Day-ahead supply risk probabilistic evaluation method and related device for high-proportion clean energy power system considering cross-region cooperation

Abstract

This invention discloses a probabilistic assessment method and related device for day-ahead supply guarantee risks in high-proportion clean energy power systems considering inter-regional collaboration. Belonging to the field of clean energy power generation and power system automation technology, it aims to solve the problem of accurately assessing the amplitude, probability, and time period of supply guarantee risks in inter-regional high-proportion clean energy power systems. Based on a constructed random scenario set of day-ahead prediction errors for renewable energy output at both the sending and receiving ends, this invention establishes a risk assessment model for day-ahead power supply insufficiency and reserve insufficiency considering inter-regional collaboration. It proposes comprehensive probabilistic assessment indicators for supply guarantee risks from aspects such as expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence. This invention considers both the uncertainty of the day-ahead predicted output of renewable energy and the adjustable space of DC interconnection lines under the condition of meeting inter-regional supply guarantee needs, while also taking into account flexible adjustment resources such as hydropower, pumped storage, and energy storage. It can be applied to provincial power grid dispatching departments, providing accurate risk early warning basis and enabling timely implementation of targeted risk defense measures, thus providing technical support for improving the system supply guarantee capacity under the collaboration of sending and receiving ends.

Description

Technical Field

[0001] This invention belongs to the field of clean energy power generation and power system automation technology, and specifically relates to a probabilistic assessment method and related device for day-ahead supply guarantee risk of high-proportion clean energy power systems that considers cross-regional collaboration. Background Technology

[0002] As the proportion of renewable energy installed capacity in the system continues to increase, the uncertainty of renewable energy output has a more prominent impact on system operation, putting pressure on the system's power balance. Due to the day-ahead forecasting error of renewable energy output, if the actual renewable energy output is much lower than the forecast output, even if all conventional thermal power units scheduled to start up on the day-ahead reach their maximum technical output, they may still be unable to meet load demand, thus exposing the system to certain supply security risks.

[0003] Based on the consideration of flexible adjustment resources such as hydropower, pumped storage, and energy storage, the inter-regional DC transmission channels can further tap the mutual assistance potential between the sending and receiving grids, reducing system supply security risks to a certain extent. However, the uncertainty of new energy forecasting errors, coupled with the difficulty in quantitatively analyzing the adjustable space of inter-regional DC interconnections, makes it difficult to accurately assess the amplitude, probability, and time period of supply security risks for the sending and receiving grids. Therefore, it is urgent to propose a probabilistic assessment method for day-ahead supply security risks of high-proportion clean energy power systems suitable for inter-regional collaboration, to achieve accurate quantitative analysis of supply security risks after inter-regional mutual assistance between the sending and receiving grids, enabling dispatching departments to take timely risk prevention measures and improve the system supply security capability under inter-regional collaboration. Summary of the Invention

[0004] The purpose of this invention is to provide a probabilistic assessment method and related apparatus for day-ahead supply risk in high-proportion clean energy power systems that consider inter-regional coordination. Specifically, the technical solution disclosed in this invention is a probabilistic assessment scheme for day-ahead supply risk in high-proportion clean energy power systems that considers inter-regional DC interconnection line mutual assistance. Based on a constructed random scenario set of new energy output prediction errors at both the sending and receiving ends, it establishes a risk assessment model for day-ahead power supply insufficiency and reserve insufficiency considering inter-regional sending and receiving end coordination, and proposes a comprehensive index for probabilistic assessment of supply risk. This invention, taking into account flexible adjustment resources such as hydropower, pumped storage, and energy storage, can effectively consider the adjustable space of DC interconnection lines under the condition of meeting inter-regional supply needs, realizing quantitative analysis of day-ahead supply risk considering inter-regional DC mutual assistance, thereby providing dispatching departments with accurate risk early warning basis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a probabilistic assessment method for day-ahead supply guarantee risks in high-proportion clean energy power systems considering cross-regional collaboration, comprising the following steps: Obtain a random set of scenarios for the day-ahead forecasting error of renewable energy output at both the sending and receiving ends; Based on the day-ahead power supply insufficiency risk assessment model, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is obtained. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of the day-ahead power output prediction error of the sending and receiving end renewable energy is used as input. Combined with the day-ahead load prediction curves of the sending and receiving ends, the renewable energy prediction output curves, the operation mode of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Based on the calculation results, the adjustable space of the DC interconnection line under the inter-regional power supply guarantee demand is calculated. Based on the calculation results, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is derived. Based on the day-ahead reserve insufficiency risk assessment model, the reserve insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is obtained; wherein, the day-ahead reserve insufficiency risk assessment model is based on the day-ahead power supply insufficiency risk assessment model, considers the positive reserve capacity demand of the sending and receiving end power grids, calculates the reserve insufficiency risk before inter-regional DC mutual assistance under the day-ahead prediction error scenario of each new energy source, and derives the reserve insufficiency risk of the sending and receiving end power grids considering the adjustable space of inter-regional DC interconnection lines based on the calculation results. Based on the obtained risks of insufficient power supply and insufficient reserve of the sending and receiving power grids, and combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk, the quantitative analysis results of day-ahead supply guarantee risk considering mutual assistance of inter-regional DC interconnection lines are obtained.

[0006] A further improvement to the technical solution of the present invention lies in that, Sending and receiving power grids in scenarios s Next period t The calculation formula for the risk of insufficient power supply before inter-regional DC mutual assistance is as follows: ; ; In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient power supply before inter-regional DC power exchange; , The sending and receiving power grids are respectively in the time period t The load demand power; For inter-provincial communication lines l During the period t External power; For the sending and receiving end inter-regional DC tie line during the time period t The transmission power; , The sending and receiving power grids are respectively in the time period t The wind power output was predicted to be [amount] days ago; , The sending and receiving power grids are respectively in the time period t The photovoltaic output is currently projected to be [amount missing]. , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Day-ahead forecast error of new energy output; For thermal power units g Maximum technical output; For hydroelectric generator units g Peak power generation capacity; , Pumped storage units g Energy storage power station g During the period t The planned charging and discharging power; , The sending and receiving power grids are respectively in the time period t A collection consisting of thermal power generating units; , These are the collections of hydropower generating units in operation, representing the sending and receiving ends of the power grid. , It is a collection consisting of pumped storage units in the sending and receiving power grids, respectively. , These are collections consisting of power storage stations at the sending and receiving ends of the power grid; , It is a collection consisting of inter-provincial interconnection lines of the sending and receiving power grids.

[0007] A further improvement to the technical solution of the present invention lies in that, The calculation expression for the adjustable space of the DC tie line between the sending and receiving ends to meet the needs of cross-regional power supply is as follows: ; ; In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; This represents the maximum transmission power of the inter-regional DC tie line between the sending and receiving ends.

[0008] A further improvement to the technical solution of this invention lies in that, in the day-ahead power supply insufficiency risk assessment model, the power supply insufficiency risk of the sending and receiving end power grids considering inter-regional DC tie-line mutual assistance is expressed as follows: ; ; In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient power supply after inter-regional DC power exchange.

[0009] A further improvement to the technical solution of this invention lies in that, in the day-ahead reserve shortage risk assessment model, the risk of reserve shortage in the sending and receiving end power grids, considering the adjustable space of inter-regional DC tie lines, is expressed as follows: ; ; In the formula, , The sending and receiving power grids are respectively in the time period t Positive reserve capacity; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient backup capacity before inter-regional DC power exchange; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient backup after inter-regional DC mutual assistance.

[0010] A further improvement of the technical solution of the present invention is that the comprehensive index for probabilistic assessment of the current-day supply guarantee risk includes one or more of the following: expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence. Among them, the expected risk value is obtained by summing the probability-weighted risk of the power supply guarantee under various random scenarios of the sending and receiving power grids; the extreme risk value is obtained by calculating the conditional risk value of the power supply guarantee under various random scenarios of the sending and receiving power grids; the probability of risk occurrence is the sum of the probabilities of random scenarios in which the power supply guarantee risk of the sending and receiving power grids is greater than zero in each time period; and the time period in which the risk occurs is the set of times when the expected value of the power supply guarantee risk of the sending and receiving power grids is greater than zero.

[0011] A second aspect of the present invention provides a probabilistic assessment system for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration, comprising: The random scenario set acquisition module is used to acquire random scenario sets of the day-ahead forecast error of renewable energy output at both the sending and receiving ends. The first risk acquisition module is used to acquire the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines based on the day-ahead power supply insufficiency risk assessment model. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of the day-ahead forecast error of the new energy output at the sending and receiving ends is used as input. Combined with the day-ahead load forecast curves of the sending and receiving ends, the new energy output forecast curves, the operation mode of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Based on the calculation results, the adjustable space of the DC interconnection line under the inter-regional supply guarantee demand is calculated. Based on the calculation results, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is derived. The second risk acquisition module is used to acquire the risk of insufficient reserve of the sending and receiving power grids, taking into account the mutual assistance of inter-regional DC tie lines, based on the day-ahead reserve insufficiency risk assessment model. The day-ahead reserve insufficiency risk assessment model is based on the day-ahead power supply insufficiency risk assessment model, taking into account the positive reserve capacity demand of the sending and receiving power grids, calculating the reserve insufficiency risk before inter-regional DC mutual assistance under the day-ahead prediction error scenario of each new energy source, and deriving the reserve insufficiency risk of the sending and receiving power grids, taking into account the adjustable space of inter-regional DC tie lines, based on the calculation results. The quantitative analysis module is used to obtain the quantitative analysis results of the day-ahead supply guarantee risk, taking into account the mutual assistance of inter-regional DC interconnection lines, based on the obtained risks of insufficient power supply and insufficient reserve of the sending and receiving end power grids, combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk.

[0012] In a third aspect, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of the first aspects of the present invention.

[0013] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of the first aspects of the present invention.

[0014] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, wherein when executed by a processor, the computer instructions implement the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of the first aspects of the present invention.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a probabilistic assessment method for day-ahead power supply risk in high-proportion clean energy power systems considering inter-regional collaboration. On one hand, it establishes a risk assessment model for day-ahead power supply insufficiency and reserve insufficiency under inter-regional transmission-receiving end collaboration. The model is based on a constructed random scenario set of day-ahead power output prediction errors from new energy sources at the transmission and receiving ends. It combines the day-ahead load prediction curves of the transmission and receiving ends, the predicted power output curves of new energy sources, the operating modes of thermal and hydropower, energy storage charging and discharging plans, inter-regional DC trading plans, and positive and negative reserve capacity requirements. This allows the calculation of the risks of power supply insufficiency and reserve insufficiency at the transmission and receiving ends under each random scenario, considering inter-regional DC mutual assistance. On the other hand, it proposes a comprehensive probabilistic assessment index for day-ahead power supply risk under inter-regional transmission-receiving end collaboration, considering aspects such as expected risk value, extreme risk value, probability of risk occurrence, and risk occurrence period. In summary, the method proposed in this invention can take into account the uncertainty of the day-ahead forecast output of new energy sources, and can also consider the adjustable space of DC interconnection lines to meet the needs of cross-regional power supply, based on the flexible adjustment resources such as hydropower, pumped storage and energy storage. It can be applied to provincial power grid dispatching departments to calculate and analyze the severity and probability of risks such as insufficient system reserves and insufficient power supply, and take timely and targeted measures to provide technical support for improving the system's power supply capacity under the coordination of sending and receiving ends. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration, as described in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of a random scenario set of day-ahead prediction error for new energy power output at the sending end, as described in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of a random scenario set of day-ahead prediction error for new energy output at the receiving end in an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of the expected risk value in the risk assessment results of the power grid at the sending end during each time period in an embodiment of the present invention.

[0021] Figure 5 This is a schematic diagram of the extreme risk values ​​in the risk assessment results of the power grid supply guarantee at each time period in an embodiment of the present invention.

[0022] Figure 6This is a schematic diagram illustrating the probability of risk occurrence in the risk assessment results of the power grid supply guarantee at each time period in an embodiment of the present invention.

[0023] Figure 7 This is a schematic diagram of the expected risk value in the risk assessment results of the receiving-end power grid at each time period in an embodiment of the present invention.

[0024] Figure 8 This is a schematic diagram of the extreme risk values ​​in the risk assessment results of the receiving-end power grid at different time periods in an embodiment of the present invention.

[0025] Figure 9 This is a schematic diagram illustrating the probability of risk occurrence in the risk assessment results of the receiving-end power grid at different time periods in an embodiment of the present invention.

[0026] Figure 10 This is a schematic diagram of a probabilistic assessment system for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration, as described in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0029] Please see Figure 1 The present invention provides a probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration, comprising the following steps: Step 1: Obtain a random set of scenarios for the day-ahead prediction error of renewable energy output at both the sending and receiving ends.

[0030] Specifically, the step of generating a random scenario set of the day-ahead forecast error of the sending-end renewable energy output includes: based on the annual historical forecast data of renewable energy in the sending-end power grid and the historical actual output data of the same period, fitting the probability density function of the day-ahead forecast error of the sending-end renewable energy output using a nonparametric Gaussian kernel density function, and generating a random scenario set containing the day-ahead forecast error curve of the sending-end renewable energy output using the Monte Carlo simulation method.

[0031] Specifically, the step of generating a random scenario set of the day-ahead prediction error of the receiving-end renewable energy output includes: based on the annual historical prediction data of renewable energy in the receiving-end power grid and the historical actual output data of the same period, fitting the probability density function of the day-ahead prediction error of the receiving-end renewable energy output using a nonparametric Gaussian kernel density function, and generating a random scenario set containing the day-ahead prediction error curve of the receiving-end renewable energy output using the Monte Carlo simulation method.

[0032] Step 2: Based on the day-ahead power supply insufficiency risk assessment model, obtain the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of day-ahead prediction errors of renewable energy output obtained in Step 1 is used as input. Combined with the day-ahead load prediction curves of the sending and receiving ends, the renewable energy prediction output curves, the operation modes of thermal power and hydropower, the energy storage charging and discharging plans, and the inter-regional DC trading plans, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Then, the adjustable space of the DC interconnection line under the inter-regional supply guarantee demand is calculated to obtain the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines.

[0033] Step 3: Based on the day-ahead reserve shortage risk assessment model, obtain the reserve shortage risk of the sending and receiving end power grids considering the adjustable space of the inter-regional DC tie line; wherein, in the day-ahead reserve shortage risk assessment model, based on step 2, the positive reserve capacity demand of the sending and receiving end power grids is considered, the reserve shortage risk after inter-regional DC mutual assistance in each scenario is calculated, and the reserve shortage risk of the sending and receiving end power grids considering the adjustable space of the inter-regional DC tie line is obtained. Step 4: Based on the obtained risks of insufficient power supply and insufficient reserves of the sending and receiving end power grids considering the adjustable space of inter-regional DC transmission, and combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk, obtain the quantitative analysis results of day-ahead supply guarantee risk considering mutual assistance of inter-regional DC transmission lines; among which, the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk can be constructed from aspects such as expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence.

[0034] In this embodiment of the invention, the specific process of obtaining the random scenario set of the day-ahead prediction error of new energy output at the sending and receiving ends in step 1 above is as follows: Based on the annual historical forecasts and actual power output data of renewable energy in the sending and receiving power grids, the probability density functions of the day-ahead forecast error of renewable energy power output in the sending and receiving power grids are fitted using nonparametric Gaussian kernel density functions, as shown in equations (1) and (2): (1) (2) In the formula, Let be the probability density function of the random variable representing the prediction error; , These are random variables representing the day-ahead forecast error of renewable energy output at the sending and receiving ends, respectively. , These are the 1st and 2nd historical prediction error sequences of new energy sources in the sending and receiving power grids, respectively. i Each sample value; , These are the sample sizes of the historical prediction error sequences of new energy sources in the sending and receiving power grids, respectively. h This is the bandwidth used to control the width and smoothness of the kernel function.

[0035] Based on the probability density functions of the day-ahead prediction error of renewable energy output at the sending and receiving ends calculated using equations (1) and (2), random scenario sets containing a large number of day-ahead prediction error curves of renewable energy output at both the sending and receiving ends are generated using the Monte Carlo simulation method. ;in, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Day-ahead forecast error of new energy output; A set of random scenarios for predicting the power output of new energy sources; T This represents the total number of time periods.

[0036] In this embodiment of the invention, the process of constructing the day-ahead power supply insufficiency risk assessment model for cross-regional power transmission and receiving end collaboration is as follows: First, by combining the day-ahead load forecast curves of the sending and receiving ends, the forecast output curves of new energy sources, the random scenario set of new energy forecast errors, the operation modes of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the risk of insufficient power supply to the sending and receiving end grids under each new energy forecast error scenario is calculated. Then, the adjustable space of the DC tie line is calculated to meet the cross-regional supply guarantee requirements; Finally, the risk of insufficient power supply at the sending and receiving end grids was identified, taking into account the mutual assistance between inter-regional DC interconnection lines. Among them, the risks of insufficient power supply at the sending and receiving ends of the power grid under the day-ahead forecast error scenarios of various new energy sources are shown in equations (3) and (4): (3) (4) In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient power supply before inter-regional DC power exchange; , The sending and receiving power grids are respectively in the time period t The load demand power; For inter-provincial communication lines lDuring the period t External power; For the sending and receiving end inter-regional DC tie line during the time period t The transmission power; , The sending and receiving power grids are respectively in the time period t The wind power output was predicted to be [amount] days ago; , The sending and receiving power grids are respectively in the time period t The photovoltaic output is currently projected to be [amount missing]. , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Day-ahead forecast error of new energy output; For thermal power units g Maximum technical output; For hydroelectric generator units g Peak power generation capacity; , Pumped storage units g Energy storage power station g During the period t The planned charging and discharging power; , The sending and receiving power grids are respectively in the time period t A collection consisting of thermal power generating units; , These are the collections of hydropower generating units in operation, representing the sending and receiving ends of the power grid. , It is a collection consisting of pumped storage units in the sending and receiving power grids, respectively. , These are collections consisting of power storage stations at the sending and receiving ends of the power grid; , This refers to the collection of inter-provincial interconnection lines between the sending and receiving power grids (excluding inter-regional DC interconnection lines between the sending and receiving ends).

[0037] The adjustable space of the DC connection line between the sending and receiving ends to meet the needs of cross-regional supply is shown in equations (5) and (6): (5) (6) In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; This represents the maximum transmission power of the inter-regional DC tie line between the sending and receiving ends.

[0038] The risks of insufficient power supply at the sending and receiving ends of the grid, considering the mutual assistance of inter-regional DC tie lines, under the day-ahead forecast error scenarios of various new energy sources are shown in equations (7) and (8): (7) (8) In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient power supply after inter-regional DC power exchange.

[0039] In this embodiment of the invention, the construction process of the day-ahead reserve shortage risk assessment model for cross-regional sending and receiving end collaboration is as follows: The risk of insufficient system reserve is mainly based on the risk of insufficient power supply, and further considers the positive reserve capacity requirements of the sending and receiving grids. This leads to the risk of insufficient reserve of the sending and receiving grids considering the adjustable space of the inter-regional DC tie line, as shown in equations (9) and (10): (9) (10) In the formula, , The sending and receiving power grids are respectively in the time period t Positive reserve capacity; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient backup capacity before inter-regional DC power exchange; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient backup after inter-regional DC mutual assistance.

[0040] This invention discloses a risk assessment model for day-ahead power supply insufficiency and reserve insufficiency in a high-proportion clean energy power system with cross-regional collaboration. On the one hand, it can take into account the uncertainty of the day-ahead predicted output of new energy sources. By setting different random scenario numbers, it can achieve a reasonable balance between the speed and accuracy of probabilistic assessment of supply guarantee risks. On the other hand, based on the flexible adjustment resources such as hydropower, pumped storage and energy storage, it can take into account the adjustable space of DC interconnection lines under the cross-regional supply guarantee demand, thereby deriving the risk of insufficient power supply and reserve insufficiency of the sending and receiving end grids considering the mutual assistance of cross-regional DC interconnection lines.

[0041] In this embodiment of the invention, the process of probabilistically assessing the comprehensive index for day-ahead supply risk in cross-regional delivery and receiving end collaboration is as follows: Based on the risks of insufficient power supply and insufficient reserves of the sending and receiving grids under various random scenarios and considering the adjustable space of cross-regional DC transmission lines, a comprehensive index for probabilistic assessment of day-ahead supply guarantee risk is proposed from aspects such as expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence, so as to realize the quantitative analysis of day-ahead supply guarantee risk considering mutual assistance of cross-regional DC transmission lines.

[0042] The expected value of supply guarantee risk is used to reflect the average level of the risk of insufficient power supply and insufficient reserves in the sending and receiving power grids. It is mainly a probability-weighted summation of the supply guarantee risk under various random scenarios of the sending and receiving power grids, as shown in equations (11) and (12): (11) (12) In the formula, , The sending and receiving power grids are respectively in the time period t Considering the expected risk value of insufficient power supply after inter-regional DC mutual assistance; , The sending and receiving power grids are respectively in the time period t Considering the expected risk value of insufficient reserve after inter-regional DC mutual assistance; , These are day-ahead forecasting error scenarios for renewable energy in the sending and receiving ends of the power grid. s The probability of occurrence.

[0043] The extreme risk value for power supply guarantee is used to reflect the extreme level of power supply insufficiency and reserve insufficiency risks in the sending and receiving power grids. It mainly calculates the conditional risk value of power supply guarantee risk under various random scenarios in the sending and receiving power grids. The extreme risk values ​​for power supply insufficiency and reserve insufficiency in the sending power grid are shown in equations (13) and (14). The extreme risk values ​​for power supply insufficiency and reserve insufficiency in the receiving power grid are similar.

[0044] (13) (14) In the formula, , For the sending-end power grid during the time period t Extreme risk values ​​for insufficient power supply and insufficient backup; , For the sending-end power grid during the time period t The risk value of insufficient power supply and the risk value of insufficient reserves; Risk confidence level; , These are non-negative auxiliary variables used to calculate the extreme risk values ​​of insufficient power supply and insufficient reserves.

[0045] The probability of supply guarantee risk is used to reflect the likelihood of insufficient power supply and insufficient reserves in the sending and receiving power grids at each time period. It mainly involves selecting random scenarios of supply guarantee risk occurring in the sending and receiving power grids at each time period and summing the probabilities of occurrence of this random scenario set. The probabilities of insufficient power supply and insufficient reserves in the sending power grid at each time period are shown in equations (15) and (16). The probabilities of insufficient power supply and insufficient reserves in the receiving power grid at each time period are similar.

[0046] (15) (16) In the formula, , For the sending-end power grid during the time period t The probability of insufficient power supply risk and the probability of insufficient backup risk.

[0047] The period of occurrence of supply guarantee risk refers to the period during which the power supply and reserve of the sending and receiving power grids are insufficient. It mainly involves statistically analyzing the set of periods during which supply guarantee risks occur in the sending and receiving power grids. The periods of occurrence of insufficient power supply and reserve of the sending power grid are shown in equations (17) and (18). The periods of occurrence of insufficient power supply and reserve of the receiving power grid are similar.

[0048] (17) (18) In the formula, , These are the sets of time periods when the power supply is insufficient and the reserve is insufficient, respectively.

[0049] The comprehensive index for probabilistic assessment of day-ahead power supply risk proposed in this invention includes expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence. It can calculate the severity and probability of risk of insufficient system reserves and insufficient power supply. By setting different extreme risk confidence levels, it can effectively analyze the day-ahead power supply risk of the sending and receiving end power grids under different extreme levels.

[0050] Based on the flexible adjustment resources such as hydropower, pumped storage, and energy storage, the inter-regional DC transmission channels can further tap the mutual assistance potential of the sending and receiving grids, reducing the system supply guarantee risk to a certain extent. However, the uncertainty of new energy prediction errors, coupled with the difficulty in quantitative analysis of the adjustable space of inter-regional DC interconnections, makes it difficult to accurately assess the amplitude, probability, and time period of supply guarantee risks of the sending and receiving grids. Therefore, there is an urgent need to propose targeted technical support for dispatching decisions regarding supply guarantee risk assessment of inter-regional high-proportion clean energy power systems. To this end, in this embodiment of the invention, on the one hand, regarding the day-ahead supply guarantee risk assessment model, based on the constructed random scenario set of day-ahead prediction errors for new energy output at the sending and receiving ends, a day-ahead power supply insufficiency and reserve insufficiency risk assessment model considering inter-regional sending and receiving end collaboration is established. On the other hand, regarding the day-ahead supply guarantee risk assessment indicators, based on the power supply insufficiency and reserve insufficiency risks of the sending and receiving grids under various random scenarios, a comprehensive probabilistic assessment indicator for supply guarantee risk is proposed from aspects such as expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence.

[0051] In summary, this invention proposes a probabilistic assessment method for day-ahead supply risk in high-proportion clean energy power systems with cross-regional transmission and receiving end coordination. This method considers both the uncertainty of the predicted day-ahead output of new energy sources and the adjustable space of DC interconnection lines under the requirement of meeting cross-regional supply needs, while taking into account flexible adjustment resources such as hydropower, pumped storage, and energy storage. It can be applied to provincial power grid dispatching departments to calculate and analyze the severity and probability of risks such as insufficient system reserves and insufficient power supply, and take timely and targeted risk defense measures, providing technical support for improving the system supply guarantee capability under transmission and receiving end coordination.

[0052] Please see Figures 2 to 9 This case study is based on a provincial power grid at a cross-regional power transmission and receiving end. The power structure of this power grid is shown in Table 1. It is connected by a cross-regional DC transmission line with a capacity of 8 million kilowatts. The installed capacity of clean energy accounts for more than 65% and 55% of the total power installed capacity, respectively. The uncertainty of the day-ahead forecast output of new energy sources brings a significant risk to the power grid at the power transmission and receiving end.

[0053] Table 1. Power Supply Structure

[0054] The random scenario set of the day-ahead prediction error of renewable energy output at both the sending and receiving ends is constructed using the method in step 1 of the technical solution of this invention. Setting the number of random scenarios to 200, and superimposing them onto the day-ahead prediction curve of renewable energy output at both the sending and receiving ends on a certain day during a cold wave in the receiving-end power grid in December, the random scenario set of renewable energy output at both the sending and receiving ends can be obtained, such as... Figure 2 , Figure 3 As shown.

[0055] The present invention constructs a risk assessment model for day-ahead power supply insufficiency and reserve insufficiency in cross-regional power transmission and receiving end coordination using steps 2 and 3 of the technical solution. Step 4 of the technical solution calculates comprehensive risk assessment indicators for day-ahead power supply, including expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence. Using the day-ahead forecast scenario, plan, and transaction results for a specific day during a cold wave in the receiving-end power grid in December as input boundary conditions, the day-ahead power supply insufficiency and reserve insufficiency risks of the power transmission and receiving end power grids are obtained.

[0056] Please see Figures 4-6 Table 2 illustrates the risks of insufficient day-ahead power supply and insufficient reserve in the sending-end power grid. For the risk of insufficient power supply in this sending-end power grid, the maximum expected risk is 5.14 MW, and the maximum extreme risk value at a 95% confidence level is 40.23 MW, corresponding to a 5% probability of occurrence at 19:00. For the risk of insufficient reserve in this sending-end power grid, assuming a positive reserve demand of 1300 MW, the maximum expected risk is 65.08 MW, and the maximum extreme risk value at a 95% confidence level is 401.01 MW, corresponding to a 21.5% probability of occurrence, also occurring at 19:00. It can be seen that when the system's reserved positive reserve demand is taken into account, the risk of insufficient system reserve will be significantly higher than the risk of insufficient power supply.

[0057] Table 2. Assessment Results of Comprehensive Indicators for Power Supply Guarantee Risk at the Sending-End Grid

[0058] Please see Figures 7-9 Table 3 illustrates the risks of insufficient day-ahead power supply and reserve inadequacy in the receiving-end power grid. It shows that for the risk of insufficient power supply during the cold wave, the maximum expected value is 15.29MW, and the maximum extreme risk value at 95% confidence level is 105.10MW, corresponding to a probability of occurrence of 16%, occurring at 18:45. For the risk of insufficient reserve during the cold wave, setting the positive reserve demand at 1500MW, the maximum expected value is 210.1MW, and the maximum extreme risk value at 95% confidence level is 933.01MW, corresponding to a probability of occurrence of 56.5%, also occurring at 18:45. This indicates that the receiving-end power grid faces significant risks of insufficient power supply and reserve inadequacy during the cold wave, requiring further exploration of flexible resource adjustment potential and proactive risk mitigation measures to reduce the risk of power supply shortages during peak evening hours.

[0059] Table 3. Assessment Results of Comprehensive Indicators for Power Supply Guarantee Risk at the Receiving End

[0060] In summary, compared with existing solutions, the present invention not only considers the uncertainty of day-ahead power generation forecasts but also takes into account the adjustable capacity of DC interconnection lines to meet inter-regional power supply demands, while also taking into account flexible adjustment resources such as hydropower, pumped storage, and energy storage. This enables probabilistic assessment of day-ahead power supply risks in inter-regional power transmission and receiving end coordination, thereby improving the system's power supply capacity under such coordination. It should be noted that by setting different numbers of random scenarios, the present invention can achieve a reasonable balance between the speed and accuracy of probabilistic assessment of power supply risks; and by setting different extreme risk confidence levels, the present invention can effectively analyze day-ahead power supply risks of the power grid at different extreme levels.

[0061] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0062] Please see Figure 10 In this embodiment of the invention, a probabilistic assessment system for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration is provided, comprising: The random scenario set acquisition module is used to acquire random scenario sets of the day-ahead forecast error of renewable energy output at both the sending and receiving ends. The first risk acquisition module is used to acquire the power supply insufficiency risk of the sending and receiving power grids considering the mutual assistance of inter-regional DC interconnection lines based on the day-ahead power supply insufficiency risk assessment model. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of the day-ahead prediction error of the renewable energy output of the sending and receiving ends is used as input. Combined with the day-ahead load prediction curve of the sending and receiving ends, the renewable energy prediction output curve, the operation mode of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Based on the calculation results, the adjustable space of the DC interconnection line under the inter-regional supply guarantee demand is calculated. Based on the calculation results, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is derived. The second risk acquisition module is used to acquire the risk of insufficient reserve of the sending and receiving grids, taking into account the adjustable space of the inter-regional DC tie line, based on the day-ahead reserve insufficiency risk assessment model. The day-ahead reserve insufficiency risk assessment model, based on the day-ahead power supply insufficiency risk assessment model, further considers the positive reserve capacity demand of the sending and receiving grids, calculates the reserve insufficiency risk before inter-regional DC mutual assistance under the day-ahead prediction error scenario of each new energy source, and derives the risk of insufficient reserve of the sending and receiving grids, taking into account the adjustable space of the inter-regional DC tie line, based on the calculation results. The quantitative analysis module is used to obtain the quantitative analysis results of the day-ahead supply guarantee risk considering the mutual assistance of inter-regional DC interconnection lines, based on the obtained risk of insufficient power supply and insufficient reserve of the sending and receiving end power grids considering the adjustable space of inter-regional DC transmission, combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk.

[0063] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The processor is used to execute program instructions stored in the memory, which stores a computer program, the computer program including program instructions. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the core of terminal computing and control, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used to perform operations of a probabilistic assessment method for day-ahead supply risk of high-proportion clean energy power systems considering cross-regional collaboration.

[0064] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It should be noted that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores both the terminal operating system and one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the probabilistic assessment method for day-ahead supply risks of high-proportion clean energy power systems considering cross-regional collaboration in the above embodiments.

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, optical storage, etc.) containing usable program code.

[0066] This invention is described using flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device for execution, forming a configuration for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that specifies a function in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium for directing a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment in order to achieve the desired process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still make modifications or equivalent substitutions to the specific implementation of the present invention; any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A probabilistic assessment method for day-ahead supply guarantee risk of a high-proportion clean energy power system considering cross-regional collaboration, characterized in that, Includes the following steps: Obtain a random set of scenarios for the day-ahead forecasting error of renewable energy output at both the sending and receiving ends; Based on the day-ahead power supply insufficiency risk assessment model, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is obtained. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of the day-ahead power output prediction error of the sending and receiving end renewable energy is used as input. Combined with the day-ahead load prediction curves of the sending and receiving ends, the renewable energy prediction output curves, the operation mode of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Based on the calculation results, the adjustable space of the DC interconnection line under the inter-regional power supply guarantee demand is calculated. Based on the calculation results, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is derived. Based on the day-ahead reserve insufficiency risk assessment model, the reserve insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is obtained; wherein, the day-ahead reserve insufficiency risk assessment model is based on the day-ahead power supply insufficiency risk assessment model, considers the positive reserve capacity demand of the sending and receiving end power grids, calculates the reserve insufficiency risk before inter-regional DC mutual assistance under the day-ahead prediction error scenario of each new energy source, and derives the reserve insufficiency risk of the sending and receiving end power grids considering the adjustable space of inter-regional DC interconnection lines based on the calculation results. Based on the obtained risks of insufficient power supply and insufficient reserve of the sending and receiving power grids, and combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk, the quantitative analysis results of day-ahead supply guarantee risk considering mutual assistance of inter-regional DC interconnection lines are obtained.

2. The probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in claim 1, characterized in that, Sending and receiving power grids in scenarios s Next period t The calculation formula for the risk of insufficient power supply before inter-regional DC mutual assistance is as follows: ； ； In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient power supply before inter-regional DC power exchange; , The sending and receiving power grids are respectively in the time period t The load demand power; For inter-provincial communication lines l During the period t External power; For the sending and receiving end inter-regional DC tie line during the time period t The transmission power; , The sending and receiving power grids are respectively in the time period t The wind power output was predicted to be [amount] days ago; , The sending and receiving power grids are respectively in the time period t The photovoltaic output is currently projected to be [amount missing]. , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Day-ahead forecast error of new energy output; For thermal power units g Maximum technical output; For hydroelectric generator units g Peak power generation capacity; , Pumped storage units g Energy storage power station g During the period t The planned charging and discharging power; , The sending and receiving power grids are respectively in the time period t A collection consisting of thermal power generating units; , These are the collections of hydropower generating units in operation, representing the sending and receiving ends of the power grid. , It is a collection consisting of pumped storage units in the sending and receiving power grids, respectively. , These are collections consisting of power storage stations at the sending and receiving ends of the power grid; , It is a collection consisting of inter-provincial interconnection lines of the sending and receiving power grids.

3. The probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in claim 2, characterized in that, The calculation expression for the adjustable space of the DC tie line between the sending and receiving ends to meet the needs of cross-regional power supply is as follows: ； ； In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; This represents the maximum transmission power of the inter-regional DC tie line between the sending and receiving ends.

4. The probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in claim 3, characterized in that, In the aforementioned day-ahead power supply insufficiency risk assessment model, the power supply insufficiency risk of the sending and receiving end power grids, considering the mutual assistance of inter-regional DC tie lines, is expressed as follows: ； ； In the formula, , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient power supply after inter-regional DC power exchange.

5. The probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in claim 4, characterized in that, In the aforementioned day-ahead reserve shortage risk assessment model, the risk of reserve shortage in the sending and receiving end power grids, considering the adjustable space of inter-regional DC tie lines, is expressed as follows: ； ； In the formula, , The sending and receiving power grids are respectively in the time period t Positive reserve capacity; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Risk of insufficient backup capacity before inter-regional DC power exchange; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Available cross-regional DC tie-line mutual power; , These are the sending-end and receiving-end power grids in different scenarios. s Next period t Consider the risk of insufficient backup after inter-regional DC mutual assistance.

6. The probabilistic assessment method for day-ahead supply guarantee risk of a high-proportion clean energy power system considering cross-regional collaboration as described in claim 5, characterized in that, The aforementioned comprehensive indicators for probabilistic assessment of supply security risks include one or more of the following: expected risk value, extreme risk value, probability of risk occurrence, and time period of risk occurrence. Among them, the expected risk value is obtained by summing the probability-weighted risk of the power supply guarantee under various random scenarios of the sending and receiving power grids; the extreme risk value is obtained by calculating the conditional risk value of the power supply guarantee under various random scenarios of the sending and receiving power grids; the probability of risk occurrence is the sum of the probabilities of random scenarios in which the power supply guarantee risk of the sending and receiving power grids is greater than zero in each time period; and the time period in which the risk occurs is the set of times when the expected value of the power supply guarantee risk of the sending and receiving power grids is greater than zero.

7. A probabilistic assessment system for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration, characterized in that, include: The random scenario set acquisition module is used to acquire random scenario sets of the day-ahead forecast error of renewable energy output at both the sending and receiving ends. The first risk acquisition module is used to acquire the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines based on the day-ahead power supply insufficiency risk assessment model. In the day-ahead power supply insufficiency risk assessment model, the random scenario set of the day-ahead forecast error of the new energy output at the sending and receiving ends is used as input. Combined with the day-ahead load forecast curves of the sending and receiving ends, the new energy output forecast curves, the operation mode of thermal power and hydropower, the energy storage charging and discharging plan and the inter-regional DC trading plan, the power supply insufficiency risk of the sending and receiving end power grids before inter-regional DC mutual assistance is calculated in each scenario. Based on the calculation results, the adjustable space of the DC interconnection line under the inter-regional supply guarantee demand is calculated. Based on the calculation results, the power supply insufficiency risk of the sending and receiving end power grids considering the mutual assistance of inter-regional DC interconnection lines is derived. The second risk acquisition module is used to acquire the risk of insufficient reserve of the sending and receiving power grids, taking into account the mutual assistance of inter-regional DC tie lines, based on the day-ahead reserve insufficiency risk assessment model. The day-ahead reserve insufficiency risk assessment model is based on the day-ahead power supply insufficiency risk assessment model, taking into account the positive reserve capacity demand of the sending and receiving power grids, calculating the reserve insufficiency risk before inter-regional DC mutual assistance under the day-ahead prediction error scenario of each new energy source, and deriving the reserve insufficiency risk of the sending and receiving power grids, taking into account the adjustable space of inter-regional DC tie lines, based on the calculation results. The quantitative analysis module is used to obtain the quantitative analysis results of the day-ahead supply guarantee risk, taking into account the mutual assistance of inter-regional DC interconnection lines, based on the obtained risks of insufficient power supply and insufficient reserve of the sending and receiving end power grids, combined with the comprehensive index of probabilistic assessment of day-ahead supply guarantee risk.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The system includes computer instructions that, when executed by a processor, implement the probabilistic assessment method for day-ahead supply risk of a high-proportion clean energy power system considering cross-regional collaboration as described in any one of claims 1 to 6.

Images