Multi-zone interconnection system reserve capacity optimization method for considering operation risk

Abstract

The invention discloses a method for optimizing the reserve capacity of a multi-zone interconnection system considering operation risks, and relates to the technical field of power systems, and the method comprises the steps: obtaining the unit fault probability and the system net load prediction error of the multi-zone interconnection system in a preset time period in a system fault scene; according to the unit fault probability and the system net load prediction error, the overall standby demand of the system is obtained through calculation; calculating expected power failure loss of the system when judging that the adjustment capability of the overall standby demand of the system does not meet the adjustment demand of the system; according to the overall reserve demand of the system and the expected power failure loss of the system, constructing a reserve capacity distribution robust optimization model of the multi-region interconnection system, and solving by using a column sum constraint algorithm to obtain an optimal reserve capacity configuration result; a multi-area standby resource sharing mechanism is optimized, and safe and stable operation of the system in an extreme scene is guaranteed.

Description

Technical Field

[0001] This application generally relates to the technical field of power systems, and specifically relates to a method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks. Background Art

[0002] In a multi-region interconnected system, each regional power system shares power resources through interconnected lines and can provide backup support during load fluctuations and equipment failures. However, with the complexity and interconnection of power systems, the uncertainties in system operation have increased significantly, including power generation equipment failures, interconnected line interruptions, and load fluctuations, posing major challenges to the safe operation of the power grid.

[0003] Traditional single-region reserve capacity planning methods can no longer meet the development needs of modern power systems. With the rapid growth of new energy generation, especially the access of distributed energy sources with strong volatility such as wind power and photovoltaic power, the reserve capacity requirements faced by power systems are more complex. The intermittency and uncertainty of new energy make it difficult to predict the power generation output, bringing higher uncertainties to the assessment of reserve capacity. In addition, the interconnection between regions increases the coupling of the system. A sudden failure or load fluctuation in one region may have a chain reaction on adjacent regions, further increasing the operation risks of the system. Therefore, we propose a method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks to solve the above problems. Summary of the Invention

[0004] In view of the above defects or deficiencies in the prior art, it is desirable to provide a method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks that optimizes the sharing of reserve resources between regions and improves the system's ability to cope with uncertainties and reliability.

[0005] This application provides a method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks, including the following steps: In the system fault scenario, obtain the unit failure probability and the system net load prediction error of the multi-region interconnected system within a preset time period; and calculate the overall system reserve demand according to the unit failure probability and the system net load prediction error; When it is determined that the regulation ability of the overall system reserve demand does not meet the system regulation demand, calculate the expected power outage loss of the system; According to the overall system reserve demand and the expected power outage loss of the system, construct a distributionally robust optimization model for the reserve capacity of the multi-region interconnected system and use the column sum constraint algorithm to solve it to obtain the optimal reserve capacity configuration result.

[0006] According to the technical solution provided by the embodiment of this application, obtaining the unit failure probability of the multi-region interconnected system within a preset time period specifically includes the following steps: Based on the Markov chain-Monte Carlo method, perform dynamic simulation on the multi-region interconnected system to obtain the Markov chain state transition equation; According to the Markov chain state transition equation, determine the state transition matrix of the unit; the state transition matrix includes the transition probabilities between the unit failure states; According to the state transition matrix, calculate the cumulative transition matrix; the cumulative transition matrix includes the cumulative probability distribution of the state transition matrix; Generate the unit failure probability based on the unit operation state binary variable and the cumulative transition matrix.

[0007] According to the technical solution provided by the embodiment of the present application, the Markov chain state transition equation is: ; Wherein, is the state transition probability, i and j are the specific states to which they belong, is the unit failure probability state at time t, is the cumulative transition times from state i to j at the adjacent time, and N is the total number of transitions.

[0008] According to the technical solution provided by the embodiment of the present application, calculate the unit failure probability according to the following formula: ; Wherein, is the unit failure probability, , are respectively the upper and lower limits of the probability interval of state j, is the cumulative transition probability of the next time period, follows a uniform distribution in the interval [0, 1].

[0009] According to the technical solution provided by the embodiment of the present application, calculate the expected system power outage loss, which specifically includes the following steps: According to the unit failure probability and the system net load prediction error, judge whether the regulation ability of the overall system reserve demand meets the system regulation demand; If the regulation ability does not meet the system regulation demand, then calculate the expected system curtailment cost and the system load shedding cost; Calculate the sum of the expected system curtailment cost and the system load shedding cost to obtain the expected system power outage loss.

[0010] According to the technical solution provided by the embodiment of the present application, calculate the expected system curtailment cost according to the following formula: ; Wherein, is the expected system curtailment cost, is the set of scheduling time phases; is the curtailment penalty cost; is the amount of curtailed power due to the reserved downward reserve capacity not meeting the system regulation requirements in scenario s, is the probability of the unit failure in the actual operation; is the uncertainty set, is the set of generating units in the multi - area interconnected system; Calculate the system load shedding cost according to the following formula: ; where, is the system load shedding cost, is the amount of load shedding due to the reserved upward reserve capacity not meeting the system regulation requirements in scenario s, is the unit load shedding cost.

[0011] According to the technical solution provided by the embodiment of the present application, the objective function is: ; where, M is the optimal reserve capacity configuration result, , and are the unit operation cost, upward and downward reserve costs of the unit respectively, , and are the unit power, upward and downward reserve capacities respectively, is the uncertainty set.

[0012] According to the technical solution provided by the embodiment of the present application, based on the unit failure probability and the system net load prediction error, it is determined that the regulation ability of the overall system reserve demand does not meet the system regulation requirements, which specifically includes the following steps: Calculate the failure scenario probability based on the analytical relationship between the system failure probability and the unit failure probability; In the system failure scenario, determine the unit failure state according to the failure scenario probability; and calculate the initial system reserve demand according to the unit failure state; Superimpose the initial system reserve demand and the net load prediction error to obtain the overall system reserve demand.

[0013] According to the technical solution provided by the embodiment of the present application, according to the unit failure probability and the system net load prediction error, determine whether the regulation ability of the overall system reserve demand meets the system regulation requirements, which specifically includes the following steps: When the unit failure probability is zero and the system net load prediction error is less than zero, it is determined that the reserved downward reserve capacity of the overall system reserve demand does not meet the system regulation requirements; When the unit failure probability or the system net load prediction error is greater than zero, it is determined that the reserved upward reserve capacity of the overall system reserve demand does not meet the system regulation demand.

[0014] According to the technical solution provided by the embodiment of the present application, a distributed robust optimization model for reserve capacity distribution of a multi-area interconnected system is constructed according to the overall system reserve demand and the expected power outage loss of the system, specifically including the following steps: Determine the objective function, constraint conditions and uncertainty set according to the overall system reserve demand and the expected power outage loss of the system; the constraint conditions at least include: unit operation constraints, power balance constraints and line power flow constraints; the uncertainty set is constructed based on the time conservatism and space conservatism of the system fault scenario; Construct a distributed robust optimization model for reserve capacity distribution of a multi-area interconnected system based on the objective function, the constraint conditions and the uncertainty set.

[0015] It can be seen from the above technical solutions that the present application has at least the following beneficial effects: The present application provides a method for optimizing the reserve capacity of a multi-area interconnected system considering operation risks, which includes: obtaining the unit failure probability and the system net load prediction error of the multi-area interconnected system within a preset time period under a system fault scenario; calculating the overall system reserve demand according to the unit failure probability and the system net load prediction error; judging whether the regulation ability of the overall system reserve demand meets the system regulation demand according to the unit failure probability and the system net load prediction error, and if the regulation ability does not meet the system regulation demand, calculating the expected system curtailment cost and the system load shedding cost; calculating the sum of the expected system curtailment cost and the system load shedding cost to obtain the expected power outage loss of the system; determining the objective function, constraint conditions and uncertainty set according to the overall system reserve demand and the expected power outage loss of the system; the constraint conditions at least include: unit operation constraints, power balance constraints and line power flow constraints; the uncertainty set is constructed based on the time conservatism and space conservatism of the system fault scenario; constructing a distributed robust optimization model for reserve capacity distribution of a multi-area interconnected system based on the objective function, the constraint conditions and the uncertainty set; and using the column and constraint algorithm to solve the distributed robust optimization model for reserve capacity distribution of a multi-area interconnected system to obtain the optimal reserve capacity configuration result; the optimal reserve capacity configuration result is used to guide the scheduling of the reserve resources of the multi-area interconnected system at the optimal operation cost.

[0016] By integrating the probability of unit failures and the prediction error of the system's net load, this application quantifies the reserve capacity requirements under multiple scenarios, effectively covering equipment failures, new energy fluctuations, and load uncertainties. It avoids the reserve capacity deviation caused by traditional methods ignoring the risk conduction effect, significantly improving the reliability of the evaluation results. Moreover, a distributionally robust optimization model for reserve capacity in a multi-region interconnected system is constructed. By integrating the unit operating cost, reserve cost, and expected power outage loss through the objective function and efficiently solving it using the column and constraint algorithm, a globally optimal reserve capacity configuration plan is generated under the premise of meeting power balance, unit, and line constraints. The sharing mechanism of reserve resources in multiple regions is optimized, reducing the risk of curtailment and load shedding caused by the high proportion of new energy access, ensuring the safe and stable operation of the system under extreme scenarios, and significantly reducing the comprehensive operating cost of the system. Description of the Drawings

[0017] Other features, objectives, and advantages of this application will become more apparent by reading the detailed description of the non-restrictive embodiments with reference to the following drawings.

[0018] Figure 1 It is a flowchart of the method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks.

[0019] Figure 2 It is a flowchart for obtaining the probability of unit failures in a multi-region interconnected system within a preset time period.

[0020] Figure 3 It is an example diagram of the situation of increasing reserve demand.

[0021] Figure 4 It is an example diagram of the situation of decreasing reserve demand.

[0022] Figure 5 It is an example diagram of the unit operating cost.

[0023] Figure 6 It is an example diagram of the unit reserve cost and the expected power outage loss. Detailed Embodiments

[0024] The following further elaborates on this application in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are merely for explaining the related invention and not for limiting the invention. Additionally, it should be noted that for ease of description, only parts related to the invention are shown in the drawings.

[0025] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will elaborate on this application in detail with reference to the drawings and embodiments.

[0026] For the sake of clear and concise description of the following embodiments, a brief introduction to the related technologies is first given: With the rapid development of the economy and continuous progress of technology, the power system has become increasingly crucial in modern society, and the multi - area interconnected system has become an important framework for power supply. Against this background, the assessment of reserve capacity faces many challenges, which has promoted the birth of the assessment method for reserve capacity of multi - area interconnected systems considering operation risks.

[0027] The multi - area interconnected system realizes the sharing of power resources of each regional power system through interconnected lines, provides backup support during load fluctuations and equipment failures, and greatly improves the reliability and economy of the power system. When the load in a certain area suddenly increases or a power generation device fails, other areas can promptly transmit power to ensure the stability of power supply. However, during the process of the complexity and interconnection of the power system, uncertainties have increased significantly. It is difficult to completely avoid power generation equipment failures. Once a failure occurs, the power generation power will suddenly decrease; interconnected lines may be interrupted due to natural disasters, equipment aging, etc., affecting power transmission between regions; load fluctuations are affected by various factors such as seasons, time, and economic activities, making it difficult to accurately predict. These uncertainties pose huge challenges to the safe operation of the power grid, and the traditional single - area reserve capacity planning method can no longer meet the development needs of modern power systems.

[0028] The rapid growth of new - energy power generation, especially the large - scale access of distributed energy sources with strong volatility such as wind power and photovoltaic power, has made the reserve capacity demand of the power system extremely complex. The intermittency and uncertainty of new energy make it difficult to predict the power generation output, and large fluctuations may occur in a short period of time. When the sun is sufficient or the wind is strong, the new - energy power generation power is relatively high, but when the weather changes, the power generation power will rapidly decline. This requires the power system to reserve sufficient reserve capacity to cope with the fluctuations of new - energy power generation, increasing the difficulty and uncertainty of reserve capacity assessment.

[0029] Although the interconnection between regions has enhanced the coordination ability of the power system, it has also increased the coupling of the system. A sudden failure or load fluctuation in one area can easily be transmitted to adjacent areas through interconnected lines, triggering a chain reaction and further increasing the operation risk of the system. The failure of a power generation device in a certain area resulting in power shortage may cause adjacent areas to increase power output. If the reserve capacity of adjacent areas is insufficient, it may cause its own power supply problems and even threaten the stability of the entire multi - area interconnected system.

[0030] At present, the system complexity brought about by multi-region interconnection has increased, and the fault conduction effect between interconnected regions has been enhanced. Accurately evaluating the reserve requirements and sharing mechanisms of each region remains a difficult problem to be solved urgently. The uncertainty and volatility of new energy are difficult to quantify. After a large number of wind power and photovoltaic power are connected to the grid, the power generation fluctuations are frequent and difficult to predict, making it difficult to accurately evaluate the reserve capacity requirements. The complexity of optimizing the allocation of reserve resources has increased. The interconnected reserve sharing among multiple regions needs to comprehensively consider the resource distribution, operating costs, and reliability requirements of each region. The calculation scale is huge, and traditional evaluation methods are difficult to handle. The risk assessment means are not perfect enough. The fault risk model of the multi-region interconnected system still needs to be deeply studied, and there are still challenges in quantifying and controlling the risk propagation mechanism between interconnected regions.

[0031] In view of this, the embodiments of the present application provide a method for optimizing the reserve capacity of a multi-region interconnected system considering operation risks. This method can be executed by a computing device, and the present application does not limit the execution subject of this method. Specifically, by integrating the unit fault probability and the system net load prediction error, this method quantifies the reserve capacity requirements in multiple scenarios, effectively covering equipment failures, new energy fluctuations, and load uncertainties, avoiding the reserve capacity deviation caused by traditional methods ignoring the risk conduction effect, and significantly improving the reliability of the evaluation results. Moreover, a distributionally robust optimization model for the reserve capacity of the multi-region interconnected system is constructed. By integrating the unit operating cost, reserve cost, and expected power outage loss through the objective function, and combining with the column and constraint algorithm for efficient solution, under the premise of meeting power balance, unit and line constraints, a globally optimal reserve capacity configuration plan is generated, optimizing the multi-region reserve resource sharing mechanism, reducing the risk of curtailment and load shedding caused by high-proportion new energy access, ensuring the safe and stable operation of the system in extreme scenarios, and significantly reducing the overall operating cost of the system.

[0032] In order to make the method for optimizing the reserve capacity of the multi-region interconnected system considering operation risks provided by the embodiments of the present application clearer and easier to understand, the following introduces this method with reference to the accompanying drawings. As Figure 1 shown, this figure is a flowchart of the method for optimizing the reserve capacity of the multi-region interconnected system considering operation risks provided by the embodiments of the present application. This method includes: S100. In the system fault scenario, obtain the unit fault probability and the system net load prediction error of the multi-region interconnected system within a preset time period; and calculate the overall system reserve requirement according to the unit fault probability and the system net load prediction error.

[0033] It should be noted that the system failure scenario refers to the situation where the multi - area interconnected system deviates from the normal operating state during operation due to various uncertain factors. These uncertain factors include power generation equipment failures, such as sudden shutdown of generator sets; interruption of interconnection lines, affecting power transmission between regions; and load fluctuations, such as sudden changes in electricity demand. In these cases, the power balance, power quality, etc. of the system may be affected, and reserve capacity is required to maintain the stable operation of the system. A multi - area interconnected system refers to subsystems in the power system that are independent of each other but connected by tie lines. Each area has its own generator sets, loads, and grid structure. For example, it may be different grid areas within a country, or the interconnection of different national power grids. Each area can independently dispatch, but conducts power exchange and reserve sharing through interconnection lines.

[0034] The probability of unit failure is used to reflect the reliability of power generation equipment. When a unit fails, the generated power will decrease, directly affecting the power supply capacity of the system. If a large - scale generator set fails, the power deficit needs to be filled by reserve capacity, otherwise it may cause insufficient power supply and affect user power consumption. The system net load prediction error is used to reflect the uncertainty of new - energy power generation and the changes in load. New - energy sources such as wind power and photovoltaic power have intermittency and volatility, and it is difficult to accurately predict their power output. There is a deviation between the actual power output and the predicted value. By establishing prediction models (such as time - series analysis, machine - learning models, etc.) to predict the new - energy power generation in future periods, the difference between the actual power generation and the predicted value is the power generation prediction error. At the same time, the load also fluctuates due to various factors (such as time, season, user behavior, etc.), and there is a difference between the predicted value and the actual load. By using a load prediction model (such as regression analysis, neural network, etc.) to obtain the predicted load, the difference between the actual load and the predicted value is the load prediction error. These uncertainties result in errors in the net load prediction, and this error affects the system's demand for reserve capacity. By superimposing the above - mentioned power generation prediction error and load prediction error, the net load prediction error can be obtained. The preset time period can be set according to actual needs.

[0035] In a multi - area interconnected system, uncertainty is an important factor affecting system operation. The probability of unit failure and the system net load prediction error can quantify these uncertainties. By calculating the probability of unit failure, it is possible to clarify the likelihood of different units failing at different times. By analyzing the system net load prediction error, it is possible to understand the degree of uncertainty brought about by new - energy power generation and load changes. This quantification method provides a data basis for accurately evaluating the system's reserve capacity demand and operation risks in the follow - up.

[0036] Furthermore, as Figure 2 shown, obtaining the probability of unit failure of the multi - area interconnected system within the preset time period specifically includes the following steps: S101. Based on the Markov chain - Monte Carlo method, perform dynamic simulation on the multi - area interconnected system to obtain the Markov chain state transition equation.

[0037] Among them, the Markov chain - Monte Carlo (MCMC) method is a technique for obtaining samples from a specific distribution by dynamically simulating a Markov chain. In this process, the sampling distribution of the Markov chain changes as the simulation progresses, and the ultimate goal is to make the simulation sequence converge to a specific stationary distribution. When the number of samplings is large enough, the obtained simulation sequence can be approximately regarded as independent samples from the target distribution. In a multi - area interconnected system, the operating state (normal or faulty) of the unit is a random process that changes over time. By using the MCMC method to perform dynamic simulation on it, the randomness and uncertainty of the unit state changes can be effectively captured.

[0038] Here, the constructed Markov chain state transition equation is used to describe the random process of unit faults, that is, it reflects the relationship between the probability of the unit transferring from one state to another state at a certain moment and the cumulative number of transfers. The Markov chain state transition equation is: ; Among them, is the state transition probability, i and j are the specific states to which they belong (i.e., the specific fault probability states), is the fault probability state of the unit at time t, is the cumulative number of transfers from state i to j at the adjacent moment, and N is the total number of transfers.

[0039] S102. According to the Markov chain state transition equation, determine the state transition matrix of the unit; the state transition matrix includes the transition probabilities between unit fault states.

[0040] Among them, according to the Markov chain state transition equation, combining all the transition probabilities between states constitutes the state transition matrix P: ; In the state transition matrix P, each row represents the probability of the unit transferring from the current state to other various states, and the sum of the elements in each row is 1. This is because the unit must be in a certain state at a certain moment, so the sum of the probabilities of transferring from this state to all possible states is 1.

[0041] The state transition matrix comprehensively describes the possibility of the unit transferring between different fault states. Understanding the law of unit state changes provides key information for predicting the unit fault probability.

[0042] S103. Calculate the cumulative transition matrix according to the state transition matrix; the state transition matrix includes the cumulative probability distribution of the state transition matrix.

[0043] Among them, the cumulative transition matrix contains the cumulative probability distribution of the state transition matrix, which not only considers the probability of one-step transition but also synthesizes the situation of multi-step transitions. This is very important for more accurately analyzing the probability that the unit is in different states within a certain period of time and can provide more comprehensive information for generating the unit failure probability.

[0044] The cumulative transition matrix Q is: ; Among them, The value of . Here, i, j, and m are all state indices.

[0045] S104. Generate the unit failure probability based on the unit operation state binary variable and the cumulative transition matrix.

[0046] Among them, the unit operation state binary variable is used to represent whether the unit is in the operating state or the failure state at a certain moment. Usually, 1 represents the failure state and 0 represents the operating state. Combining it with the cumulative transition matrix can accurately reflect the failure probability situation of the unit under different operating states.

[0047] Here, calculate the unit failure probability according to the following formula: ; Among them, is the unit failure probability, , are respectively the upper and lower limits of the probability interval of state j, is the cumulative transition probability of the next time period, The value of follows a uniform distribution in the interval [0, 1].

[0048] The above unit failure probability formula describes the random transition law of the failure state through the cumulative transition matrix, determines the current state by combining the unit operation state binary variable, and finally generates a specific failure probability value through uniform sampling.

[0049] Among them, the overall system reserve requirement is used to determine the total reserve capacity that the system needs to reserve under various possible failures and load changes to ensure the stability of power supply.

[0050] Furthermore, calculate the overall system reserve requirement according to the unit failure probability and the system net load prediction error, which specifically includes the following steps: Calculate the failure scenario probability based on the analytical relationship between the system failure probability and the unit failure probability; In the system fault scenario, determine the unit fault status according to the fault scenario probability; and calculate the initial system reserve demand according to the unit fault status. Overlay the initial system reserve demand and the net load prediction error to obtain the overall system reserve demand.

[0051] Among them, system faults are often caused by unit faults, and the two are closely related. The unit fault probability reflects the possibility of a single unit failing, while the system fault probability is the probability of the system failing under the combined action of multiple units. The system fault probability is the sum of the single fault probabilities of the operating units, and there is a specific analytical relationship between the two, which is the basis for calculating the fault scenario probability.

[0052] The fault scenario probability represents the probability that a certain unit may fail under actual operating conditions, providing a key basis for subsequent determination of the unit fault status and calculation of the reserve demand. The calculation formula for the fault scenario probability is: ; Among them, represents the probability that a certain unit may fail under actual operating conditions; is the probability that unit g fails at time t in scenario s; is a 0-1 binary variable representing whether the unit is in an operating state; is the set of generating units in the system, and both g and h are unit indices.

[0053] The fault scenario probability provides a probabilistic basis for determining the unit fault status. In actual operation, although it is impossible to accurately predict which units will fail, analysis can be carried out based on the fault scenario probability. Different combinations of unit fault states can be simulated through random sampling in combination with the fault scenario probability. If the fault scenario probability indicates that a certain unit has a high probability of failure in a certain scenario, then the number of times this unit is in a fault state may be relatively large in the simulation. When the unit fault status is determined, the power deficit caused by the unit fault can be calculated, and then the initial system reserve demand can be obtained. If a certain unit fails and its rated power generation cannot be output normally, the system needs additional reserve capacity to make up for this deficit to maintain power balance. Here, the calculation formula for the initial system reserve demand is: ; Among them, is a 0-1 binary variable representing whether the unit is in a fault state, =1 represents the unit is faulty, otherwise it indicates that the unit is in good operating condition.

[0054] The net load prediction error is caused by factors such as the intermittency of new energy power generation and the uncertainty of the load. In the system fault scenario, in addition to considering the power shortage caused by unit failures, the net load prediction error also needs to be considered. Because even when the units are operating normally, the fluctuations in the net load may lead to power imbalance in the system. If the predicted value of the net load is less than the actual value, there will be a situation of insufficient power, and additional reserve capacity is required to meet the load demand.

[0055] Adding the initial system reserve demand and the net load prediction error gives the overall system reserve demand: ; where is the initial system reserve demand, is the net load prediction error.

[0056] S200. When it is determined that the regulation ability of the overall system reserve demand does not meet the system regulation demand, calculate the expected system outage loss.

[0057] It should be noted that based on the unit failure probability and the system net load prediction error, determining that the regulation ability of the overall system reserve demand does not meet the system regulation demand specifically includes the following steps: When the unit failure probability is zero and the system net load prediction error is less than zero, it is determined that the reserved downward regulation reserve capacity of the overall system reserve demand does not meet the system regulation demand; When the unit failure probability or the system net load prediction error is greater than zero, it is determined that the reserved upward regulation reserve capacity of the overall system reserve demand does not meet the system regulation demand.

[0058] When the regulation ability does not meet the demand, calculate the expected system curtailment cost and the system load shedding cost respectively, and the sum of the two is the expected system outage loss. The expected system curtailment cost represents the system curtailment situation caused by the large-scale new energy generation, and the system load shedding cost represents the system load shedding situation caused by unit failures or net load prediction error > 0.

[0059] Here, the reserved downward regulation reserve capacity of the overall system reserve demand refers to the adjustable power capacity pre-planned and reserved in a multi-region interconnected power system to cope with the power surplus situation caused by large-scale new energy generation or load prediction deviation. For example, when no unit in the system fails and the net load prediction error < 0, it means that the new energy power generation may exceed expectations, or the actual load value is lower than the predicted value, and the system power shows a surplus at this time. If the reserved downward regulation reserve capacity of the system is insufficient, there will be a curtailment phenomenon. The existence of this capacity can enable the system to quickly adjust the power generation power when there is a power surplus, avoid waste of electric energy, maintain the power supply and demand balance, and ensure the stable and economic operation of the power system.

[0060] The reserved upward regulation reserve capacity for the overall system reserve requirement refers to the power regulation capacity preset and reserved in the overall system reserve requirement to cope with power shortage problems caused by unit failures or sudden increases in load. For example, when a unit fails, its power generation cannot be output as planned, resulting in a decrease in the system's power supply capacity; or when the net load prediction error > 0, that is, the actual load is greater than the predicted load, the system's power demand increases. In these cases, if the reserved upward regulation reserve capacity is insufficient, load shedding measures need to be taken, affecting the normal power consumption of users. The reserved upward regulation reserve capacity can be put into use in a timely manner when the system's power is insufficient, making up for the power gap, ensuring the power supply reliability of the power system, and meeting the power consumption needs of users.

[0061] The expected curtailment cost of the system is calculated according to the following formula: ; where is the expected curtailment cost of the system, is the set of scheduling time periods; is the curtailment penalty cost; is the curtailment amount caused by the insufficient reserved downward regulation reserve capacity not meeting the system regulation demand under scenario s, is the probability of unit failure in the actual operation; is the uncertainty set, is the set of generating units in the multi-area interconnected system; The load shedding cost of the system is calculated according to the following formula: ; where is the load shedding cost of the system, is the load shedding amount caused by the insufficient reserved upward regulation reserve capacity not meeting the system regulation demand under scenario s, is the unit load shedding cost.

[0062] S300. Based on the overall system reserve requirement and the expected power outage loss of the system, a distributionally robust optimization model for the reserve capacity of the multi-area interconnected system is constructed and solved using the column and constraint generation algorithm to obtain the optimal reserve capacity configuration result.

[0063] It should be noted that the basic framework for constructing the optimization model, the objective function is used to measure the comprehensive index of the system operation cost and power outage loss, the constraint conditions ensure the safety and feasibility of the system operation, and the uncertainty set takes into account the uncertainty of the system failure scenario, making the optimization result more robust.

[0064] The objective function is set to minimize the sum of the system operation cost and the expected power outage loss. Among them, the system operation cost includes the unit operation cost of the generators and the reserve capacity cost. The constraint conditions include the generator operation constraints (such as power constraints, ramping constraints, etc.), the power balance constraint (ensuring power balance in the normal and reserve capacity invocation states of the system), and the line power flow constraint (limiting the line transmission power within a safe range). The uncertainty set is constructed based on the time conservatism and space conservatism of the system fault scenarios, considering the uncertainties in the time and space of the fault occurrence.

[0065] Here, the objective function is: ; Among them, M is the optimal reserve capacity configuration result, , and are the unit operation cost of the generators, the upward and downward reserve costs respectively, , and are the generator powers, the upward and downward reserve capacities respectively, is the uncertainty set.

[0066] The generator operation constraints are: ; ; ; ; ; ; ; Among them, and are the upper and lower limits of the power constraints of the generators; and are the upper and lower ramping constraint limits of the generators; is the power adjustment amount of the generator within the reserve capacity range, which is the actual adjustment made to cope with generator failures and source-load prediction errors.

[0067] The power balance constraint includes the power balance constraints in the normal state and the reserve capacity invocation state, specifically: ; ; Among them, is the set of new energy generators in the system; is the set of load nodes in the system; is the load demand of load node d. In the embodiments of the present invention, no detailed distinction is made between the modeling of the sending-end system and the receiving-end system, and they are uniformly modeled as the above equations. It should be noted that the sending-end system acts as a generating unit in the receiving-end system, and the receiving-end system acts as a load in the sending-end system.

[0068] The line power flow constraints include two forms under normal conditions and under the state of reserve capacity invocation, specifically: ; ; Among them, and are the upper and lower limits of the line transmission power respectively; F is the large-scale generation load transfer factor of the system.

[0069] Construct an uncertainty set, which considers the time conservatism and space conservatism of the system fault scenario, specifically: ; ; Among them, is the system fault state matrix of S×G×T, and its internal elements characterize whether the unit g is in a fault state at time t under scenario s; construct a distributionally robust uncertainty set based on the fault state matrix ; and are the space conservatism limit value and the time conservatism limit value respectively; is the probability parameter of unit fault.

[0070] In addition, by constructing and solving an optimization model, an optimal reserve capacity allocation scheme that minimizes the system operation cost and can cope with operation risks under various constraint conditions is obtained, realizing the reasonable sharing and efficient utilization of multi-region reserve resources.

[0071] Based on the objective function, constraint conditions and uncertainty set, construct a distributionally robust optimization model for the reserve capacity of the multi-region interconnected system. Use the column sum constraint algorithm to solve this model, and finally obtain the optimal reserve capacity allocation result. This result is used to guide the scheduling of the reserve resources of the multi-region interconnected system at the optimal operation cost, ensure that the reserve resources are economically and reliably collected by the power grid and participate in the power grid reserve service, and guarantee the safe and stable operation of the system under extreme scenarios.

[0072] To further verify the effectiveness of the proposed spare capacity distribution robust optimization model for the multi - area interconnected system in this application, the wind and solar data of a certain region in a certain province and the multi - area interconnected system are used to carry out case studies. The multi - area interconnected system is constructed based on the improved IEEE 118 - bus system. The system contains 15 traditional generating units with a rated power of 200 MW and 4 new - energy generating units with a power of 600 MW, and the peak system load reaches 4528 MW.

[0073] When performing simulation and solution, a PC with a configuration of 3.5 GHz CPU and 32 GB of memory is selected. With the help of the MATLAB software platform, the GUROBI solver is used for calculation. Through this simulation environment, key indicators such as the spare capacity demand and operating cost of the system under different scenarios are simulated and analyzed.

[0074] From Figure 3 and Figure 4 the upward and downward regulation spare capacity in each scenario shown, Figure 3 it is clearly shown that after considering the unit failure factor, the upward regulation spare demand of the system increases significantly. This is because once a unit fails, the loss of its generating power needs to be quickly supplemented, otherwise it will affect the stable supply of the power system. Therefore, sufficient upward regulation spare capacity must be reserved to cope with such emergencies. And Figure 4 shows that the downward regulation spare is mainly affected by the source - load prediction error, and in each scenario, the change trend of the downward regulation spare demand is roughly similar. Further comparison shows that the upward regulation spare demand is significantly higher than the downward regulation spare. This is mainly because the equipment failure probability fluctuates within a certain range, and to ensure that the system can maintain stable operation in the face of various failure probabilities and has strong robustness, more upward regulation spare capacity needs to be reserved.

[0075] Then observe Figure 5 and Figure 6 , which respectively reflect the impact of different spatial conservativeness on the system operating cost. As the spatial conservativeness gradually increases, in the day - ahead stage, both the operating cost and the spare cost of the units show an upward trend. This is because a higher spatial conservativeness means that more potential risks need to be considered in the system planning and operation, and measures such as increasing the investment in spare units and improving the operating redundancy of generating equipment may be taken, thus leading to an increase in cost. However, at the same time, the expected power outage loss is gradually decreasing. This shows that by adjusting the spatial conservativeness, the system's response strategy to uncertain factors can be changed. Decision - makers can flexibly adjust the uncertainty set based on this characteristic, optimize the system operating cost while ensuring the reliability of system operation, and achieve a balance between operating economy and risk reliability, providing strong support for the scientific planning and efficient operation of the multi - area interconnected system.

[0076] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solution formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks, characterized in that, It includes the following steps: Under the system fault scenario, obtain the unit fault probability and the system net load prediction error of the multi - area interconnected system within a preset time period; and calculate the overall system reserve demand according to the unit fault probability and the system net load prediction error; When it is determined that the adjustment capacity of the overall system reserve demand does not meet the system adjustment demand, calculate the expected system power outage loss; According to the overall system reserve demand and the expected system power outage loss, construct a distributionally robust optimization model for the reserve capacity of the multi - area interconnected system, and use the column - sum constraint algorithm to solve it to obtain the optimal reserve capacity configuration result.

2. The method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 1, wherein Obtain the unit fault probability of the multi - area interconnected system within a preset time period, which specifically includes the following steps: Based on the Markov chain - Monte Carlo method, perform dynamic simulation on the multi - area interconnected system to obtain the Markov chain state transition equation; According to the Markov chain state transition equation, determine the state transition matrix of the unit; the state transition matrix includes the transition probabilities between unit fault states; According to the state transition matrix, calculate the cumulative transition matrix; the cumulative transition matrix includes the cumulative probability distribution of the state transition matrix; Based on the unit operation state binary variable and the cumulative transition matrix, generate the unit fault probability.

3. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 2, characterized in that, The Markov chain state transition equation is: ； Among them, is the state transition probability, where i and j are specific states, is the failure probability state of the unit at time t, is the cumulative number of transitions from state i to state j at adjacent times, and N is the total number of transitions.

4. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 3, characterized in that Calculate the unit fault probability according to the following formula: ； Among them, is the probability of unit failure, , are respectively the upper and lower limits of the probability interval of state j, is the cumulative transition probability for the next time period, The value of follows a uniform distribution on the interval [0, 1].

5. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 4, characterized in that, Calculate the expected system power outage loss, which specifically includes the following steps: According to the unit fault probability and the system net load prediction error, determine whether the adjustment capacity of the overall system reserve demand meets the system adjustment demand; If the adjustment capacity does not meet the system adjustment demand, then calculate the expected system curtailment cost and the system load shedding cost; Calculate the sum of the expected system curtailment cost and the system load shedding cost to obtain the expected system power outage loss.

6. The method for optimizing the reserve capacity of a multi-area interconnected system considering operation risks according to claim 5, characterized in that Calculate the expected system curtailment cost according to the following formula: ； Among them, is the system-expected curtailment cost, is the set of dispatching time stages; is the curtailment penalty cost; is the curtailment amount caused by the insufficient system regulation demand due to the reserved downward regulation reserve capacity under scenario s, is the probability of the unit failure under the actual operation condition; is the uncertainty set, is the set of generating units in the multi-area interconnected system; Calculate the system load shedding cost according to the following formula: ； Among them, is the system load shedding cost, is the load shedding amount caused by the insufficient reserve upward regulation capacity not meeting the system regulation demand under scenario s, is the unit load shedding cost.

7. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 6, characterized in that The objective function is: ； Among them, M is the optimal reserve capacity allocation result, , and are the unit operating cost of the unit, the upward and downward reserve costs respectively, , and are the unit power, the upward and downward reserve capacities respectively, is the uncertainty set.

8. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 1, characterized in that Calculate the overall system reserve demand according to the unit fault probability and the system net load prediction error, which specifically includes the following steps: Based on the analytical relationship between the system fault probability and the unit fault probability, calculate the fault scenario probability; Under the system fault scenario, according to the fault scenario probability, determine the unit fault state; and according to the unit fault state, calculate the initial system reserve demand; Superimpose the initial system reserve demand and the net load prediction error to obtain the overall system reserve demand.

9. The spare capacity optimization method for a multi-region interconnected system considering operation risks according to claim 1, characterized in that Based on the unit fault probability and the system net load prediction error, determine that the adjustment capacity of the overall system reserve demand does not meet the system adjustment demand, which specifically includes the following steps: When the unit fault probability is zero and the system net load prediction error is less than zero, determine that the reserved downward reserve capacity of the overall system reserve demand does not meet the system adjustment demand; When the unit fault probability or the system net load prediction error is greater than zero, determine that the reserved upward reserve capacity of the overall system reserve demand does not meet the system adjustment demand.

10. A method for optimizing the reserve capacity of a multi - area interconnected system considering operation risks according to claim 1, characterized in that, Based on the overall system reserve requirement and the expected power outage loss of the system, a distributionally robust optimization model for the reserve capacity of the multi - area interconnected system is constructed, which specifically includes the following steps: Based on the overall system reserve requirement and the expected power outage loss of the system, determine the objective function, constraints, and uncertainty set; the constraints at least include: unit operation constraints, power balance constraints, and line power flow constraints; the uncertainty set is constructed based on the time conservatism and space conservatism of the system fault scenarios; Based on the objective function, the constraints, and the uncertainty set, construct a distributionally robust optimization model for the reserve capacity of the multi - area interconnected system.

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