Capacity allocation method and system considering day-ahead market coupling uncertainty

Abstract

The present invention discloses a capacity allocation method and system considering the uncertainty of day-ahead market coupling, and relates to the technical field of power market. The method includes obtaining historical sample data of new energy output, constructing a probability distribution uncertainty set using the historical sample data and determining the relationship between the safety margin capacity and the risk of power abandonment and load shedding; establishing a safety margin capacity decision model based on the relationship between the safety margin capacity and the risk of power abandonment and load shedding, linearizing and solving the safety margin capacity decision model to obtain the remaining available margin value in the current area; establishing an inter-regional capacity allocation model based on the uncertainty of new energy output, solving the inter-regional capacity allocation model in consideration of the remaining available margin value, and obtaining the final capacity allocation plan. The present invention finally obtains a more accurate capacity allocation strategy by combining the day-ahead market information provided by the TSO with the power flow equation.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a capacity allocation method and system considering the uncertainty of day-ahead market coupling. Background Art

[0002] The flow based market coupling method (FBMC) can be considered as a combination of the ATC method and the node pricing method. It takes into account the physical constraints of regional transmission lines and retains market partitions to simplify the grid topology. The network topology under FBMC only considers the physical constraints of key lines, and each transmission system operator (TSO) defines key network elements for its jurisdiction. Unlike the ATC-based market coupling method, FBMC not only considers the commercial exchange between the two connected market partitions, but also considers all market partitions, so the exchanged electricity between the two market partitions is no longer independent.

[0003] However, the congestion management and price prediction of the existing FBMC model in the day-ahead coupled market stage are more complicated. Due to the uncertainty of the day-ahead market, the current FBMC model is less accurate and cannot accurately achieve reasonable capacity allocation. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a capacity allocation method and system that takes into account the uncertainty of day-ahead market coupling, and finally obtain a capacity allocation strategy by combining the day-ahead market information provided by the TSO with the power flow equation.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A first aspect of the present invention provides a capacity allocation method considering the uncertainty of day-ahead market coupling, comprising the following steps:

[0007] Obtain historical sample data of renewable energy output, and use the historical sample data to construct a probability distribution uncertainty set by estimating the confidence band of the cumulative probability distribution function through probability intervals;

[0008] Determine the relationship between safety margin capacity and risk of power curtailment and load shedding based on the boundary of the uncertainty set of probability distribution;

[0009] A safety margin capacity decision model is established based on the relationship between the safety margin capacity and the risk of power curtailment and load shedding. The safety margin capacity decision model is linearized and solved to obtain the remaining available margin value in the current area.

[0010] Based on the uncertainty of renewable energy output, an inter-regional capacity allocation model is established. Taking the remaining available margin value into consideration, the inter-regional capacity allocation model is solved to obtain the final capacity allocation plan.

[0011] Furthermore, the specific steps of constructing the probability distribution uncertainty set by using historical sample data to estimate the confidence band of the cumulative probability distribution function through probability intervals are as follows:

[0012] For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point;

[0013] Construct the confidence band of the entire cumulative probability distribution function based on the cumulative probability distribution function boundary at each point;

[0014] The entire confidence band is formed using numerical calculation methods to obtain the uncertainty set of probability distribution.

[0015] Furthermore, the specific steps for determining the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the boundary of the probability distribution uncertainty set are as follows:

[0016] According to the definition of wind power acceptance risk, considering the uncertainty of wind power probability distribution, the wind power acceptance risk index corresponding to insufficient wind power generation at a given node is expressed as a function;

[0017] According to the different indicator requirements of the system, the transmission system operator adjusts the size of the safety margin capacity retained for the inter-regional interconnection lines, and determines the relationship between the safety margin capacity and the risk of power abandonment and load shedding based on changes in probability distribution.

[0018] Furthermore, the objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

[0019] Furthermore, the constraints of the objective function of the safety margin capacity decision model include: base point power balance constraint, conventional unit capacity constraint, renewable energy output acceptable range constraint, flexibility demand constraint, regional line transmission capacity constraint and safety margin capacity constraint.

[0020] Furthermore, the objective function of the inter-regional capacity allocation model is to maximize the overall social welfare.

[0021] Furthermore, an inter-regional capacity allocation model is established based on the uncertainty of renewable energy output, specifically allocating the uncertainty of renewable energy output to each conventional power generation unit according to different participation factors.

[0022] Furthermore, the constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints, and inter-regional line transmission capacity constraints.

[0023] Furthermore, the capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The day before the inter-regional power exchange delivery, based on the capacity allocation plan and the bidding situation of market participants, the power market is cleared one day before the operation day to generate a detailed power generation plan.

[0024] A second aspect of the present invention provides a capacity allocation system considering the day-ahead market coupling uncertainty, comprising:

[0025] A data acquisition and processing module is configured to acquire historical sample data of renewable energy output, and construct a probability distribution uncertainty set by using the historical sample data to estimate the confidence band of the cumulative probability distribution function through probability intervals;

[0026] A risk threshold determination module is configured to determine the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the boundary of the probability distribution uncertainty set;

[0027] The safety decision module is configured to establish a safety margin capacity decision model according to the relationship between the safety margin capacity and the risk of power abandonment and load shedding, linearize and solve the safety margin capacity decision model, and obtain the remaining available margin value of the current area;

[0028] The inter-regional capacity allocation module is configured to establish an inter-regional capacity allocation model based on the uncertainty of the output of new energy sources, solve the inter-regional capacity allocation model in consideration of the remaining available margin value, and obtain a final capacity allocation plan.

[0029] Furthermore, the data acquisition and processing module also includes a data processing module, which is used to construct a probability distribution uncertainty set by using historical sample data to estimate the confidence band of the cumulative probability distribution function through probability intervals:

[0030] For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point;

[0031] Construct the confidence band of the entire cumulative probability distribution function based on the cumulative probability distribution function boundary at each point;

[0032] The entire confidence band is formed using numerical calculation methods to obtain the uncertainty set of probability distribution.

[0033] Furthermore, the risk threshold determination module is also used to:

[0034] According to the definition of wind power acceptance risk, considering the uncertainty of wind power probability distribution, the wind power acceptance risk index corresponding to insufficient wind power generation at a given node is expressed as a function;

[0035] According to the different indicator requirements of the system, the transmission system operator adjusts the size of the safety margin capacity retained for the inter-regional interconnection lines, and determines the relationship between the safety margin capacity and the risk of power abandonment and load shedding based on changes in probability distribution.

[0036] Furthermore, in the safety decision module, the objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

[0037] Furthermore, in the safety decision module, the constraints of the objective function of the safety margin capacity decision model include: base point power balance constraints, conventional unit capacity constraints, new energy output acceptable range constraints, flexibility demand constraints, regional line transmission capacity constraints and safety margin capacity constraints.

[0038] Furthermore, in the inter-regional capacity allocation module, the objective function of the inter-regional capacity allocation model is to maximize the overall social welfare.

[0039] Furthermore, in the inter-regional capacity allocation module, an inter-regional capacity allocation model is established based on the uncertainty of renewable energy output, specifically, the uncertainty of renewable energy output is allocated to each conventional power generation unit according to different participation factors.

[0040] Furthermore, in the inter-regional capacity allocation module, the constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints and inter-regional line transmission capacity constraints.

[0041] Furthermore, the capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The day before the inter-regional power exchange delivery, based on the capacity allocation plan and the bidding situation of market participants, the power market is cleared one day before the operation day to generate a detailed power generation plan.

[0042] One or more of the above technical solutions have the following beneficial effects:

[0043] The present invention discloses a capacity allocation method and system considering the uncertainty of day-ahead market coupling, which uses the FBMC method to start from the baseline scenario and the day-ahead market clearing stage, and transforms the complex probability problem into a computable deterministic problem through a parameterized uncertainty model. By internalizing the influence of the grid operation base point and the dispatch instruction allocation, the proposed method can not only more accurately consider the influence of the uncertainty of renewable energy output and avoid the common problems of robust optimization methods, but also realize the efficient allocation of the capacity of key network components between regions.

[0044] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] Figure 1 This is a flow chart of a capacity allocation method considering the uncertainty of day-ahead market coupling in Embodiment 1 of the present invention;

[0047] Figure 2 Schematic diagram of CDF confidence interval at a certain sampling point in Embodiment 1 of the present invention;

[0048] Figure 3 Schematic diagram of confidence bands of the cumulative probability distribution function in Embodiment 1 of the present invention;

[0049] Figure 4 Schematic diagram of a step interpolation method in Embodiment 1 of the present invention;

[0050] Figure 5 Schematic diagram of an uncertain set of probability distribution in Embodiment 1 of the present invention;

[0051] Figure 6 Schematic diagram of probability density function corresponding to the boundary in the first embodiment of the present invention;

[0052] Figure 7 It is a schematic diagram of the relationship between FRM and wind power acceptance risk in the first embodiment of the present invention;

[0053] Figure 8 It is a schematic diagram of the linearization of the cumulative probability distribution function in the first embodiment of the present invention. DETAILED DESCRIPTION

[0054] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0056] Terminology explanation:

[0057] Market partition: All nodes in the power system are grouped into NZ compact and non-overlapping market partitions. Each market partition represents an independent power market area. The division of market partitions is usually based on policy regulations, geographical location, administrative regions or the physical structure of the power grid, which helps to manage and coordinate the production, transmission, distribution and consumption of power systems between and within regions.

[0058] CNEs: CNEs are network elements (inter-regional transmission lines, regional internal lines) that are significantly affected by inter-regional market transactions in the power system. CNEs are directional. In the FBMC model, only selected critical lines are subject to flow constraints. The definition of CNEs indicates that a certain proportion of transaction power will flow through this branch in regional power market transactions. Therefore, the selection of CNEs can be determined by the absolute value of the difference in the power transfer distribution factor PTDF of the interconnected area to the tie line, as follows:

[0059] .

[0060] in, For custom values, The value of determines which network elements can be identified as CNEs. If the value is set too low, some lines in the region will also be identified as CNEs. Although this can effectively prevent network congestion in the region, the redundancy of line capacity restrictions may lead to a reduction in overall social welfare. If the value is set too high, some lines related to congestion management may be ignored, resulting in serious network congestion. and It represents the power transfer distribution factor of the key network element c for the two market partitions z1 and z2 respectively. Z is a set of regions. In the present invention, a region is an independent market partition, so it can represent the set of all market partitions.

[0061] At present, TSOs in European countries mostly adopt the following principles in selecting CNEs:

[0062] Inter-regional contact lines will certainly be chosen;

[0063] In-region lines Should be above a certain threshold (e.g. 5%).

[0064] In addition, critical network elements (CNEs) include not only lines in normal state, but for each network element identified as a CNE, the TSO will identify a set of N-1 or N-2 state failure scenarios (CNECs), i.e., unplanned outages of critical network elements, which have a significant impact on the power flow on the CNE.

[0065] Net Position: Net Position of a region represents the sum of the injected power of all nodes in market partition z. , partition z is the area of ​​net power outflow; if , partition z is the area where power flows in net. The net power changes of all partitions constitute the column vector

[0066] Power generation transfer coefficient (Generation Shift Key), characterization The best prediction of the power change map injected into the node is the bridge to establish the connection between the node PTDF and the regional PTDF.

[0067] (1)

[0068] Among them, the column vector Characterizes the change in power injected into each node.

[0069] The calculation methods of GSK mainly include the average strategy, the unit forecast capacity strategy and the unit marginal capacity strategy. The unit marginal capacity strategy is to obtain the power size of each unit by clearing the node pricing model in the basic case. The maximum capacity of the unit minus the clearing power size is the marginal capacity of the unit. The GSK of the region is obtained by weighting each node in the region by the size of the marginal unit capacity. Compared with the average strategy and the unit forecast capacity strategy, the unit marginal capacity strategy is more accurate in taking GSK values. However, this strategy has higher requirements on the reliability of the clearing results. Only when the marginal units cleared by the node pricing model are consistent with the marginal units in the actual power grid operation, the GSK will be more accurate. From region z to node n The calculation formula is as follows:

[0070] (2).

[0071] in, , , and They respectively represent the maximum power generation capacity, maximum load capacity, node power generation obtained by clearing the node pricing model, and node load obtained by clearing at node n in region z.

[0072] Regional power transfer distribution factor: Regional power transfer distribution factor , characterization The FBMC method focuses on two key issues: first, how does inter-regional power trading affect the power flow on the transmission line, and second, how much power flow changes caused by inter-regional power trading can each transmission line accommodate. Realize each area Mapping to accurate description of CNEs power flows to ensure power system reliability and security.

[0073] Regional power transfer distribution factor matrix The generation transfer coefficient GSK and the node power transfer distribution factor matrix can be used The calculation is as follows:

[0074] (3).

[0075] Among them, N c Represents the total number of key network components, N N represents the total number of nodes, GSK represents the power generation transfer coefficient, represents the regional power transfer distribution factor matrix, Represents the node power transfer distribution factor matrix.

[0076] Remaining available margin: Remaining available margin , represents the line transmission capacity that can be used for regional day-ahead market transactions in the direction of each key network element under the premise of ensuring safe and reliable operation of the power grid. , represents the reliability margin of line capacity used to cope with the impact of uncertainty on CNEs flow, which is usually determined based on the observation of CNEs flow from the predicted baseline value to the actual dispatch value, to ensure the safety and reliability of the power grid during actual operation. Final adjustment value , represents the remaining available margin RAM value that TSO increases or decreases on CNEs based on operational experience, reflecting remedial measures at dispatch points or other complex system safety considerations. For each CNE, the relationship between the three is as follows:

[0077] (4)

[0078] (5).

[0079] in, is the remaining available margin, is the final adjustment value, is the safety margin capacity, Nc represents the total number of key network components, is the maximum transmission capacity vector on CNEs, It is the reference line flow vector, which mainly includes the intra-regional or inter-regional trading capacity outside the spot market such as medium- and long-term electricity contracts under the baseline scenario, ensuring the feasibility of medium- and long-term market transactions such as futures and bilateral contracts, and avoiding the interference of day-ahead and real-time market transactions on medium- and long-term transactions. and They represent the predicted values ​​of the power flow of each key network component and the predicted values ​​of the net power change of the market segment, Represents the regional power transfer distribution factor matrix.

[0080] In addition, TSOs often set a minimum remaining available margin based on policies and regulations. To ensure that after considering the uncertainty of grid operation and the approximation error of the model, CNEs still have sufficient capacity for inter-regional power trading. Therefore, in practical applications, RAM is expressed as:

[0081] (6).

[0082] The final result of available commercial exchange capacity (between market sectors) is:

[0083] (7)

[0084] in, Refers to the net power change in the market area on the day-ahead.

[0085] Explanation of index related parameters in the present invention:

[0086] i represents the index of the conventional unit, N G represents the total number of conventional units, and G represents the set of conventional units;

[0087] t represents the time index, and T represents the time step of the market clearing calculation (for example, every 1 hour or 15 minutes is a decision point);

[0088] n represents the node index, N N represents the total number of nodes, and N represents the node set;

[0089] z represents the region index, N Z represents the total number of regions, and Z represents the set of regions;

[0090] l represents the line index, N L represents the total number of lines, and L represents the line set;

[0091] c represents the key network element index, N c represents the total number of key network elements, and C represents the set of key network elements.

[0092] Represents a real number.

[0093] Embodiment 1:

[0094] Embodiment 1 of the present invention provides a capacity allocation method considering the uncertainty of day-ahead market coupling, using the FBMC method, and the FBMC clearing method is intended to allocate commercial exchange capacity between adjacent power market partitions. It is coordinated by the local transmission system operator (TSO) and the power trading agency, and centralized clearing is carried out. Figure 1 The main contents of the FBMC clearing method are presented from multiple aspects, including time, stakeholders, input and output. The capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The power market is cleared one day before the operation date based on the capacity allocation plan and the bidding of market participants, and a detailed power generation plan is generated.

[0095] In a specific implementation, for any operation day D, the FBMC method is divided into D-2 baseline scenario, D-1 day-ahead market clearing, and D-day intraday market clearing and congestion management from the time dimension. The present invention focuses on the day-ahead stage, i.e., the margin capacity decision and allocation of the D-2 and D-1 stage markets.

[0096] like Figure 1 As shown, the FBMC clearing process starts at the D-2 day baseline scenario. The so-called baseline scenario is the "best estimate" of the power system state at the time of delivery, also known as the D-2 congestion forecast, which is jointly determined by simplified network characteristics, forecast information, and relevant policy regulations one day before the day-ahead market clearing (D-1). The power flow determined under the baseline scenario can be directly used to calculate the commercial exchange of inter-regional transmission capacity. The calculated market coupling-related parameters are of great significance to the reliability of the feasible domain decision-making of the day-ahead and intra-day trading stages. The more accurate the parameter calculation, the less congestion will be generated in the market area, and more social welfare will be generated.

[0097] Two days before the inter-regional power exchange delivery, TSO will use the corresponding model to form a next-day congestion forecast file based on a large amount of historical data, real-time monitoring data and production experience. The file contains data and analysis of network topology, regional load forecast, power generation forecast, transmission capacity assessment and other related factors. It is mainly used internally by TSO to help it with daily scheduling and decision-making.

[0098] In the baseline scenario, TSO uses the capacity allocation model between regions to decide key parameters of cross-regional power trading, such as regional power transfer distribution factors and remaining available margins. Its input parameters mainly include network topology, predicted operating base point, power generation transfer coefficient, safety margin capacity, final adjustment value and expected fault set. On this basis, key components in the network and their possible fault states are identified.

[0099] During the day-ahead market clearing stage, the TSO uses the results of the previous day's baseline scenario calculation as the day-ahead market coupling parameters and the day-ahead declaration (quantity quotation) to clear the electricity market one day before the operation day and generate a detailed power generation plan, i.e., the day-ahead unit dispatch arrangement, to ensure the balance between electricity supply and demand and provide a basis for subsequent intraday markets and real-time dispatching.

[0100] The day-ahead market clearing coordinates the power flow in different regions through a centralized algorithm to ensure the safe and reliable operation of inter-regional transmission lines. The specific process is as follows: the market management agency first collects and processes the price, capacity and other bidding data of all market participants; then the key parameters of cross-regional power transactions and the bidding data of market members are input into the day-ahead market clearing model, and the inter-regional power flow, regional power generation plans and load distribution plans are calculated with the goal of maximizing social welfare to ensure the balance between power supply and demand; finally, the power market clearing price of each region is determined based on the market bidding and optimization results.

[0101] Specifically, it includes two stages: safety margin capacity calculation and regional capacity allocation:

[0102] 1. Safety margin capacity calculation:

[0103] Due to the lack of historical data and policy restrictions, TSOs often use deterministic new energy and load distribution decision methods or directly formulate them based on operating experience when formulating the parameters of the inter-regional capacity allocation model FRM, and apply them to all CNEs on average. However, in the market environment, this fixed safety margin capacity cannot adapt to the strong volatility and uncertainty of both the source and the load under the condition of high proportion of new energy access, and cannot efficiently convey commercial exchange capacity information and price signals to the market. How to use historical information of basic conditions to deal with the uncertainty of new energy is crucial.

[0104] Based on the above problems, this embodiment proposes an inexact Dirichlet model based on inexact probability theory, constructs a probability distribution uncertainty set formed by all possible cumulative distribution functions of regional renewable energy output, and then quantitatively evaluates the risks of renewable energy power abandonment and load shedding under different FRMs, and realizes market-based decision-making of safety margin capacity by introducing economic soft penalties in the objective function.

[0105] Step 1: Obtain historical sample data of renewable energy output, and use the historical sample data to construct a probability distribution uncertainty set by estimating the confidence band of the cumulative probability distribution function through probability intervals.

[0106] Step 1.1, obtain historical sample data of renewable energy output. In the historical sample set of renewable energy output r, all samples are independent and identically distributed. According to the law of large numbers, a samples out of b samples are less than or equal to the given value A. hour, The value of will be equal to a / b. By repeating this process for all possible values ​​of r, we can obtain the cumulative probability distribution function of the random variable, as Figure 2 However, TSO can only grasp limited historical data samples and cannot guarantee the accuracy of the probability estimation of new energy output. Therefore, this embodiment proposes a method for estimating the confidence band of the cumulative probability distribution function through probability intervals based on the existing historical data of TSO, and finally forms a probability distribution uncertainty set.

[0107] Step 1.2, use historical sample data to construct a probability distribution uncertainty set by estimating the confidence band of the cumulative probability distribution function through probability intervals.

[0108] Step 1.2.1: For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point.

[0109] In a specific implementation, for a certain value of r, such as A, the probability interval of the occurrence of a random event is estimated at a specified confidence level. Through this step, the upper bound and lower bound of the cumulative probability distribution function at the value point can be obtained. Figure 2 As shown in Figure 1, the width of the probability interval is closely related to the quantity and quality of historical data. The more valid historical data there is, the narrower the probability interval is and the more accurate the probability is. If there is enough historical data, the endpoints of the probability interval will overlap, resulting in an accurate probability.

[0110] Setting confidence intervals The cumulative probability distribution function is the event A handling confidence is The probability interval of can be estimated by the following formula:

[0111] (8).

[0112] in, and Respectively represent the lower and upper bounds of the probability interval; X is distributed The cumulative probability distribution function of distributed Cumulative probability distribution function; m is the total sample size; is the number of times a statistical event occurs; s represents the size of the equivalent sample and is used to adjust the parameters for calculating the cumulative probability distribution function. The larger the s value, the more conservative the estimate will be, reflecting a higher confidence level.

[0113] Step 1.2.2, construct the confidence band of the entire cumulative probability distribution function based on the boundary of the cumulative probability distribution function at each point, such as Figure 3As shown, within a limited range of historical data samples, the probability intervals of all events occurring are calculated.

[0114] Step 1.2.3, use numerical calculation method to form the entire confidence band and obtain the probability distribution uncertainty set.

[0115] In a specific implementation, a numerical calculation method such as interpolation is used to form the entire confidence band to obtain the probability distribution uncertainty set. This embodiment uses simple step interpolation to obtain the confidence band of the entire cumulative probability distribution function, which is specifically expressed as:

[0116] (9)

[0117] (10).

[0118] Among them, formula (9) is used to determine the lower boundary of the uncertainty set of probability distribution, which is taken as all the conditions that meet Sample points The largest ; Formula (10) is used to determine the upper boundary of the uncertainty set of probability distribution, which is all the values ​​that meet the condition Sample points The largest , the schematic diagram is as follows Figure 4 shown.

[0119] Finally, the complete expression of the probability distribution uncertainty set can be obtained as follows:

[0120] (11).

[0121] The above probability interval estimation method is used to obtain the boundary of the uncertainty set of the cumulative probability distribution function based on the point-by-point confidence level. For any given new energy output value r, the value of the cumulative probability distribution function at point r is , will fall within the estimated bounds with a given confidence level.

[0122] Step 2: Determine the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the boundary of the probability distribution uncertainty set.

[0123] Step 2.1: According to the definition of wind power acceptance risk CVaR, considering the uncertainty of wind power probability distribution, the wind power acceptance risk CVaR index corresponding to insufficient wind power generation (actual wind power value is lower than the predicted value) at a given node can be expressed as a function:

[0124] (12).

[0125] In the formula, represents the wind power acceptance risk function, represents the lower limit of the wind power acceptance range; Fu(x) represents the cumulative probability distribution function of wind power, which is the upper limit of the cumulative probability distribution uncertainty set A estimated in advance through historical data. Based on the idea of ​​robustness, the wind power acceptance risk CVaR indicator at this time is the maximum expected loss corresponding to all possible cumulative probability distribution functions in the probability distribution uncertainty set.

[0126] In a specific implementation, taking wind power as an example, for a given new energy access area, the risk value of node wind power acceptance conditions is defined as: the average potential loss that a wind power access node may suffer due to wind power disturbance exceeding the maximum wind power acceptance range of the node. Taking a certain wind power access node as an example, assuming that the probability density function of the wind power injection power on the node is as follows: Figure 2 As shown in the figure. For a given wind power access node, its wind power acceptance effective safety domain represents the power disturbance acceptance capacity of the node. As long as the wind power disturbance is within this area, the operational risk caused by wind abandonment or power shortage will not occur; on the contrary, if the wind power disturbance exceeds this area, wind abandonment or load shedding may occur. The wind abandonment / load shedding loss will be quantitatively evaluated in this section using the CVaR indicator of wind power acceptance.

[0127] Step 2.2: According to the different index requirements of the system, the transmission system operator adjusts the size of the safety margin capacity reserved for the regional tie line, and determines the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the change in probability distribution. Figure 5 Given a probability distribution uncertainty set represented by a cumulative probability distribution function, Figure 6 represents the probability density function corresponding to its boundary, where the shaded part represents the risk of wind abandonment or load shedding. , They represent the lower and upper boundaries of the uncertainty set of the cumulative probability distribution function respectively; , Represent the probability density functions corresponding to the upper and lower boundaries respectively.

[0128] In a specific implementation, when the system has different requirements for indicators, the TSO adjusts the size of the FRM retained by the inter-regional tie line to change the probability distribution of F(x). The calculation formula is as follows:

[0129] (13).

[0130] in, It represents the safety margin capacity pre-set by TSO for each tie line and is the decision-making quantity.

[0131] According to the above calculation, FRM and Relationship: Assume that When FRM is 0, Figure 7 As shown in the middle curve II. If the power system operator has higher requirements for power supply reliability and requires a lower risk of load loss, it is ( ), by increasing the value of the transmission line FRM (taking the node power injection direction as positive), F(x) changes, such as Figure 7 As shown in the dashed curve band III, the transmission line is retained after calculation. The safety margin capacity of the system can make up for the insufficient power generation and meet the load side demand.

[0132] Similarly, we can get FRM and The relationship is as follows:

[0133] (14).

[0134] in, represents the upper limit of wind power acceptance range, Indicates the maximum output of the wind turbine; F l (x) It represents the lower boundary of the uncertainty set A of the cumulative probability distribution of wind power.

[0135] If the power system operator has higher requirements for the consumption of new energy and requires a reduction in the risk of power abandonment, F(x) will change by increasing the value of the reverse FRM of the transmission line (in this case, FRM is the node outflow direction, a negative value), such as Figure 7 As shown in the dashed curve band Ⅰ, the transmission line is retained after calculation. The safety margin capacity of various sizes can make up for the insufficient capacity of the system to absorb new energy and meet the needs of the power generation side.

[0136] Step 3: Establish a safety margin capacity decision model based on the relationship between the safety margin capacity and the risk of power curtailment and load shedding, linearize and solve the safety margin capacity decision model, and obtain the remaining available margin value in the current area.

[0137] Step 3.1, new energy unit modeling:

[0138] (15).

[0139] in, Characterizes the output of new energy, represents the expected value (predicted value) of wind power output, is a random variable. Based on the historical data of similar days and similar times, we assume obey Figure 5 The probability distribution of the uncertain set is shown.

[0140] Step 3.2, construct the objective function: The objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

[0141] The area under the jurisdiction of the transmission system operator is the area involved in this embodiment. , and It only represents the units, nodes and lines in the area. The total operating cost includes the power generation cost and risk cost.

[0142] In the process of optimization decision, the power balance constraint of the system operation base point, the capacity constraint of conventional units, and the branch power flow constraint are considered. The objective function is expressed as follows:

[0143] (16).

[0144] in, is the operating cost coefficient of conventional units; and They represent the risk cost coefficients of system load shedding and renewable energy power abandonment respectively.

[0145] Step 3.3, the constraints of the objective function of the safety margin capacity decision model include: base point power balance constraint, conventional unit capacity constraint, new energy output acceptable range constraint, flexibility demand constraint, regional line transmission capacity constraint and safety margin capacity constraint:

[0146] a. Base point power balance constraints.

[0147] (17).

[0148] in, represents the operating base point of unit i at time t; Represents the total load of the area under the jurisdiction of TSO at time t, represents the output of the new energy unit at node n at time t, N G Indicates the total number of conventional units, N N Indicates the total number of nodes.

[0149] b. Conventional unit capacity constraints.

[0150] (18)

[0151] (19)

[0152] (20).

[0153] in, and They represent the upper and lower output limits of unit i respectively. and They represent the upward and downward flexibility of unit i at time t respectively; and are the up-climbing and down-climbing capabilities of unit i respectively.

[0154] The sum of the adjustable capacities of all conventional units in a certain period is equal to the flexible regulation capacity that the system can provide in that period. Therefore, the overall flexibility constraint of the region is:

[0155] (twenty one).

[0156] in, and They represent the upward and downward flexibility of the decision area at time t, respectively.

[0157] c. The acceptable range of renewable energy output is limited.

[0158] For random variables , the acceptable range of new energy output It can be expressed as:

[0159] (twenty two).

[0160] d. Flexibility demand constraints.

[0161] The flexibility demand constraints include regional flexibility constraints and unit flexibility constraints, as shown in equations (23) and (24), respectively:

[0162] (twenty three),

[0163] (twenty four),

[0164] (25).

[0165] in, Characterize the participating factors; is a column vector of a specific dimension, where all elements are 1.

[0166] e. Transmission capacity constraints of lines within the region.

[0167] (26).

[0168] in, Characterizes the upper limit of the transmission capacity of line l at time t; and represents the power transfer distribution factor from unit i and node n to line l. Since the above constraint (26) contains random variables, based on robust optimization theory and Soyster method, the constraint can be transformed into:

[0169] (27).

[0170] f. FRM capacity constraints.

[0171] (28)

[0172] (29).

[0173] in, Represents the line flow reference vector at time t, including the decomposition of transaction capacity outside the spot market such as medium- and long-term electricity contracts under the baseline scenario at time t.

[0174] Step 3.4, linearize and solve the safety margin capacity decision model.

[0175] Step 3.4.1, linearization of nonlinear constraints.

[0176] In this embodiment, the bilinear variables of constraint (27) together form the matrix , as follows:

[0177] (30).

[0178] Introducing auxiliary variables correspond For any element in , based on the McCormick envelope method, the original constraint can be transformed into the following constraint group:

[0179] (31),

[0180] (32),

[0181] (33).

[0182] By replacing the original constraints with the constraints of equations (31), (32) and (33), the nonlinear model is transformed into a linear model, which can then be easily solved using conventional commercial solvers. It is worth noting that constraints (32) and (33) will greatly increase the computational burden in the actual calculation process, and in fact only the line transmission capacity constraints in a few areas will play a role in the decision-making, because when solving the problem, the relaxed model without constraint (27) can be calculated first, and then the linear constraints (31), (32) and (33) can be used to quickly check whether the initial solution satisfies the line transmission capacity constraints in the relaxed model. If all inequalities are satisfied, the process is terminated and the optimal solution is output; otherwise, the effective line transmission capacity constraints are added to the relaxed model in a linear form and then solved; this process is repeated until all line transmission constraints are satisfied.

[0183] Step 3.4.2, linearization of risk value of new energy acceptance conditions.

[0184] Figure 5 The cumulative probability distribution function F in the equation has significant nonlinear characteristics after integration. For example, the schematic diagram is as follows Figure 8 As shown by the solid line, the linearization steps are as follows:

[0185] Step 1: Change the horizontal axis 0 to The part between them is divided equally, each segment is l long, and the coordinates are obtained , in the example .

[0186] Step 2: Integrate the known cumulative probability distribution function Get the function value corresponding to each segment point , and on this basis, the linear function of each segment is obtained:

[0187] (34),

[0188] (35),

[0189] (36).

[0190] in, , Line segment for the function (Number the line segments according to the starting and ending points of the line segments, segment).

[0191] Step 3: Based on the K-block method, by introducing the auxiliary objective function And linear constraint expressions are used to approximate the nonlinear function and realize the piecewise linearization of the model.

[0192] (37),

[0193] (38).

[0194] Step 4: Based on the model composed of equations (37) and (38), the piecewise linearized function is obtained , as follows:

[0195] (39).

[0196] It should be pointed out that the piecewise linearization method used here is different from the traditional piecewise linear method. This method does not introduce integer variables in the linearization process, which will help improve the computational efficiency of the optimization model and enhance the convergence of the optimization algorithm. At the same time, in the above-mentioned piecewise linearization method, the more segments are selected, the more accurate the solution obtained. Of course, as the number of segments increases, the computational burden will also increase. In actual situations, the method will select an appropriate number of segments to balance the solution efficiency and solution accuracy of the model.

[0197] 2. Regional capacity allocation:

[0198] Step 4: Based on the uncertainty of renewable energy output, an inter-regional capacity allocation model is established. Taking into account the remaining available margin value, the inter-regional capacity allocation model is solved to obtain the final capacity allocation plan.

[0199] Step 4.1, modeling of conventional units and renewable energy units. The difference between the modeling of renewable energy units in the safety margin capacity decision model and the modeling of renewable energy units is that according to empirical results, the forecast error of renewable energy output can be well modeled with a parameterized normal distribution, that is, the random variable ,in is the covariance matrix of the new energy prediction error at node n at time t. The new energy unit modeling method is as follows:

[0200] (40).

[0201] in, Represents the collection of new energy unit outputs in each area of ​​the system.

[0202] Step 4.2, based on the uncertainty of renewable energy output, establish an inter-regional capacity allocation model, specifically allocating the uncertainty of renewable energy output to each conventional generator set according to different participation factors.

[0203] According to the FBMC clearing method, the inter-regional capacity allocation model adopts a centralized algorithm. Therefore, it is considered to directly allocate the uncertainty of renewable energy output (system imbalance) to each conventional generator according to different participation factors, as follows:

[0204] (41).

[0205] in, Characterizes the set of conventional unit outputs in each area of ​​the system, is the operating base point of unit i at time t; is a random variable, is also a random variable, . Similarly It is also necessary to satisfy equation (25).

[0206] Step 4.3, objective function. The objective function of the inter-regional capacity allocation model is to maximize the overall social welfare.

[0207] The details are as follows:

[0208] (42).

[0209] in, is the inter-regional transaction capacity, and together constitute the decision variables.

[0210] Step 4.3, the constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints, and inter-regional line transmission capacity constraints.

[0211] a. Unit output constraints.

[0212] (43).

[0213] because and are all random variables. This embodiment introduces a chance constraint method to ensure that each conventional generator set meets its required response and the probability of overload in each N-1 or N-2 state is not higher than , Indicates the risk level of unit overload, usually a smaller value is selected, such as 5%. Based on the above analysis, the unit output constraint can be rewritten as:

[0214] (44),

[0215] (45).

[0216] b. Node / area power balance constraints.

[0217] In order to facilitate representation without loss of generality, an auxiliary parameter is introduced , each element of which is a binary parameter, representing the market partition of each node. Therefore, the node power balance constraint can be further rewritten as the regional power balance constraint, as follows:

[0218] (46),

[0219] (47).

[0220] c. Inter-regional line transmission capacity constraints.

[0221] The economic dispatch of regional net power change and node power injection is limited by the transmission system capacity and the power system flow calculation model, as follows:

[0222] (48),

[0223] (49).

[0224] in, is the input parameter, which is determined by equation (6) and the content of Section III; the regional power transmission distribution factor It may vary depending on the time step, representing the grid state and transmission capacity at each decision moment. According to formula (46), is also a random variable, so it also needs to be written in the form of chance constraints, as follows:

[0225] (50).

[0226] In addition, inter-regional electricity trading This can be directly limited by a static bilateral network transmission capacity (NTC), i.e. the maximum commercial exchange capacity between regions, as follows:

[0227] (51).

[0228] in, Characterizes the static bilateral network transmission capacity between regions at time t.

[0229] Step 4.4, opportunity constraint transformation.

[0230] Since the chance constraints (44), (45) and (50) cannot be solved directly, this embodiment uses the random variable The moment information of the opportunity constraint is converted into a tractable linear constraint or a second-order cone constraint, which is convenient for solving the model. Taking formula (44) as an example, the specific derivation process is as follows:

[0231] Formula (44) can be transformed into the following form:

[0232] (52).

[0233] set up ,in satisfy , .

[0234] According to the one-sided Chebyshev inequality principle, we can get:

[0235] (53),

[0236] (54).

[0237] The infimum calculation depends on , which is equivalent to the square of the distance from the origin of the hyperplane, so for:

[0238] (55).

[0239] Then we can get:

[0240] (56).

[0241] in, .

[0242] Similarly, for equation (45) and equation (50), we can obtain equation (57), equation (58) and equation (59), respectively, as follows:

[0243] (57)

[0244] (58)

[0245] (59)

[0246] in, is an auxiliary variable, representing the standard deviation of the power flow through the key network element c; represents the 2-norm, and Equation (59) is a second-order cone constraint. Based on the analysis in the previous section, This can explain the endogenous FRM set by TSO for each key network element to cope with the uncertainty of renewable energy output. At the same time, equation (59) also provides a suitable method for TSOs without detailed historical data to calculate FRM more accurately. In this case, equation (58) can be expressed as:

[0247] (60).

[0248] After the opportunity constraints are transformed, the general solver is used to directly solve the objective function (42) to obtain the optimal solution of the decision quantity and generate the final capacity allocation plan.

[0249] Embodiment 2:

[0250] Embodiment 2 of the present invention provides a capacity allocation system considering the day-ahead market coupling uncertainty, including:

[0251] The data acquisition and processing module is configured to obtain historical sample data of renewable energy output, and construct a probability distribution uncertainty set by using the historical sample data to estimate the confidence band of the cumulative probability distribution function through probability intervals.

[0252] The data acquisition and processing module also includes a data acquisition module and a data processing module.

[0253] The data acquisition module is used to acquire historical sample data of new energy output.

[0254] The data processing module is used to construct a probability distribution uncertainty set by using historical sample data through a method of estimating the confidence band of the cumulative probability distribution function through probability intervals:

[0255] For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point;

[0256] Construct the confidence band of the entire cumulative probability distribution function based on the cumulative probability distribution function boundary at each point;

[0257] The entire confidence band is formed using numerical calculation methods to obtain the uncertainty set of probability distribution.

[0258] The risk threshold determination module is configured to determine the relationship between the safety margin capacity and the risk of power abandonment and load shedding according to the boundary of the probability distribution uncertainty set.

[0259] According to the definition of wind power acceptance risk, considering the uncertainty of wind power probability distribution, the wind power acceptance risk index corresponding to insufficient wind power generation at a given node is expressed as a function;

[0260] According to the different indicator requirements of the system, the transmission system operator adjusts the size of the safety margin capacity retained for the inter-regional interconnection lines, and determines the relationship between the safety margin capacity and the risk of power abandonment and load shedding based on changes in probability distribution.

[0261] The safety decision module is configured to establish a safety margin capacity decision model according to the relationship between the safety margin capacity and the risk of power abandonment and load shedding, linearize and solve the safety margin capacity decision model, and obtain the remaining available margin value of the current area;

[0262] Among them, the objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

[0263] The constraints of the objective function of the safety margin capacity decision model include: base point power balance constraint, conventional unit capacity constraint, renewable energy output acceptable range constraint, flexibility demand constraint, regional line transmission capacity constraint and safety margin capacity constraint.

[0264] The inter-regional capacity allocation module is configured to establish an inter-regional capacity allocation model based on the uncertainty of the output of new energy sources, solve the inter-regional capacity allocation model in consideration of the remaining available margin value, and obtain a final capacity allocation plan.

[0265] Among them, the objective function of the inter-regional capacity allocation model is to maximize the overall social welfare. Based on the uncertainty of new energy output, an inter-regional capacity allocation model is established, specifically allocating the uncertainty of new energy output to each conventional generator set according to different participation factors.

[0266] The constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints, and inter-regional line transmission capacity constraints.

[0267] It should be noted that the capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The day before the inter-regional power exchange delivery, based on the capacity allocation plan and the bidding situation of market participants, the power market is cleared one day before the operation day to generate a detailed power generation plan.

[0268] The steps involved in the above embodiment 2 correspond to those in the method embodiment 1. For the specific implementation method, please refer to the relevant description part of the embodiment 1.

[0269] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

[0270] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0271] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A capacity allocation method considering the uncertainty of day-ahead market coupling, characterized in that: The following steps are involved: Obtain historical sample data of renewable energy output, and use the historical sample data to construct a probability distribution uncertainty set by estimating the confidence band of the cumulative probability distribution function through probability intervals. The specific steps are as follows: For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point; Construct the confidence band of the entire cumulative probability distribution function based on the cumulative probability distribution function boundary at each point; The entire confidence band is formed using numerical calculation methods to obtain the uncertainty set of probability distribution; Determine the relationship between safety margin capacity and risk of power curtailment and load shedding based on the boundary of the uncertainty set of probability distribution; A safety margin capacity decision model is established based on the relationship between the safety margin capacity and the risk of power curtailment and load shedding. The safety margin capacity decision model is linearized and solved to obtain the safety margin capacity of each line, and then the remaining available margin value of each area is obtained. Based on the uncertainty of renewable energy output, an inter-regional capacity allocation model is established. Taking the remaining available margin value into consideration, the inter-regional capacity allocation model is solved to obtain the final capacity allocation plan.

2. The capacity allocation method considering the uncertainty of day-ahead market coupling as claimed in claim 1, characterized in that: The specific steps to determine the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the boundary of the probability distribution uncertainty set are as follows: According to the definition of wind power acceptance risk, considering the uncertainty of wind power probability distribution, the wind power acceptance risk index corresponding to insufficient wind power generation at a given node is expressed as a function; According to the different indicator requirements of the system, the transmission system operator adjusts the size of the safety margin capacity retained for the inter-regional interconnection lines, and determines the relationship between the safety margin capacity and the risk of power abandonment and load shedding based on changes in probability distribution.

3. The capacity allocation method considering the uncertainty of day-ahead market coupling as claimed in claim 1, characterized in that: The objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

4. The capacity allocation method considering the day-ahead market coupling uncertainty as claimed in claim 3, characterized in that: The constraints of the objective function of the safety margin capacity decision model include: base point power balance constraint, conventional unit capacity constraint, renewable energy output acceptable range constraint, flexibility demand constraint, regional line transmission capacity constraint and safety margin capacity constraint.

5. The capacity allocation method considering the uncertainty of day-ahead market coupling as claimed in claim 1, characterized in that: The objective function of the inter-regional capacity allocation model is to maximize the overall social welfare.

6. The capacity allocation method considering the day-ahead market coupling uncertainty as claimed in claim 5, characterized in that: Based on the uncertainty of renewable energy output, an inter-regional capacity allocation model is established. Specifically, the uncertainty of renewable energy output is allocated to each conventional generating unit according to different participation factors, thereby forming an inter-regional transmission line capacity allocation plan.

7. The capacity allocation method considering the uncertainty of day-ahead market coupling as claimed in claim 6, characterized in that: The constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints, and inter-regional line transmission capacity constraints.

8. The capacity allocation method considering the uncertainty of day-ahead market coupling as claimed in claim 1, characterized in that: The capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The day before the inter-regional power exchange delivery, based on the capacity allocation plan and the bidding situation of market participants, the power market is cleared one day before the operation day to generate a detailed power generation plan and inter-regional line capacity allocation plan.

9. A capacity allocation system considering the uncertainty of day-ahead market coupling, comprising: The data acquisition and processing module is configured to obtain historical sample data of renewable energy output, and construct a probability distribution uncertainty set by estimating the confidence band of the cumulative probability distribution function through probability intervals using the historical sample data. The specific steps are as follows: For a certain value of renewable energy output, estimate the probability interval of random events at a specified confidence level, and obtain the upper and lower bounds of the cumulative probability distribution function at the value point; Construct the confidence band of the entire cumulative probability distribution function based on the cumulative probability distribution function boundary at each point; The entire confidence band is formed using numerical calculation methods to obtain the uncertainty set of probability distribution; A risk threshold determination module is configured to determine the relationship between the safety margin capacity and the risk of power curtailment and load shedding according to the boundary of the probability distribution uncertainty set; The safety decision module is configured to establish a safety margin capacity decision model according to the relationship between the safety margin capacity and the risk of power abandonment and load shedding, linearize and solve the safety margin capacity decision model, and obtain the remaining available margin value of the current area; The inter-regional capacity allocation module is configured to establish an inter-regional capacity allocation model based on the uncertainty of the output of new energy sources, solve the inter-regional capacity allocation model in consideration of the remaining available margin value, and obtain a final capacity allocation plan.

10. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 9, characterized in that: The risk threshold determination module is also used to: According to the definition of wind power acceptance risk, considering the uncertainty of wind power probability distribution, the wind power acceptance risk index corresponding to insufficient wind power generation at a given node is expressed as a function; According to the different indicator requirements of the system, the transmission system operator adjusts the size of the safety margin capacity retained for the inter-regional interconnection lines, and determines the relationship between the safety margin capacity and the risk of power abandonment and load shedding based on changes in probability distribution.

11. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 9, characterized in that: In the safety decision module, the objective function of the safety margin capacity decision model is to minimize the total operating cost of the system within the area under the jurisdiction of the transmission system operator.

12. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 11, characterized in that: In the safety decision module, the constraints of the objective function of the safety margin capacity decision model include: base point power balance constraint, conventional unit capacity constraint, new energy output acceptable range constraint, flexibility demand constraint, regional line transmission capacity constraint and safety margin capacity constraint.

13. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 9, characterized in that: In the inter-regional capacity allocation module, the objective function of the inter-regional capacity allocation model is to maximize the overall social welfare.

14. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 13, characterized in that: In the inter-regional capacity allocation module, an inter-regional capacity allocation model is established based on the uncertainty of renewable energy output. Specifically, the uncertainty of renewable energy output is allocated to each conventional generating unit according to different participation factors.

15. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 14, characterized in that: In the inter-regional capacity allocation module, the constraints of the objective function of the inter-regional capacity allocation model include: unit output constraints, node or regional power balance constraints and inter-regional line transmission capacity constraints.

16. The capacity allocation system considering the day-ahead market coupling uncertainty as claimed in claim 9, characterized in that: The capacity allocation plan is calculated and generated two days before the inter-regional power exchange delivery. The day before the inter-regional power exchange delivery, based on the capacity allocation plan and the bidding situation of market participants, the power market is cleared one day before the operation day to generate a detailed power generation plan.

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