A power system long-time energy storage demand analysis method and system considering an extreme scenario set

Abstract

This invention relates to a method and system for analyzing long-term energy storage demand in power systems, taking into account extreme scenario sets. It belongs to the technical field of power system energy storage demand analysis. The method includes: constructing a future extreme scenario meteorological dataset for the region based on historical hourly meteorological data and future meteorological forecast data from a regional center; calculating the renewable energy output curves and load curves for each extreme scenario based on the future extreme scenario meteorological dataset; constructing a long-term energy storage demand calculation model for the power system that considers carbon budgets and aims to minimize total system losses; total system losses include energy storage losses, system operation and maintenance losses, thermal power unit start-up and shutdown losses, and environmental losses; environmental losses are calculated from carbon emissions exceeding the carbon budget; obtaining thermal power unit parameters and energy storage parameters for the power system at each time period; and solving the model based on the renewable energy output curves and load curves for each extreme scenario to obtain the corresponding long-term energy storage demand configuration scheme.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage demand analysis technology, and in particular to a method and system for long-term power system energy storage demand analysis that takes into account extreme scenario sets. Background Technology

[0002] Against the backdrop of global efforts to address climate change and advance a deep low-carbon transition, the power system is undergoing a structural transformation from being primarily reliant on fossil fuels to being centered on renewable energy. The rapid and sustained growth in installed capacity of renewable energy sources such as wind and solar power has powerfully driven the clean energy transformation of the power system, but it has also brought about high volatility and uncertainty in power output. Particularly with the increasing frequency of extreme weather events, the safe and stable operation of the power system faces even greater challenges. In recent years, the frequency of "non-catastrophic extreme weather events," such as prolonged periods of extreme cold without sunshine and extreme heat without wind, has increased significantly globally. While these events do not cause direct damage, they can continuously reduce the output of renewable energy while simultaneously increasing electricity load demand, leading to severe supply-demand imbalances in the power system on a medium- to long-term scale. This long-term, systemic supply-demand mismatch cannot be effectively alleviated by short-term adjustment measures and urgently requires the intervention of resources with large-capacity, long-duration adjustment capabilities to achieve balance.

[0003] Traditional power systems typically rely on controllable fossil fuel units, such as coal-fired and gas-fired power plants, to fill the gap in renewable energy output and maintain real-time supply-demand balance. However, with the introduction of carbon neutrality goals and increasingly stringent carbon budget constraints, the use of these high-carbon emission regulation resources is severely limited, and their role as a means of ensuring system flexibility is weakening. Against this backdrop, long-duration energy storage technology, with its ability to provide continuous power supply and regulation over long timescales and its environmental advantage of zero carbon emissions, has become a crucial flexibility resource in future low-carbon power systems. Long-duration energy storage can not only maintain stable power supply during periods of continuous low renewable energy output but also replace traditional fossil fuel regulation sources in carbon-constrained scenarios, enhancing the system's resilience to extreme weather impacts. Therefore, exploring the demand for long-duration energy storage adapted to carbon budget constraints and extreme climate conditions has significant practical and strategic value.

[0004] Despite the increasingly prominent role of long-duration energy storage in low-carbon power systems, current research and technological approaches still have many shortcomings, particularly in the failure to systematically construct models for risk resilience analysis when assessing energy storage demand. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for analyzing the long-term energy storage demand of power systems that takes into account extreme scenario sets, in order to solve the problem of how to systematically analyze the long-term energy storage demand of power systems in extreme climate scenario sets, so as to overcome the technical problems of unsystematic extreme scenario construction and lack of joint assessment of carbon constraints and extreme climate in existing methods.

[0006] This invention provides a method for analyzing long-term energy storage demand in power systems that takes into account extreme scenario sets, comprising the following steps:

[0007] Based on historical hourly meteorological data and future meteorological forecast data from the regional center, a future extreme scenario meteorological dataset is constructed within the region; based on the future extreme scenario meteorological dataset, the power output curve and load curve of new energy sources under each extreme scenario are calculated respectively. A long-term energy storage demand calculation model for a power system is constructed, taking into account the carbon budget and aiming to minimize the total system loss. The total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss. The environmental loss is calculated from the carbon emissions exceeding the carbon budget. The parameters of thermal power units and energy storage parameters of the power system at each time period are obtained. Based on the output curves and load curves of new energy sources under each extreme scenario, the long-term energy storage demand calculation model of the power system is solved to obtain the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

[0008] Furthermore, a meteorological dataset for future extreme scenarios will be constructed, including: Acquire historical hourly meteorological data and predicted daily average meteorological data for future scenarios from the regional center; Based on the fluctuation characteristics of historical hourly meteorological data, the predicted daily average meteorological data is extended in time scale to generate future hourly meteorological data; wherein, the future hourly meteorological data includes future hourly wind speed data, solar irradiance data, and temperature data; Based on the aforementioned future hourly meteorological data and historical hourly meteorological data, extreme low-output events are statistically analyzed based on the graded thresholds of wind speed and solar irradiance, resulting in multiple future extreme scenario meteorological datasets with different degrees of extremeness.

[0009] Furthermore, the predicted daily average meteorological data is extended in time scale to generate future hourly meteorological data, including: For non-negative wind speed or solar irradiance data, calculate the difference sequence between the historical daily average and the future predicted daily average, and superimpose the difference sequence onto the historical hourly data sequence to obtain the future hourly wind speed or solar irradiance sequence. For temperature data containing negative values, the historical hourly temperature data is recombined using the sequence recombination method to obtain the recombined historical hourly temperature sequence; the difference sequence between the recombined historical hourly temperature sequence and the predicted daily average temperature is calculated; the recombined historical hourly temperature sequence is superimposed with the difference sequence to obtain the future hourly temperature sequence.

[0010] Furthermore, based on the meteorological dataset of future extreme scenarios, the calculation of the new energy output curve and load curve under each extreme scenario includes: Based on the wind speed data in the aforementioned future extreme scenario meteorological dataset, the wind power output curves under each extreme scenario are obtained using the constructed wind power output calculation model. Based on the solar irradiance, temperature and photovoltaic panel parameters of the meteorological data of the future extreme scenarios, the photovoltaic output curves under each extreme scenario are obtained by using the constructed photovoltaic output calculation model. Based on the temperature, irradiance, wind speed, and humidity data in the aforementioned future extreme scenario meteorological dataset, load curves for each extreme scenario are obtained using the constructed temperature-responsive load model.

[0011] Furthermore, the objective function of the power system long-term energy storage demand calculation model is as follows:

[0012] in, For the total system loss, These are energy storage losses, system operation and maintenance losses, thermal power unit start-up and shutdown losses, and environmental losses.

[0013] Furthermore, the energy storage loss is as follows:

[0014] in, , and These are the equivalent annual value factor, discount rate, and equipment life for short-term and long-term energy storage, respectively. , and These are the unit charging power loss, unit discharging power loss, and unit capacity loss for long-term energy storage, respectively. , and These are the charging power capacity, discharging power capacity, and energy capacity configurations for long-term energy storage, respectively. The system operation and maintenance costs are as follows:

[0015] in, , and These are respectively: unit fuel loss of thermal power units, unit maintenance loss of thermal power units, and unit variable operation and maintenance loss of long-term energy storage. , These are respectively the installed capacity of thermal power units and thermal power units. Always put in the effort; It is a time set.

[0016] Furthermore, the start-up and shutdown losses of the thermal power unit are as follows:

[0017] in, , These are the starting and stopping losses of thermal power units, respectively. , For thermal power units The number of starts and stops at any given time; The environmental losses are as follows:

[0018] in, , The unit loss required to meet carbon emission limits and The amount of carbon dioxide emissions that exceeds the carbon emission limit at any given time.

[0019] Furthermore, the constraints of the long-term energy storage demand calculation model include power balance constraints, decision constraints, thermal power unit operation constraints, new energy output constraints, and long-term energy storage constraints. The operating constraints of thermal power units include thermal power unit output constraints, start-stop constraints, and annual utilization hours constraints. The long-term energy storage constraints include upper and lower limits for charging power, SOC state constraints, upper and lower limits for SOC capacity, SOC constraints at the end of the scheduling cycle, discharge power constraints, charging power constraints, and capacity-to-power ratio constraints.

[0020] Furthermore, the long-term energy storage demand calculation model of the power system is solved by calling CPLEX through YALMP. The resulting long-term energy storage demand configuration scheme includes the charging power capacity of long-term energy storage corresponding to extreme scenarios. Discharge power capacity With energy capacity .

[0021] The present invention also discloses a power system long-term energy storage demand analysis system that takes into account extreme scenario sets. The system includes a scenario construction and data calculation module M1, an optimization model construction module M2, and a solution and configuration output module M3. The scenario construction and data calculation module M1 is used to construct a future extreme scenario meteorological dataset within the region based on historical hourly meteorological data and future meteorological forecast data from the regional center; and to calculate the new energy output curve and load curve under each extreme scenario based on the future extreme scenario meteorological dataset. The optimization model construction module M2 is used to construct a long-term energy storage demand calculation model for the power system that takes into account the carbon budget and aims to minimize the total system loss; wherein, the total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss; the environmental loss is calculated from the carbon emissions that exceed the carbon budget; The solution and configuration output module M3 is used to obtain the thermal power unit parameters and energy storage parameters of the power system at each time period. Based on the new energy output curves and load curves under each extreme scenario, it solves the long-term energy storage demand calculation model of the power system and obtains the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

[0022] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. This invention integrates historical meteorological data with future climate change predictions to systematically construct a set of extreme scenarios covering multiple types and time scales, accurately depicting the supply-demand imbalance under the most unfavorable meteorological conditions such as continuous low renewable energy output and extreme load increases. This method provides a scientific and rigorous boundary condition basis for long-term energy storage configuration, significantly improving the robustness and adaptability of the model in dealing with extreme climate risks; and enhancing the power system's risk response capabilities. 2. Under carbon budget constraints, this solution not only calculates the total energy storage capacity but also analyzes key performance parameters such as energy storage discharge duration and charge / discharge power scale in detail. It establishes the response relationship between these parameters and the intensity, duration, and carbon emission constraints of extreme scenarios, quantifying multi-dimensional energy storage requirements. Compared to traditional single-capacity index assessments, the multi-dimensional parameter outputs are closer to actual engineering design and system operation needs, providing direct and practical decision support for the selection and configuration of energy storage systems. 3. This invention constructs an optimization model aimed at minimizing total system losses, comprehensively considering energy storage investment, operation and maintenance, unit start-up and shutdown, and environmental costs. This solution can scientifically determine the optimal configuration for long-term energy storage while meeting carbon constraints. It helps avoid system instability risks caused by insufficient energy storage configuration or energy waste due to redundant configuration, improving both long-term power system reliability and operational efficiency.

[0023] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0024] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0025] Figure 1 This is a flowchart of a method for analyzing the long-term energy storage demand of a power system that takes into account extreme scenario sets, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the historical average daily temperature before recombination and the future average daily temperature curves in an embodiment of the present invention; Figure 3 This is a schematic diagram of the historical average daily temperature and future average daily temperature curves after recombination in an embodiment of the present invention; Figure 4 This is a schematic diagram of the functional modules of a power system long-term energy storage demand analysis system that takes into account extreme scenario sets, according to an embodiment of the present invention. Detailed Implementation

[0026] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0027] Example 1: This invention provides a method for analyzing the long-term energy storage demand of power systems that takes into account carbon budgets and extreme scenario sets. It comprehensively considers the construction of carbon budgets and extreme scenario sets to fully analyze the long-term energy storage demand of regional power systems.

[0028] A specific embodiment of the present invention discloses a method for analyzing long-term energy storage demand in power systems that takes into account extreme scenario sets, such as... Figure 1 As shown, it includes the following steps: Step S1: Based on historical hourly meteorological data and future meteorological forecast data from the regional center, construct a future extreme scenario meteorological dataset for the region; based on the future extreme scenario meteorological dataset, calculate the new energy output curve and load curve for each extreme scenario. Step S2: Construct a long-term energy storage demand calculation model for the power system that takes into account the carbon budget and aims to minimize the total system loss; wherein, the total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss; the environmental loss is calculated from the carbon emissions exceeding the carbon budget; Step S3: Obtain the thermal power unit parameters and energy storage parameters of the power system for each time period. Based on the new energy output curves and load curves under each extreme scenario, solve the long-term energy storage demand calculation model of the power system to obtain the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

[0029] Step S1 includes steps S11-S12.

[0030] Step S11: Construct a meteorological dataset of future extreme scenarios within the region.

[0031] Based on historical hourly meteorological data of the regional center point and meteorological forecast data from future global models, a meteorological dataset of future extreme scenarios considering climate change and the increasing frequency of extreme weather events is constructed within the region. For example, this scheme selects a 0.5° × 0.5° (latitude and longitude) grid as the target study area. The coordinates of the selected regional center point are used to download meteorological data.

[0032] Constructing a meteorological dataset for future extreme scenarios, including: Acquire historical hourly meteorological data and predicted daily average meteorological data for future scenarios from the regional center; Based on the fluctuation characteristics of historical hourly meteorological data, the predicted daily average meteorological data is extended in time scale to generate future hourly meteorological data; wherein, the future hourly meteorological data includes future hourly wind speed data, solar irradiance data, and temperature data; Based on the aforementioned future hourly meteorological data and historical hourly meteorological data, extreme low-output events are statistically analyzed based on the graded thresholds of wind speed and solar irradiance, resulting in multiple future extreme scenario meteorological datasets with different degrees of extremeness.

[0033] The predicted daily average meteorological data is augmented with a time scale to generate future hourly meteorological data, including: For non-negative wind speed or solar irradiance data, calculate the difference sequence between the historical daily average and the future predicted daily average, and superimpose the difference sequence onto the historical hourly data sequence to obtain the future hourly wind speed or solar irradiance sequence. For temperature data containing negative values, the historical hourly temperature data is recombined using the sequence recombination method to obtain the recombined historical hourly temperature sequence; the difference sequence between the recombined historical hourly temperature sequence and the predicted daily average temperature is calculated; the recombined historical hourly temperature sequence is superimposed with the difference sequence to obtain the future hourly temperature sequence.

[0034] (1) Obtain historical hourly meteorological information of the regional center point and obtain daily average meteorological forecast data for future scenarios.

[0035] Historical hourly meteorological data includes temperature, wind speed, humidity, solar irradiance, and solar position data. For example, the historical hourly meteorological data comes from the NASA POWER project platform, which is an open-source, non-profit scientific data platform that can be downloaded independently. The predicted daily average meteorological data for future scenarios includes temperature, wind speed, and solar irradiance. The data comes from the CMIP6 dataset, which includes prediction data from GCM models participating in the CMIP6 program. It is an open-source platform, and the data can be downloaded independently. For example, the predicted daily average meteorological data for 2040 under the BCC-CSM2-MR model of the CMIP6 program and the SSP245 intermediate carbon emission scenario can be selected.

[0036] (2) Based on the fluctuation characteristics of historical hourly meteorological data, the time scale of the daily average meteorological forecast data for future scenarios is expanded from the daily average meteorological forecast data to the hourly meteorological forecast data.

[0037] The expanded hourly weather forecast curve shape is consistent with the historical hourly weather data curve shape, and the daily average value of the expanded weather forecast data is consistent with the historical daily average weather data forecast value. This assumes that climate change only causes daily average changes, and the changing trends are the same for each hour. In practical operation, two methods are used: one for non-negative data and the other for data containing negative values, as follows: ① For non-negative data (wind speed, solar irradiance): Taking wind speed data as an example, the first step is to calculate the difference between the historical simulated daily average wind speed (not real historical data, but historical data simulated using the model in CMIP6) and the future predicted daily average wind speed. Both the historical simulated daily average wind speed and the future predicted daily average wind speed in this step are simulated data from the CMIP6 model.

[0038] By superimposing the above differences onto historical hourly wind speed data (historical data released by NASA), a new hourly wind speed is obtained. This new hourly wind speed not only preserves the fluctuation pattern (i.e., the curve shape) of historical hourly wind speeds, but also satisfies the requirement that the daily average change conforms to the predictions of global climate models, and can be used as a future hourly wind speed series.

[0039] For solar irradiance, the same method as for wind speed was used to obtain the future solar irradiance sequence.

[0040] ② For data containing negative numbers (temperature): Do not use the above method, as it will lead to extreme distortion values.

[0041] The sequence recombination method is adopted. First, the historical hourly data is recombined, and then the above-mentioned operation method of balancing the daily average values ​​is repeated.

[0042] Historical hourly data is reorganized to increase the matching degree between historical and predicted data, so that the hourly data is less likely to be distorted when the historical data is scaled by mean.

[0043] The recombination method is as follows: First, the historical daily average temperature is calculated based on historical hourly temperature data. One historical daily average temperature corresponds to 24 historical hourly temperatures. That is, a historical daily average temperature is obtained by using the historical hourly temperatures of a day over 24 hours. For each future historical daily average temperature, match the nearest historical daily average temperature and its corresponding 24-hour historical hourly temperature. The nearest historical daily average temperature represents the daily average temperature within 30 days before and after the same month and day in the future year that is closest to the historical daily average temperature. For example, if the daily average temperature on February 1, 2040 is 8℃, the closest daily average temperature between January 16, 2022 and February 14, 2022 is found to be 8.1℃, which occurs on January 25, 2022. Then, we use the 24-hour hourly temperature corresponding to January 25, 2022 as the hourly data for February 1, 2022 in the recombined sequence, and so on to obtain all hourly temperature data for 2022 after recombination.

[0044] like Figure 2 , 3 As shown, the reconstructed historical hourly temperature series was obtained based on the future daily average temperature. For the reconstructed historical hourly temperature series, the difference between the reconstructed daily average temperature and the future predicted daily average temperature was calculated. By overlaying the recombined historical hourly temperature series with the temperature difference series, the future hourly temperature series is obtained. The future hourly temperature series retains the intraday temperature characteristic curves and ensures that the daily average temperature is consistent with the predicted value.

[0045] (3) Based on the expanded future hourly meteorological data and historical hourly meteorological data, extreme low power output events are statistically analyzed. The method for statistically analyzing extreme low power output events is as follows: For example, an extreme low-output event is defined as a duration of 5 hours or more from the start to the end of a condition.

[0046] For example, the conditions could be threshold values ​​for wind speed and irradiance. For instance, the no-wind event condition could be v ≤ 3 m / s.

[0047] Using the power output capacity of new energy sources as the core indicator, this study selects two key variables—wind speed and solar irradiance—to characterize the impact of extreme weather on wind and solar power resources. Specifically, multiple tiered thresholds are set based on the joint distribution of wind speed and irradiance to classify the degree of low availability of new energy power output. Different levels of wind and solar conditions correspond to different degrees of extreme weather severity.

[0048] For example, this solution constructs extreme low-output events such as low light, low light, no light, light wind, and no wind.

[0049] (1) A cut-in wind speed v ≤ 3 m / s is defined as no wind; (2) A cut-in wind speed v ≤ 5 m / s is defined as a light breeze; (3) An event in which the actual received irradiance GTI ≤ 30 W / m² is defined as no light; (4) Events with GTI < 100 W / m² are defined as low light; (5) An event with GTI < 200 W / m² is defined as low light.

[0050] The frequency and duration of extremely low output events were statistically analyzed for both historical and future scenarios.

[0051] (4) Construct a meteorological dataset for future extreme scenarios: The future extreme scenario meteorological dataset represents meteorological data under a preset level of extreme weather growth.

[0052] For example, the extreme low power output events are set to increase by 5%, 10%, and 15% respectively. The event whose duration is closest to the average duration among the original extreme low power output events is selected as the meteorological data of the new extreme low power output events. The meteorological data corresponding to the new events replaces the normal meteorological data of the same period under non-extreme days, thus completing the construction of the meteorological dataset under extreme scenarios.

[0053] The purpose of step S11 is to construct a meteorological dataset based on the existing dataset for the scenarios under the influence of climate change and extreme weather, so as to provide basic data for subsequent calculation of new energy and load curves.

[0054] Step S12: Calculate the new energy output and load curves for each scenario based on the constructed extreme scenario meteorological dataset, and use them as the basic input data for the long-term energy storage demand calculation model.

[0055] Based on the aforementioned future extreme scenario meteorological dataset, the renewable energy output curves and load curves under each extreme scenario are calculated, including: Based on the wind speed data in the aforementioned future extreme scenario meteorological dataset, the wind power output curves under each extreme scenario are obtained using the constructed wind power output calculation model. Based on the solar irradiance, temperature and photovoltaic panel parameters of the meteorological data of the future extreme scenarios, the photovoltaic output curves under each extreme scenario are obtained by using the constructed photovoltaic output model. Based on the temperature, irradiance, wind speed, and humidity data in the aforementioned future extreme scenario meteorological dataset, load curves for each extreme scenario are obtained using the constructed temperature-responsive load model.

[0056] We constructed wind power output calculation models, photovoltaic power output calculation models, and temperature response load calculation models based on meteorological information, and calculated wind power output curves, photovoltaic power output curves, and load curves under corresponding extreme scenarios.

[0057] (1) Obtain the wind power and photovoltaic output curves; The wind power output calculation model is constructed as follows: A CRRC CWT2000-D122 wind turbine with a rated capacity of 2MW and a hub height of 90m was used as a typical wind turbine. Taking this typical wind turbine as an example, the cut-in wind speed, rated wind speed, and cut-out wind speed are 3m / s, 8.7m / s, and 20m / s, respectively. The hourly wind power capacity factor was obtained by fitting its actual output curve, and the calculation is as follows: Formula (1) in, The wind power capacity factor represents the proportion of the actual output of wind power at the corresponding wind speed to the rated capacity. express The wind speed at the height of the wheel hub (e.g., 90m above the wheel hub); in formula (1) It is derived through fitting. For example, the coefficient fitting results are as follows: =0.00111, b= 0.00291, c= 0.01854, d= 0.11003.

[0058] If the wind speed is lower than the cut-in wind speed, the fan will not start; The wind turbine is in the ramp-up power output stage, and the output increases with the increase of wind speed; When the wind speed reaches or exceeds the rated wind speed, the fan operates at full capacity; If the wind speed exceeds the cut-out wind speed, the fan will shut down for protection.

[0059] The wind power capacity factors, sorted by time, form a wind power capacity factor sequence. Multiplying this sequence by the region's installed wind power capacity for that year yields the wind power output curve. For example, calculating the wind power capacity factors over 8760 hours in a year results in a sequence of 8760 wind power capacity factor values, each ranging from [0,1]. Multiplying each of these 8760 wind power capacity factor values ​​by the region's installed wind power capacity value yields the region's wind power generation over those 8760 hours in that year, creating a wind power output curve containing these 8760 values.

[0060] (2) Obtain the photovoltaic power output curve; The photovoltaic power output calculation model is as follows: calculate The actual amount of irradiance received by the photovoltaic panel at any given time is calculated as follows: Formula (2)

[0061] Formula (3) in, for The diffuse irradiance component coefficient at time t is calculated as follows:

[0062] Formula (4)

[0063] Formula (5) in, , , They are respectively Global downward irradiance, direct irradiance component, and diffuse irradiance component at any given time; for The solar zenith angle at that moment; for The actual amount of irradiance received by the photovoltaic panel at any given time; , They are respectively The tilt angle of the photovoltaic array and the surface albedo at any given time; for The angle of incidence of the photovoltaic panel at time t; excluding the above variables The settings can be customized according to the photovoltaic panel tracking method, while the rest comes from the extreme scenario meteorological data in step S1. Intermediate variables used for ease of calculation have no special physical meaning.

[0064] The actual amount of irradiance received by the photovoltaic panel at each grid point is arranged in time to form a sequence of the actual amount of irradiance received by the photovoltaic panel. Based on each grid point The actual amount of irradiance received by the photovoltaic panel at a given time is calculated. Photovoltaic capacity factor at time ,as follows: Formula (6) in, For photovoltaic depreciation factor; Irradiance under standard test conditions; The power temperature coefficient of a photovoltaic module; Temperature of photovoltaic panel components under standard testing conditions; This represents the actual temperature of the photovoltaic panel module.

[0065] Photovoltaic capacity factors are sorted by time to form a photovoltaic capacity factor sequence. Multiplying this sequence by the regional photovoltaic installed capacity for that year yields the photovoltaic power output curve. For example, calculating the photovoltaic capacity factors for 8760 hours in a year results in a sequence containing 8760 photovoltaic capacity factor values, each ranging from 0 to 1. Multiplying these 8760 photovoltaic capacity factor values ​​by the regional photovoltaic installed capacity value yields the regional photovoltaic power generation for that year's 8760 hours, forming a photovoltaic power output curve containing 8760 values.

[0066] For example, Set to 0.9, and it can be adjusted at any time according to actual needs; The standard value is 1000 W / m². This is an empirical value, set to -0.0046, which can be adjusted at any time according to actual needs.

[0067] Unit installed photovoltaic power generation (That is, the capacity factor). In this embodiment, it is the hourly capacity factor sequence of 8760h photovoltaic.

[0068] Actual temperature of photovoltaic panel modules The calculation is as follows: Formula (7) in, The ambient temperature. For example, The standard value is set to 25℃.

[0069] This refers to the ambient temperature (hourly temperature data). This refers to the temperature of a photovoltaic cell module under standard testing conditions.

[0070] The angle of incidence of the photovoltaic panel at any given time The calculation is as follows:

[0071] Formula (8) in, for The solar azimuth at any given time; for The azimuth angles of the photovoltaic array at each moment are all from the extreme scenario dataset in S1.

[0072] (3) Obtain the load output curve; A temperature-responsive load model is constructed, and the load is calculated as follows: Formula (8) in, , , and These represent the total daily load demand, base load, heating supply, and cooling load within the region, in GW. and The heating and cooling loads representing the temperature response are expressed in GW / ℃. This indicates the indoor temperature index of the building in that area; and These represent the threshold temperatures for cooling and heating in the area, exemplified by 22°C and 14°C respectively. (The text then abruptly shifts to a seemingly unrelated topic: "comparing it with...") Only positive numbers are considered when performing difference calculations; Indicates the average daily temperature; These represent irradiance, wind speed, and humidity relative to [other parameters]. The linear coefficients, for example, ; ; ; , , , They represent the average daily temperature. Daily average irradiance ( Daily average wind speed ( Daily relative humidity ; This represents a control group derived from global experience, for example, , , , .

[0073] Based on historical hourly load data, the above daily load values Decomposed into hourly load curves Historical hourly load data includes normalized hourly base load curves, hourly cooling load curves, and hourly heating load curves corresponding to 24 characteristic days. The curves represent hourly values ​​for 24 hours in a day.

[0074] The 24 characteristic days are divided as follows: ① Based on the above cooling and heating threshold temperatures, the model divides all days into three categories of characteristic days: cooling days, heating days, and mild days. Among them, days with an average daily temperature lower than the cooling threshold temperature are heating days, days with an average daily temperature between the cooling and heating threshold temperatures are mild days, and days with an average daily temperature higher than the heating threshold temperature are cooling days.

[0075] ② Divide all days into working days and non-working days, with non-working days including all statutory holidays.

[0076] ③ Divide all days into spring, summer, autumn, and winter according to the seasons.

[0077] ④ Based on the above three characteristics, all days There are 24 types, and each characteristic day corresponds to a combination of three characteristics: weather, workday status, and season. For example, a certain characteristic day might correspond to the characteristics of a cooling day + workday + summer.

[0078] The daily load value is decomposed into hourly load curves, as follows: For the daily load data obtained by the above formula, all days are first divided into 24 characteristic days; For each type of characteristic day, the corresponding daily total load value Multiply by the hourly base load curve, the hourly cooling load curve, and the hourly heating load curve respectively; superimpose the three curves to obtain the hourly load curves corresponding to all daily loads; Connect all hourly load curves in sequence to obtain the complete annual hourly total load curve.

[0079] The purpose of step S12 is to calculate the output curves and load curves of new energy sources under various extreme scenarios based on meteorological datasets under the influence of climate change and extreme weather, so as to provide basic data for the subsequent calculation of long-term energy storage demand.

[0080] Step S2, specifically.

[0081] A novel long-term energy storage demand calculation model for power systems that considers carbon budgets is constructed. The full-cycle constraint of the carbon budget is transformed into dynamic marginal losses through quota allocation, that is, the impact of the carbon budget is considered in terms of carbon emission losses.

[0082] (1) Objective function of the long-term energy storage demand calculation model for power systems The objective function is to minimize the total system loss, which includes four parts: energy storage loss, system operation loss, thermal power unit start-up and shutdown loss, and environmental loss.

[0083] The objective function of the power system long-term energy storage demand calculation model is as follows: Formula (9) in, For the total system loss, These are energy storage losses, system operation and maintenance losses, thermal power unit start-up and shutdown losses, and environmental losses.

[0084] Calculating energy storage losses requires decoupling the energy capacity, charging power capacity, and discharging power capacity of long-term energy storage.

[0085] The energy storage loss is as follows: Formula (10) in, , and These are the equivalent annual value factor, discount rate, and equipment life for short-term and long-term energy storage, respectively. , and These are the unit charging power loss, unit discharging power loss, and unit capacity loss for long-term energy storage, respectively. , and These are the charging power capacity, discharging power capacity, and energy capacity configurations for long-term energy storage, respectively. System operation and maintenance costs This includes fuel loss, fixed maintenance loss of thermal power units, and operation and maintenance loss of energy storage.

[0086] The system operation and maintenance costs are as follows: Formula (11) in, , and These are respectively: unit fuel loss of thermal power units, unit maintenance loss of thermal power units, and unit variable operation and maintenance loss of long-term energy storage. , These are respectively the installed capacity of thermal power units and thermal power units. Always put in the effort; It is a time set.

[0087] The start-up and shutdown losses of the thermal power units are as follows: Formula (12) in, , These are the starting and stopping losses of thermal power units, respectively. , For thermal power units The number of starts and stops at any given time.

[0088] The environmental losses are as follows: Formula (13) in, , The unit loss required to meet carbon emission limits and The amount of carbon dioxide emissions that exceeds the carbon emission limit at any given time.

[0089] (2) Constraints of the long-term energy storage demand calculation model The constraints of the long-term energy storage demand calculation model include power balance constraints, decision constraints, thermal power unit operation constraints, new energy output constraints, and long-term energy storage constraints. The operating constraints of thermal power units include thermal power unit output constraints, start-stop constraints, and annual utilization hours constraints. The long-term energy storage constraints include upper and lower limits for charging power, SOC state constraints, upper and lower limits for SOC capacity, SOC constraints at the end of the scheduling cycle, discharge power constraints, charging power constraints, and capacity-to-power ratio constraints.

[0090] ① Power balance constraints, as follows: Formula (14) in, , and They represent The output of thermal power units, the output of new energy sources (wind power and photovoltaic), and the load size at all times; , They represent Discharge power and charging power for long-term energy storage.

[0091] ② Decision constraints, as follows: Formula (15) in, , , , These represent the minimum and maximum charging and discharging power of long-term energy storage, respectively.

[0092] ③ Operating constraints for thermal power units include output constraints, start-up and shutdown constraints, and annual utilization hours constraints, as follows: Formula (16) Formula (17) Formula (18) in, This is the start / stop variable for the unit; the variable is 1 when the unit is running and 0 otherwise. , These are the upper and lower limits of the output of thermal power units; , These are the shortest continuous start-up and shutdown times for thermal power units, respectively. , For thermal power units The time elapsed during continuous operation and downtime; , , These represent the annual utilization hours of thermal power units and the upper and lower limits of annual utilization hours of thermal power units, respectively.

[0093] ④ Constraints on new energy output are as follows: Formula (19) in, This refers to the grid connection coefficient of new energy generating units; For a moment The total output of new energy sources (wind power and photovoltaic power) actually dispatched by the power system; For a moment The theoretical available power generation of new energy sources (wind power, photovoltaic).

[0094] ⑤ Long-term energy storage constraints include upper and lower limits for charging power, state of charge (SOC) constraints, upper and lower limits for SOC capacity, SOC constraints at the end of the scheduling cycle, discharge power constraints, charging power constraints, and capacity-to-power ratio constraints, as follows: Formula (20) in, They represent the times at time 1 and 2 respectively. Limitations on charging and discharging power for long-term energy storage; This is the upper limit of the charging power capacity for long-term energy storage; This represents the lower limit of the long-term energy storage discharge power capacity.

[0095] Formula (21) in, , For at any time , Long-term energy storage capacity; For at any time Charging power for long-term energy storage; Charging power for long-term energy storage; Charging power for long-term energy storage; Discharge efficiency for long-term energy storage.

[0096] Formula (22) in, The maximum energy storage capacity limit for long-term energy storage.

[0097] Formula (23) in, At the start of the scheduling period Energy storage capacity; End of scheduling period Energy storage capacity at that time.

[0098] Formula (24) Formula (25) Formula (26) in, , These represent the upper and lower limits of the capacity-to-power ratio of the energy storage system.

[0099] The purpose of step S2 is to construct a long-term energy storage demand calculation model, clarify the objective function and constraints, and provide a key calculation method for subsequent calculation of the long-term energy storage demand of the power system in extreme scenarios under the actual regional carbon budget.

[0100] Step S3, specifically.

[0101] The long-term energy storage demand calculation model of the power system is solved by calling CPLEX through YALMP. The resulting long-term energy storage demand configuration scheme includes the charging power capacity of long-term energy storage corresponding to extreme scenarios. Discharge power capacity With energy capacity .

[0102] Obtain parameters of each thermal power unit and energy storage system in the power system at different time periods; The parameters of each thermal power unit in the power system include the upper and lower limits of output, operating characteristics, and upper and lower limits of gradient rate of the thermal power unit at different times.

[0103] The power system parameters obtained above, along with the wind and solar power output data from the extreme scenario power output curves obtained in S12 and the load data from the load curves, are input into the power system long-term energy storage demand calculation model to calculate the power system long-term energy storage demand under the influence of climate change and extreme weather. Long-term energy storage demand includes charging power, discharging power, and storage duration.

[0104] The optimal function is obtained by calling CPLEX through YALMP. The decision variables obtained include the charging power capacity of long-term energy storage. Discharge power capacity With energy capacity This allows us to clearly understand the changes in long-term energy storage demand under different extreme scenarios.

[0105] Specifically, the objective function in the long-term energy storage demand calculation model can be flexibly selected and customized according to actual costs, and constraints can be added or removed according to actual needs.

[0106] The purpose of step S3 is to summarize the data from steps S1 and S2 and input them into the long-term energy storage demand calculation model to calculate the long-term energy storage demand of the power system under the influence of climate change and extreme weather, quantify the carbon budget, and determine the impact of different degrees of climate change and extreme weather on long-term energy storage demand.

[0107] Example 2: A specific embodiment of the present invention discloses a power system long-term energy storage demand analysis system considering extreme scenario sets, thereby implementing the power system long-term energy storage demand analysis method considering extreme scenario sets in Embodiment 1. The specific implementation methods of each module are as described in the corresponding descriptions in Embodiment 1.

[0108] like Figure 4 As shown, a power system long-term energy storage demand analysis system considering extreme scenario sets is provided. The system includes a scenario construction and data calculation module M1, an optimization model construction module M2, and a solution and configuration output module M3. The scenario construction and data calculation module M1 is used to construct a future extreme scenario meteorological dataset within the region based on historical hourly meteorological data and future meteorological forecast data from the regional center; and to calculate the new energy output curve and load curve under each extreme scenario based on the future extreme scenario meteorological dataset. The optimization model construction module M2 is used to construct a long-term energy storage demand calculation model for the power system that takes into account the carbon budget and aims to minimize the total system loss; wherein, the total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss; the environmental loss is calculated from the carbon emissions that exceed the carbon budget; The solution and configuration output module M3 is used to obtain the thermal power unit parameters and energy storage parameters of the power system at each time period. Based on the new energy output curves and load curves under each extreme scenario, it solves the long-term energy storage demand calculation model of the power system and obtains the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

[0109] Since the system in this embodiment and the method in Embodiment 1 are related and can be referenced from each other, this description is redundant and will not be repeated here. Because this system embodiment shares the same principle as the above method embodiment, it also possesses the corresponding technical effects of the above method embodiment.

[0110] In summary, the long-term energy storage demand analysis method and system for power systems considering extreme scenario sets according to embodiments of the present invention have the following beneficial effects: 1. This invention integrates historical meteorological data with future climate change predictions to systematically construct a set of extreme scenarios covering multiple types and time scales, accurately depicting the supply-demand imbalance under the most unfavorable meteorological conditions such as continuous low renewable energy output and extreme load increases. This method provides a scientific and rigorous boundary condition basis for long-term energy storage configuration, significantly improving the robustness and adaptability of the model in dealing with extreme climate risks; and enhancing the power system's risk response capabilities. 2. Under carbon budget constraints, this solution not only calculates the total energy storage capacity but also analyzes key performance parameters such as energy storage discharge duration and charge / discharge power scale in detail. It establishes the response relationship between these parameters and the intensity, duration, and carbon emission constraints of extreme scenarios, quantifying multi-dimensional energy storage requirements. Compared to traditional single-capacity index assessments, the multi-dimensional parameter outputs are closer to actual engineering design and system operation needs, providing direct and practical decision support for the selection and configuration of energy storage systems. 3. This invention constructs an optimization model aimed at minimizing total system losses, comprehensively considering energy storage investment, operation and maintenance, unit start-up and shutdown, and environmental costs. This solution can scientifically determine the optimal configuration for long-term energy storage while meeting carbon constraints. It helps avoid system instability risks caused by insufficient energy storage configuration or energy waste due to redundant configuration, improving both long-term power system reliability and operational efficiency.

[0111] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0112] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing long-term energy storage demand in power systems considering extreme scenario sets, characterized in that, include: Based on historical hourly meteorological data and future meteorological forecast data from the regional center, a meteorological dataset of future extreme scenarios in the region is constructed. Based on the meteorological dataset of future extreme scenarios, the output curves and load curves of new energy sources under each extreme scenario were calculated respectively. A long-term energy storage demand calculation model for a power system is constructed, taking into account the carbon budget and aiming to minimize the total system loss. The total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss. The environmental loss is calculated from the carbon emissions exceeding the carbon budget. The parameters of thermal power units and energy storage parameters of the power system at each time period are obtained. Based on the output curves and load curves of new energy sources under each extreme scenario, the long-term energy storage demand calculation model of the power system is solved to obtain the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

2. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets as described in claim 1, characterized in that, Constructing a meteorological dataset for future extreme scenarios, including: Acquire historical hourly meteorological data and predicted daily average meteorological data for future scenarios from the regional center; Based on the fluctuation characteristics of historical hourly meteorological data, the predicted daily average meteorological data is extended in time scale to generate future hourly meteorological data; wherein, the future hourly meteorological data includes future hourly wind speed data, solar irradiance data, and temperature data; Based on the aforementioned future hourly meteorological data and historical hourly meteorological data, extreme low-output events are statistically analyzed based on the graded thresholds of wind speed and solar irradiance, resulting in multiple future extreme scenario meteorological datasets with different degrees of extremeness.

3. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 2, characterized in that, The predicted daily average meteorological data is augmented with a time scale to generate future hourly meteorological data, including: For non-negative wind speed or solar irradiance data, calculate the difference sequence between the historical daily average and the future predicted daily average, and superimpose the difference sequence onto the historical hourly data sequence to obtain the future hourly wind speed or solar irradiance sequence. For temperature data containing negative values, the historical hourly temperature data is recombined using the sequence recombination method to obtain the recombined historical hourly temperature sequence; the difference sequence between the recombined historical hourly temperature sequence and the predicted daily average temperature is calculated; the recombined historical hourly temperature sequence is superimposed with the difference sequence to obtain the future hourly temperature sequence.

4. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 3, characterized in that, Based on the aforementioned future extreme scenario meteorological dataset, the renewable energy output curves and load curves under each extreme scenario are calculated, including: Based on the wind speed data in the aforementioned future extreme scenario meteorological dataset, the wind power output curves under each extreme scenario are obtained using the constructed wind power output calculation model. Based on the solar irradiance, temperature and photovoltaic panel parameters of the meteorological data of the future extreme scenarios, the photovoltaic output curves under each extreme scenario are obtained by using the constructed photovoltaic output calculation model. Based on the temperature, irradiance, wind speed, and humidity data in the aforementioned future extreme scenario meteorological dataset, load curves for each extreme scenario are obtained using the constructed temperature-responsive load model.

5. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 1, characterized in that, The objective function of the power system long-term energy storage demand calculation model is as follows: in, For the total system loss, These are energy storage losses, system operation and maintenance losses, thermal power unit start-up and shutdown losses, and environmental losses.

6. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 5, characterized in that, The energy storage loss is as follows: in, , and These are the equivalent annual value factor, discount rate, and equipment life for short-term and long-term energy storage, respectively. , and These are the unit charging power loss, unit discharging power loss, and unit capacity loss for long-term energy storage, respectively. , and These are the charging power capacity, discharging power capacity, and energy capacity configurations for long-term energy storage, respectively. The system operation and maintenance costs are as follows: in, , and These are respectively: unit fuel loss of thermal power units, unit maintenance loss of thermal power units, and unit variable operation and maintenance loss of long-term energy storage. , These are respectively the installed capacity of thermal power units and thermal power units. Always put in the effort; It is a time set.

7. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 6, characterized in that, The start-up and shutdown losses of the thermal power units are as follows: in, , These are the starting and stopping losses of thermal power units, respectively. , For thermal power units The number of starts and stops at any given time; The environmental losses are as follows: in, , The unit loss required to meet carbon emission limits and The amount of carbon dioxide emissions that exceeds the carbon emission limit at any given time.

8. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to claim 5, characterized in that, The constraints of the long-term energy storage demand calculation model include power balance constraints, decision constraints, thermal power unit operation constraints, new energy output constraints, and long-term energy storage constraints. The operating constraints of thermal power units include thermal power unit output constraints, start-stop constraints, and annual utilization hours constraints. The long-term energy storage constraints include upper and lower limits for charging power, SOC state constraints, upper and lower limits for SOC capacity, SOC constraints at the end of the scheduling cycle, discharge power constraints, charging power constraints, and capacity-to-power ratio constraints.

9. The method for analyzing long-term energy storage demand in power systems considering extreme scenario sets according to any one of claims 1-8, characterized in that, The long-term energy storage demand calculation model of the power system is solved by calling CPLEX through YALMP. The resulting long-term energy storage demand configuration scheme includes the charging power capacity of long-term energy storage corresponding to extreme scenarios. Discharge power capacity With energy capacity .

10. A power system long-term energy storage demand analysis system considering extreme scenario sets, characterized in that, The system includes a scene construction and data calculation module M1, an optimization model construction module M2, and a solution and configuration output module M3. The scenario construction and data calculation module M1 is used to construct a future extreme scenario meteorological dataset within the region based on historical hourly meteorological data and future meteorological forecast data from the regional center; and to calculate the new energy output curve and load curve under each extreme scenario based on the future extreme scenario meteorological dataset. The optimization model construction module M2 is used to construct a long-term energy storage demand calculation model for the power system that takes into account the carbon budget and aims to minimize the total system loss; wherein, the total system loss includes energy storage loss, system operation and maintenance loss, thermal power unit start-up and shutdown loss, and environmental loss; the environmental loss is calculated from the carbon emissions that exceed the carbon budget; The solution and configuration output module M3 is used to obtain the thermal power unit parameters and energy storage parameters of the power system at each time period. Based on the new energy output curves and load curves under each extreme scenario, it solves the long-term energy storage demand calculation model of the power system and obtains the long-term energy storage demand configuration scheme corresponding to each extreme scenario.

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