantly reduce power supply reliability.

[0004] Some studies propose methods based on meteorological data statistics and simulation to construct extreme weather scenarios for power system operation simulation. However, these methods are usually limited to specific scenarios and cannot comprehensively simulate the multi-faceted impacts of different meteorological conditions on the system, especially the role and response mechanism of energy storage devices under extreme conditions.

[0005] The high proportion of access of new energy (such as wind power and photovoltaic power generation) significantly increases the complexity of grid load regulation. Natural factors such as wind speed and light intensity lead to the intermittency and uncertainty of new energy output, which poses a severe challenge to grid stability.

[0006] Some studies attempt to alleviate the impact of new energy fluctuations by optimizing the configuration and dispatching strategies of energy storage devices. For example, a battery energy storage system (BESS) is used to regulate load fluctuations. However, most of the existing technologies do not fully consider the impact of extreme weather conditions on the regulation effect of energy storage devices. Especially in the case of new energy disconnection from the grid due to equipment failures in the power grid, the coordinated dispatching scheme of energy storage devices still needs to be improved.

[0007] To improve the reliability of power systems, existing research has extensively modeled the failure rates of electrical components. For example, based on environmental conditions and historical operation data, the failure probabilities of transmission lines, photovoltaic panels, and wind turbines are analyzed. Some scholars propose to evaluate the reliability indicators of the power grid through the Monte Carlo simulation method, such as the loss of load probability (LOLP) and the expected energy not served (EENS). Although these methods are effective under normal operating conditions, under extreme weather conditions, existing models often fail to comprehensively consider the dynamic relationship between environmental variables and equipment states and are difficult to accurately evaluate the loss of load risk.

[0008] Technical problems existing in the prior art:

[0009] Although the above research has made certain progress in the fields of new energy access, power grid fault modeling, and energy storage optimization, there are still the following deficiencies:

[0010] There is a lack of a comprehensive extreme weather scenario construction method, making it difficult to accurately simulate the multi-dimensional impacts of extreme meteorological conditions on the distribution network and electrical components.

[0011] In the simulation of power grid operation considering extreme weather, the role of energy storage devices is not fully combined, and it is impossible to effectively evaluate the mitigation effect of energy storage on the risk of power grid load shedding.

[0012] The current failure rate model of electrical components is relatively single and fails to comprehensively reflect the dynamic impact of environmental variables on the failure rate of electrical components under extreme weather.

[0013] The scheduling and optimization strategies of energy storage devices lack pertinence under extreme weather conditions and are difficult to achieve coordinated responses to new energy fluctuations and power grid faults. Summary of the Invention

[0014] To solve the technical defect in the prior art that there is a lack of a comprehensive extreme weather scenario construction method in the existing power system and energy storage optimization technology, the technical solution provided by the present invention is as follows:

[0015] A novel power system energy storage optimization and load shedding assessment method under extreme weather, comprising the following steps:

[0016] Collect environmental data related to extreme weather, extract key meteorological parameters, and establish an extreme meteorological scenario;

[0017] Collect the operation data of the distribution network, establish a digital model of the distribution network, and simulate the operation scenario of the distribution network under extreme meteorological conditions;

[0018] Based on the extreme meteorological scenario and the operation data of the distribution network, construct a failure rate model of electrical components to generate a comprehensive time-varying failure probability model;

[0019] Use the digital model of the distribution network for power flow calculation, obtain the operation parameters of the distribution network, and analyze the response characteristics of the distribution network under different operating conditions;

[0020] Based on the extreme meteorological scenario and the comprehensive time-varying failure probability model, calculate the load shedding probability and the expected value of energy shortage, and comprehensively evaluate the load shedding risk of the power grid;

[0021] Combine the load shedding risk assessment results to optimize the configuration and scheduling strategies of energy storage devices.

[0022] Further, the construction process of the extreme weather scenario set includes: extracting key weather parameters based on the statistical parameters of the obtained climate data, and analyzing the key weather parameters, where the key weather parameters include wind speed, precipitation, temperature, and humidity; establishing a meteorological data set of extreme weather based on the meteorological data related to extreme weather in the area where the power system is located.

[0023] Further, the construction process of the distribution network scenario includes: providing reference information for the prediction of wind power generation, photovoltaic power generation, and thermal power generation according to the operation data of the distribution network.

[0024] Further, based on the influence of weather and meteorology, a mathematical model is constructed to analyze the failure rate model of electrical components.

[0025] Further, the response of the distribution network under different operating conditions or faults is used to analyze the operating efficiency and system stability of the power grid.

[0026] Further, the dispatching strategy includes: under extreme weather conditions, the energy storage device responds to the change of the power grid load in real time, adjusts the power grid voltage, and balances the fluctuation of new energy; when a fault occurs in the power grid, the energy storage device releases the stored energy to make up for the power gap of the power grid.

[0027] There is also provided an energy storage optimization and load shedding assessment device for a new power system under extreme weather, including the following modules:

[0028] Used to collect environmental data related to extreme weather, extract key meteorological parameters, and establish an extreme meteorological scenario;

[0029] Used to collect the operation data of the distribution network, establish a digital model of the distribution network, and simulate the operation scenario of the distribution network under extreme meteorological conditions;

[0030] Used to construct a failure rate model of electrical components based on the extreme meteorological scenario and the operation data of the distribution network, and generate a comprehensive time-varying failure probability model;

[0031] Used to perform power flow calculation using the digital model of the distribution network, obtain the operation parameters of the distribution network, and analyze the response characteristics of the distribution network under different operating conditions;

[0032] Used to calculate the load shedding probability and the expected value of insufficient electricity based on the extreme meteorological scenario and the comprehensive time-varying failure probability model, and comprehensively evaluate the load shedding risk of the power grid;

[0033] Used to optimize the configuration and dispatching strategy of the energy storage device in combination with the load shedding risk assessment results.

[0034] There is also provided a computer storage medium for storing a computer program, and when the computer program is read by a computer, the computer executes the method described above.

[0035] A computer is also provided, which includes a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the described method.

[0036] A computer program product is also provided. As a computer program, when the computer program is executed, the described method is implemented.

[0037] Compared with the prior art, the beneficial effects of the technical solution provided by the present invention are as follows:

[0038] By constructing a set of extreme weather scenarios, key parameters are extracted based on the collected meteorological data to simulate the impact of extreme meteorological conditions on the power system. Compared with the static analysis method that only relies on single meteorological data in the prior art, the dynamic scenario simulation of this solution can more comprehensively evaluate the operating characteristics of the power grid under different extreme meteorological conditions, thereby providing more accurate prediction support for power grid planning.

[0039] By establishing a failure rate model of electrical components, this solution comprehensively considers the dynamic correlation of environmental variables, equipment operating status, and historical data, and uses linear fitting and Monte Carlo simulation to generate a time-varying failure probability model. Compared with the traditional static failure rate model, this method can more accurately reflect the operating reliability of equipment under extreme weather conditions, thereby providing a higher level of credibility for the safety assessment of power grid operation.

[0040] In the power flow calculation part, by establishing a refined distribution network model, the Newton-Raphson algorithm is used to simulate and analyze the voltage, power flow direction, and losses of the distribution network under different operating states. Compared with the prior research that only uses a simplified model for power flow analysis, this method more accurately reveals the dynamic response characteristics of the power grid under extreme conditions, providing important technical support for power grid optimal dispatching.

[0041] In terms of the optimal configuration of energy storage devices, this solution introduces an energy storage system into the distribution network and combines dynamic scenario simulation to analyze its regulation effect on new energy fluctuations and power grid faults. Different from the traditional static energy storage dispatching method, this method can respond to load changes in real time under extreme weather, significantly improving the resilience and stability of the power grid and effectively reducing the risk of load shedding.

[0042] The comprehensive evaluation module provides a comprehensive load shedding risk assessment system by calculating the load shedding probability and the expected value of energy shortage, combined with the operating state of the power grid and the dispatching strategy of energy storage devices. Compared with the method that only relies on historical data for evaluation in the prior art, this method can more intuitively reflect the improvement effect of the power grid reliability after the optimization of energy storage devices, providing stronger guiding significance for decision-making support.

[0043] It can be applied to the planning, operation dispatching, and emergency management of power systems under extreme weather conditions. Description of the Drawings

[0044] Figure 1 It is a schematic flow chart of an optimization method for energy storage and load shedding assessment of a new power system under extreme weather conditions;

[0045] Figure 2 It is a diagram of the geographical location connection of the test system and a simulation diagram of the ice disaster scenario;

[0046] Figure 3 It is a line chart comparing the proportion of operating lines and load changes. Detailed Implementation Modes

[0047] To make the advantages and beneficial effects of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention will be further described in detail below in conjunction with the drawings. Specifically:

[0048] Embodiment 1. This embodiment provides an optimization method for energy storage and load shedding assessment of a new power system under extreme weather conditions, including the following steps:

[0049] Collect environmental data related to extreme weather, extract key meteorological parameters, and establish an extreme meteorological scenario;

[0050] Collect the operation data of the distribution network, establish a digital model of the distribution network, and simulate the operation scenario of the distribution network under extreme meteorological conditions;

[0051] Based on the extreme meteorological scenario and the operation data of the distribution network, construct a failure rate model of electrical components, and generate a comprehensive time-varying failure probability model;

[0052] Use the digital model of the distribution network for power flow calculation, obtain the operation parameters of the distribution network, and analyze the response characteristics of the distribution network under different operation conditions;

[0053] Based on the extreme meteorological scenario and the comprehensive time-varying failure probability model, calculate the load shedding probability and the expected value of insufficient power, and comprehensively evaluate the load shedding risk of the power grid;

[0054] Combined with the load shedding risk assessment results, optimize the configuration and scheduling strategy of energy storage devices.

[0055] The construction process of the extreme weather scenario set includes: based on the statistical parameters of the obtained climate data, extract key weather parameters, analyze the key weather parameters, and the key weather parameters include wind speed, precipitation, temperature and humidity; based on the meteorological data related to extreme weather in the area where the power system is located, establish a meteorological data set of extreme weather.

[0056] The construction process of the distribution network scenario includes: providing reference information for the prediction of wind power generation, photovoltaic power generation and thermal power generation according to the operation data of the distribution network.

[0057] Based on the impact of weather and meteorology, a mathematical model is constructed to analyze the failure rate model of electrical components.

[0058] The response of the distribution network under different operating conditions or fault situations is used to analyze the operating efficiency and system stability of the power grid.

[0059] The scheduling strategy includes: under extreme weather conditions, the energy storage device responds to the changes in the grid load in real time, adjusts the grid voltage, and balances the fluctuations of new energy; when a fault occurs in the grid, the energy storage device releases the stored energy to make up for the power gap in the grid.

[0060] Specifically:

[0061] Step 1: Collection of environmental data and construction of extreme meteorological scenarios

[0062] By collecting environmental data related to extreme weather, extracting key meteorological parameters, and establishing a complete set of extreme meteorological scenarios, it provides basic data support for subsequent power system operation simulation and evaluation.

[0063] Detailed description:

[0064] Through means such as meteorological monitoring stations and satellite remote sensing, collect environmental data related to extreme weather, including wind speed, temperature, humidity, precipitation, ice coating thickness, etc.

[0065] Based on the collected data, conduct statistical analysis, extract key meteorological parameters, especially the factors that have a significant impact on extreme weather events, such as wind speed and ice coating thickness in ice and snow disasters.

[0066] Construct a set of extreme meteorological scenarios, and through classification and induction of scenarios under different meteorological conditions, form a dynamic meteorological data model to provide input data for the subsequent steps.

[0067] Step 2: Establishment of the distribution network model and scenario simulation

[0068] By searching for and collecting various types of data related to the distribution network, construct a digital model of the distribution network and simulate the operating scenarios under extreme weather conditions.

[0069] Detailed description:

[0070] Collect operation data related to the distribution network, including the geographical location of nodes, access point information of power equipment, installed capacity and operating status of power generation equipment (wind power, photovoltaic, thermal power) and energy storage equipment.

[0071] Using power system simulation software, based on the collected data, establish a digital model of the distribution network, and the parameters include node voltage, reactance, resistance, power capacity, etc.

[0072] Overlay the extreme meteorological scenarios generated in the first step in the digital model to simulate the operation status of the distribution network under different meteorological conditions, and pay special attention to the fluctuations of new energy power generation, load changes, and the response behavior of energy storage devices.

[0073] Step 3: Establishment of the failure rate model for electrical components

[0074] Based on environmental data and distribution network operation data, establish a failure rate model for electrical components under extreme weather conditions to provide support for the probability analysis of grid fault states.

[0075] Detailed description:

[0076] For transmission lines, based on the failure probability analysis of ice-wind loads, establish a line icing growth model and a line stress model, and calculate the failure probability of the line through the metal deformation theory.

[0077] For photovoltaic power generation equipment, establish a photovoltaic output probability model based on light intensity, temperature, and meteorological conditions, and calculate the failure rate of photovoltaic equipment using regression analysis.

[0078] For wind power equipment, construct a wind power output probability model based on wind speed and meteorological data, and calculate the failure probability in combination with the operating status of the wind turbines.

[0079] Combined with the operating status data of the equipment, use linear fitting to generate an operating failure probability model for the equipment, and finally obtain a comprehensive time-varying failure probability model through the series model and the Monte Carlo simulation method.

[0080] Step 4: Power flow calculation and parameter analysis of the distribution network

[0081] Use the distribution network digital model to perform power flow calculations to obtain the operating parameters of the distribution network, providing data support for subsequent load loss assessment and energy storage optimization.

[0082] Detailed description:

[0083] Apply the Newton-Raphson power flow calculation method to analyze the key operating parameters such as voltage, power flow direction, and loss of the distribution network, and obtain data such as node voltage, branch current, and system loss.

[0084] Compare the power flow calculation results under normal operation and extreme weather conditions, and analyze the response characteristics of the power grid under different operating states.

[0085] Extract the parameter data of the power grid operation, including voltage level, power factor, load distribution, etc., to provide input for subsequent load loss assessment.

[0086] Step 5: Load loss assessment and energy storage optimization

[0087] Based on extreme meteorological scenarios, electrical component failure rate models, and power flow calculation results, comprehensively evaluate the load shedding risk of the power grid, and optimize the energy storage configuration and operation strategy.

[0088] Detailed description:

[0089] Use the Monte Carlo simulation method, combined with extreme meteorological scenarios and equipment failure rate models, to calculate the loss of load probability (LOLP) and the expected energy not served (EENS), and evaluate the load shedding risk of the power grid.

[0090] Analyze the charge and discharge response of energy storage devices under extreme weather conditions, and optimize the configuration and scheduling of energy storage devices by comparing the system reliability indicators under different operation strategies.

[0091] Determine the capacity and operation plan of the energy storage device to ensure that the energy storage system can effectively compensate for the impact of new energy fluctuations and power grid failures on power supply under extreme weather conditions, reduce the load shedding risk, and improve the stability of the power grid.

[0092] Step 6: Comprehensive evaluation and optimization decision support

[0093] Based on the load shedding assessment and energy storage optimization results, generate a reliability assessment report for power grid operation, providing scientific decision-making support for power grid planning and emergency management.

[0094] Detailed description:

[0095] Combine the load shedding risk analysis and energy storage scheduling optimization results to quantify the reliability and security indicators of the power system under extreme weather conditions.

[0096] Output the risk curve of power grid operation and the optimized energy storage configuration plan, providing a scientific basis for the power grid dispatching department.

[0097] According to the meteorological characteristics and power grid structure of different regions, flexibly adjust the methods and model parameters to form a more adaptable optimization management strategy.

[0098] Implementation method 2. Combine Figures 1-3 To illustrate this implementation method, this implementation method further describes the above-provided technical solution through specific embodiments. Specifically:

[0099] This embodiment utilizes extreme weather meteorological data and system operation data to construct a comprehensive set of extreme weather scenarios and corresponding parameters for in-depth analysis of the impact of extreme weather on the new power system. By systematically collecting environmental data related to extreme weather and various operation data of the distribution network, including wind power, photovoltaic power, and thermal power generation, etc., a rich set of extreme meteorological scenarios are established. By analyzing the failure rates of electrical components under different operating conditions, a probability model of the occurrence of failure states is established, and a comprehensive analysis framework based on the extreme weather model and the probability model of the occurrence of grid failure states is established. The change in the grid load shedding amount is obtained, and the response ability of energy storage devices to the power system under extreme weather is quantified, thereby promoting the optimization management and emergency response strategies of the new power system. By establishing a probabilistic optimal power flow model, various risk indicators and reliability indicators of the overall operation of the power system are deduced, and a power system risk curve is drawn, providing a scientific basis and decision-making support for the safe and stable operation of the power system.

[0100] To achieve the above objectives, this embodiment specifically includes the following components:

[0101] Collecting environmental data related to extreme weather includes: environmental data related to extreme weather such as temperature, humidity, wind speed, wind direction, precipitation, ice coating thickness, etc. These data can be obtained through means such as meteorological monitoring stations and satellite remote sensing, and stored in a database to provide basic data support for subsequent analysis;

[0102] Based on the collected environmental data, establish extreme meteorological scenarios, simulate distribution network scenarios, and collect various data related to the distribution network, including the installed capacity, geographical location, access point information, and operating status of wind power generation, photovoltaic power generation, thermal power generation, and energy storage devices. Use power system simulation software to establish a digital model of the distribution network, simulate the distribution network scenarios under different operating conditions, consider the intermittency and uncertainty of new energy power generation, as well as the charge and discharge characteristics of energy storage devices, to achieve dynamic simulation of the operating status of the distribution network.

[0103] Combine the extreme meteorological scenarios and the distribution network simulation results to establish a failure rate model of electrical components, analyze the stress conditions, temperature changes, humidity effects, etc. of electrical components under different extreme weather conditions. This model can quantitatively describe the failure probability of electrical components under the action of extreme weather and the conditional probability of the occurrence of failure states, providing key parameters for grid risk assessment.

[0104] (1) Construct a line ice accretion growth model and a line stress model; according to the metal deformation theory, analyze the line ice accretion growth model and the line stress model to obtain a line failure probability model of ice-wind load, L WI is the ice-wind load a WI and b WI are two threshold values of the ice-wind load.

[0105]

[0106] (2) Obtain historical photovoltaic power output and meteorological factor data, and construct a photovoltaic power output probability model; based on influencing factors such as light intensity, temperature, and light conditions, perform regression analysis on the above to obtain the photovoltaic power output probability. α and β are the shape parameters released by Beta; Γ(·) is the gamma function; P PV is the actual output power of the photovoltaic power plant; is the maximum output power of the photovoltaic power plant.

[0107] Photovoltaic power output probability calculation formula:

[0108]

[0109] Obtain historical wind speed and meteorological factor data, and construct a wind power output probability model; based on wind speed, temperature, and other relevant meteorological conditions, analyze the wind power output probability model to obtain the wind power output probability model of the wind turbine under different wind speed conditions; v r is the rated wind speed; v ci is the cut-in wind speed; v co is the cut-out wind speed; P r is the rated power.

[0110] Wind power output probability calculation formula:

[0111]

[0112] Using the determined distribution network model, perform power flow calculations to obtain parameter data of the distribution network under different operating conditions or fault conditions, such as node voltage, branch current, power distribution, line loss, etc. By comparing the power flow calculation results under normal operation and fault conditions, analyze the response characteristics of the power grid, evaluate the stability and power supply reliability of the power grid, and provide data support for subsequent load loss assessment and energy storage optimization.

[0113] According to the established extreme weather model and power grid fault state occurrence probability model, use Monte Carlo simulation, risk assessment theory, and system reliability analysis methods to comprehensively evaluate the change in load loss of the power grid under extreme weather conditions;

[0114] Calculate the loss of load probability (LOLP) and the expected energy not served (EENS), and evaluate the loss of load risk of the power grid.

[0115] The calculation formula for the loss of load probability LOLP is as follows:

[0116]

[0117] Where S is the total number of scenarios; T is the total number of time intervals in each scenario; △t is the duration of each time interval;

[0118] The calculation formula for the expected energy not served (EENS) is as follows:

[0119]

[0120] Where P unsupplied,i,t is the load power not met by the system during the t-th time interval in the i-th scenario; △t is the duration of each time interval; n is the total number of scenarios; T is the total number of time intervals in each scenario;

[0121] Combining the loss-of-load probability and the expected energy not served, comprehensively evaluate the loss-of-load risk of the power system;

[0122] LOLR = LOLP × EENS

[0123] Analyze the impact of energy storage devices on the reliability and stability of the system under different operating strategies, and determine the optimal energy storage configuration plan and operating strategy;

[0124] Under extreme weather conditions, the impact of energy storage devices on loss of load and on power plant outages are important considerations for the stability and reliability of the microgrid system;

[0125] The impact of energy storage devices on loss of load. Energy storage devices can serve as backup power sources under extreme weather, can quickly provide electricity, reduce the loss-of-load incidence rate under extreme weather, and improve the power supply reliability of users; when extreme weather is predicted, the system can be pre-charged, and the energy storage system can act as a buffer, improving the overall resilience of the microgrid and helping to cope with various emergencies, thereby enhancing the stability of the power system.

[0126] The impact of energy storage devices on power plant outages. In the case of a power plant outage, energy storage devices can release the stored electricity in a timely manner, balance the grid load, maintain the stability of power supply, reduce the economic losses and service interruptions caused by insufficient power generation, and improve the economy of the system.

[0127] The impact of energy storage devices on loss of load and on power plant outages under extreme weather conditions complement each other. An effective energy storage system not only helps to reduce loss of load and enhance the resilience of the power system, but also provides important support when a power plant outage occurs, ensuring stable and reliable power supply in extreme weather areas. When designing a microgrid system, the capacity, charge-discharge strategy, and dispatching control of energy storage devices should be fully considered to cope with possible extreme weather events.

[0128] For the new power system in the northeast region of China, where ice and snow disasters often occur in winter. Collect meteorological data such as ice and snow thickness, temperature, and wind speed in the local area to construct an ice and snow disaster meteorological scenario. Combine the local distribution network data to simulate the impact of ice and snow disasters on the distribution network and establish a failure rate model for electrical components. Conduct power flow calculations to analyze the operating state of the power grid under ice and snow disaster conditions and evaluate the load shedding risk. By optimizing the configuration and operation strategy of energy storage devices, improve the power supply reliability of the power grid during ice and snow disasters and reduce power outage losses. The above embodiments are only used to illustrate the specific application of this implementation manner, and the protection scope of this implementation manner is not limited to the above embodiments. In actual applications, the methods of this implementation manner can be flexibly used according to the meteorological characteristics of different regions and the power system structure to achieve effective evaluation and optimization of the new power system under extreme weather conditions.

[0129] The technical solutions provided by the present invention are further described in detail through several specific implementation manners to highlight the advantages and beneficial effects of the technical solutions provided by the present invention. However, the above-mentioned several specific implementation manners are not used as a limitation to the present invention. Any reasonable modifications and improvements, combinations of implementation manners, and equivalent replacements based on the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A new method for optimizing energy storage and evaluating load loss in power systems under extreme weather conditions, characterized in that: The following steps are involved: Collect environmental data related to extreme weather, extract key meteorological parameters, and establish extreme weather scenarios; Collect the operation data of the distribution network, establish a digital model of the distribution network, and simulate the operation scenarios of the distribution network under extreme weather conditions; Based on extreme weather scenarios and distribution network operation data, a failure rate model for electrical components is constructed to generate a comprehensive time-varying failure probability model; Use the digital model of the distribution network to calculate the power flow, obtain the distribution network operating parameters, and analyze the response characteristics of the distribution network under different operating conditions; Based on extreme weather scenarios and a comprehensive time-varying fault probability model, the load loss probability and expected value of power shortage are calculated to comprehensively assess the load loss risk of the power grid; Combined with the load loss risk assessment results, the configuration and scheduling strategy of energy storage equipment are optimized.

2. According to a new power system energy storage optimization and load loss assessment method under extreme weather conditions as described in claim 1, it is characterized in that: The construction process of the extreme weather scenario set includes: extracting key weather parameters based on the acquired climate data statistical parameters, and analyzing the key weather parameters, wherein the key weather parameters include wind speed, precipitation, temperature and humidity; and establishing an extreme weather meteorological data set based on the meteorological data related to extreme weather in the area where the power system is located.

3. According to a new power system energy storage optimization and load loss assessment method under extreme weather conditions as described in claim 1, it is characterized in that: The construction process of the distribution network scenario includes: providing reference information for the prediction of wind power generation, photovoltaic power generation and thermal power generation based on the operation data of the distribution network.

4. According to a new power system energy storage optimization and load loss assessment method under extreme weather conditions as described in claim 1, it is characterized in that: Based on the influence of weather and meteorology, a mathematical model is constructed to analyze the failure rate model of electrical components.

5. A new power system energy storage optimization and load loss assessment method under extreme weather conditions according to claim 1, characterized in that: The response of the distribution network under different operating conditions or fault conditions is used to analyze the operating efficiency and system stability of the power grid.

6. A new power system energy storage optimization and load loss assessment method under extreme weather conditions according to claim 1, characterized in that: The dispatching strategy includes: under extreme weather conditions, the energy storage equipment responds to changes in grid load in real time, adjusts grid voltage, and balances fluctuations in renewable energy; when a grid failure occurs, the energy storage equipment releases stored energy to make up for the power gap in the grid.

7. A new type of power system energy storage optimization and load loss assessment device under extreme weather conditions, characterized in that: Includes the following modules: Used to collect environmental data related to extreme weather, extract key meteorological parameters, and establish extreme weather scenarios; Used to collect distribution network operation data, establish a distribution network digital model, and simulate distribution network operation scenarios under extreme weather conditions; Used to build a failure rate model for electrical components and generate a comprehensive time-varying failure probability model based on extreme weather scenarios and distribution network operation data; Used to calculate power flow using the digital model of the distribution network, obtain the operating parameters of the distribution network, and analyze the response characteristics of the distribution network under different operating conditions; It is used to calculate the load loss probability and power shortage expectation value based on extreme weather scenarios and comprehensive time-varying fault probability models, and comprehensively evaluate the load loss risk of the power grid; Used to optimize the configuration and scheduling strategy of energy storage equipment based on the load loss risk assessment results.

8. A computer storage medium for storing a computer program, characterized in that: When the computer program is read by a computer, the computer executes the method of claim 1 .

9. A computer, comprising a processor and a storage medium, characterized in that: When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method of claim 1 is implemented.

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