A robust unit commitment optimization method based on fluctuation analysis to identify key constraints

Abstract

The present invention belongs to the technical field of electric power system, and specifically discloses a robust unit combination optimization method for identifying key constraints based on fluctuation analysis, including: constructing a unit combination optimization model; determining a first unit combination optimization model, and calculating a first remaining transmission capacity; constructing a robust uncertainty set, determining a second unit combination optimization model, and calculating a second remaining transmission capacity; calculating a time period spontaneous fluctuation amount, calculating a first net remaining transmission capacity, calculating a second net remaining transmission capacity, and determining a first key line set; calculating the full-day fluctuation amount of each line in the power system, analyzing the time period spontaneous fluctuation amount, and determining a second key line set; and determining a third unit combination optimization model. The present invention solves the problem that the key constraint identification in the prior art does not consider time correlation by effectively identifying the key line flow safety constraints that are highly sensitive to new energy power generation fluctuations and full-day supply and demand changes in the day-ahead robust unit combination.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a robust unit combination optimization method based on fluctuation analysis to identify key constraints. Background Art

[0002] The safety constrained unit commitment (SCUC) problem is a typical non-convex, large-scale mixed integer optimization problem, which contains a large number of binary variables and continuous variables and is restricted by a series of equality and inequality constraints. In actual power systems, most of the line transmission safety constraints are not effective, and these redundant constraints are often the main limiting factor for the computational efficiency of SCUC. At present, the research methods for the identification of redundant line transmission safety constraints mainly include three categories: heuristic methods, analysis methods based on power transfer distribution factors (PTDFs), and data-driven methods. The heuristic method starts with the unit commitment optimization model that does not consider all line flow safety constraints. At each iteration, it checks whether the current solution violates the constraints and adds the violated constraints to the model until all constraints are satisfied. The PTDF analysis method eliminates the safety constraints that will not work before solving, thereby obtaining a simplified model of unit commitment without multiple iterations. The data-driven method analyzes critical and non-critical lines through historical data, but this method requires a large amount of historical data, and when faced with unseen extreme samples, the prediction accuracy is often poor, which affects the identification effect.

[0003] However, most of the existing methods are only verified without considering the uncertainty and volatility of renewable energy, which makes it difficult to meet the requirements of high-proportion renewable energy power systems for grid operation safety. Especially when considering the uncertainty and volatility of renewable energy, the time-varying fluctuations of power system line flows are more severe. However, existing methods generally adopt isolated analysis for each period, ignoring the time correlation of line flows, resulting in redundant constraint identification results that are too conservative and cannot be directly applied to day-ahead robust unit combinations and actual intraday operation scenarios, and the improvement in computing performance is limited. Therefore, there is still much room for improvement in the power system robust unit combination optimization method based on key constraint identification. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a robust unit combination optimization method for identifying key constraints based on fluctuation analysis, which solves the problems in the prior art that the key constraint identification does not consider time correlation, is too conservative, and the identification results cannot be directly applied to actual daily operation, thereby improving the safety of the system during daily operation.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a robust unit commitment optimization method based on fluctuation analysis to identify key constraints, comprising the following steps:

[0006] S1. Obtain the day-ahead optimal dispatching data of the power system, and build a unit commitment optimization model without considering line flow safety constraints based on the day-ahead optimal dispatching data of the power system;

[0007] S2. The day-ahead predicted output of the new energy station in the unit combination optimization model is set to 0, to obtain the first unit combination optimization model, to solve the first unit combination optimization model, and to calculate the first remaining transmission capacity of each line in the power system in each time period according to the solution result;

[0008] S3. Obtain the historical day-ahead predicted output data and historical real-time output data of the new energy station and calculate the output prediction deviation, construct a robust uncertainty set, determine the maximum output deviation of the new energy station in each time period based on the historical day-ahead predicted output data and historical real-time output data, and substitute it into the unit combination optimization model to obtain the second unit combination optimization model, solve the second unit combination optimization model, and calculate the second residual transmission capacity of each line in the power system in each time period based on the solution result;

[0009] S4, calculating the period spontaneous fluctuation momentum according to the first remaining transmission capacity and the second remaining transmission capacity, calculating the first net remaining transmission capacity according to the first remaining transmission capacity and the period spontaneous fluctuation momentum, calculating the second net remaining transmission capacity according to the second remaining transmission capacity and the period spontaneous fluctuation momentum, and counting the lines violating the line flow safety constraint in any period in the power system according to the first net remaining transmission capacity and the second net remaining transmission capacity, respectively, to obtain a first critical line set;

[0010] S5. Count the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity respectively, and calculate the all-day fluctuation of each line in the power system according to the statistical results of the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity, analyze the time period self-fluctuation, obtain the first element position that is less than the preset threshold value in the time period self-fluctuation, and set the second element corresponding to the first element position in the all-day fluctuation to 0, calculate the third net remaining transmission capacity according to the first net remaining transmission capacity and the all-day fluctuation, calculate the fourth net remaining transmission capacity according to the second net remaining transmission capacity and the all-day fluctuation, and count the lines that violate the line flow safety constraints in any time period in the power system according to the third net remaining transmission capacity and the fourth net remaining transmission capacity to obtain the second key line set;

[0011] S6. According to the first critical line set and the second critical line set, the line flow safety constraints are added to all time periods of the corresponding lines of the power system to obtain the third unit combination optimization model, and according to the robust uncertainty set, a robust unit combination simplified model is constructed and solved to accelerate the optimization of the robust unit combination problem of the power system.

[0012] The beneficial effect of the above scheme is that by simulating the power flow of each line in the power system when new energy is not connected and the power generation is at maximum, combined with dimensionless conversion and fluctuation analysis, it is possible to effectively identify the key lines that are highly sensitive to the fluctuation of new energy power generation and the trend of supply and demand changes throughout the day. Based on this, the redundant line flow safety constraints that do not work in the optimization process can be directly eliminated before solving the day-ahead robust unit combination, thereby significantly reducing the complexity of the optimization model and improving the solution efficiency. At the same time, the key line identification results obtained can be directly applied to the actual operation during the day without the need for secondary adjustments, thereby improving the safety of the power system during the day's operation.

[0013] Furthermore, in S1, the day-ahead optimization dispatching data of the power system includes the power grid topology data, the cost data of the units participating in the optimization, the operation characteristic data of the units participating in the optimization, the day-ahead load forecast data and the day-ahead forecast output data of the new energy station;

[0014] According to the day-ahead optimized dispatching data of the power system, a unit commitment optimization model without considering the line flow safety constraints is constructed, including:

[0015] According to the day-ahead optimal dispatching data of the power system, a unit commitment optimization model without considering the line flow safety constraints and the corresponding constraint conditions of the unit commitment optimization model are constructed;

[0016] The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop status, minimum continuous start and stop constraints, unit ramp constraints, system supply and demand balance constraints, and new energy station power abandonment constraints;

[0017] Among them, the unit commitment optimization model is:

[0018]

[0019] in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of Indicates the units involved in optimization In the period The corresponding no-load charges, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost corresponding to the startup event. Indicates the unit In the period Whether a downtime event occurred, Indicates the downtime cost corresponding to the downtime event; Indicates time period unit The output level, Indicates the unit The variable generation cost function; It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted electricity; represents the set of units participating in the optimization, Represents the collection of new energy stations, represents a set of scheduling periods;

[0020] The upper and lower limits of the unit output are:

[0021]

[0022] in, and Respectively represent the unit The minimum and maximum technical output of

[0023] The start and stop state constraints of the unit are:

[0024]

[0025]

[0026] in, Indicates the units involved in optimization In the period The operating status of

[0027] The minimum continuous start and stop constraints of the unit are:

[0028]

[0029]

[0030] in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred, Indicates the unit Minimum continuous power-on time parameter, Indicates the unit Minimum continuous shutdown time parameter;

[0031] The unit climbing constraint is:

[0032]

[0033] in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level;

[0034] The system supply and demand balance constraint is:

[0035]

[0036] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The predicted deviation between the day-ahead predicted output and the next day's actual output is 0 when the uncertainty of new energy is not considered. Representation Node In the period The load forecast value of the day ahead is is the set of load nodes;

[0037] The constraints on power abandonment at new energy stations are:

[0038]

[0039] in, Represents new energy station In the period of wasted electricity.

[0040] The beneficial effect of the above further scheme is that by introducing a unit commitment model that includes renewable energy forecast deviations and power abandonment, it lays the foundation for building a robust unit commitment model that can effectively cope with the uncertainty of renewable energy power generation and identifying key line flow safety constraints in the model.

[0041] Furthermore, in S2, the first unit combination optimization model is:

[0042]

[0043]

[0044] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output;

[0045] The first remaining transmission capacity of each line in the power system in each period is calculated according to the solution results, specifically including:

[0046] S21, calculating the first power flow of each line in each time period according to the solution result;

[0047] S22, calculating a first remaining transmission capacity according to the first power flow of each line in each time period;

[0048] According to the solution results, the power flow of each line in each period is calculated using the following calculation formula:

[0049]

[0050] in, Indicates line Time The first trend, Represents the first unit combination optimization model Time The optimal power generation plan, and Respectively represent the unit and nodes To line The power transfer distribution factor, represents a collection of transmission lines;

[0051] The first remaining transmission capacity is calculated using the following formula:

[0052]

[0053]

[0054] in, Indicates line The steady-state power flow limit value is Indicates line Time The first trend, and Respectively indicate lines Time The first forward remaining transmission capacity and the first reverse remaining transmission capacity.

[0055] The beneficial effect of the above further scheme is: by simulating the optimal power generation plan and line flows in each time period without the participation of renewable energy in the power system and without considering the line flow safety constraints, and non-dimensionalizing the line flow into a percentage of the remaining transmission capacity, it lays the foundation for the subsequent line flow fluctuation analysis and the effective identification of key constraints.

[0056] Furthermore, in S3, the force prediction deviation is calculated using the following calculation formula:

[0057]

[0058] in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time Real-time output, Represents new energy station No. Day time The day-ahead forecast output;

[0059] The robust uncertainty set is:

[0060]

[0061] in, represents the robust uncertainty set, for Tiantian New Energy Station Time The mean of the output forecast deviation, for Tiantian New Energy Station Time The standard deviation of the output forecast deviation, is the conservative control coefficient. The size of the conservative control coefficient is related to the confidence level requirement. budgeting for uncertain sets;

[0062] The second unit combination optimization model is:

[0063]

[0064] in, is the conservative control coefficient;

[0065] The second remaining transmission capacity of each line in the power system in each period is calculated according to the solution results, specifically including:

[0066] S31, calculating the second power flow of each line in each time period according to the solution result;

[0067] S32, calculating the second remaining transmission capacity according to the second power flow of each line in each time period;

[0068] According to the solution results, the second power flow of each line in each period is calculated using the following calculation formula:

[0069]

[0070] in, Indicates line Time The second trend, Represents the unit in the second unit combination optimization model Time The optimal power generation plan, Represents the new energy station in the second unit combination optimization model Time of wasted power, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, represents a collection of transmission lines;

[0071] The second remaining transmission capacity is calculated using the following formula:

[0072]

[0073]

[0074] in, Indicates line The steady-state power flow limit value is Indicates line Time The second trend, and Respectively indicate lines Time The second positive remaining capacity and the second reverse remaining capacity.

[0075] The beneficial effect of the above further scheme is: by simulating the power system without considering the line flow safety constraints, the optimal power generation plan and line flow in each time period when the renewable energy stations have the maximum forward predicted output deviation within the robust uncertainty set, and the line flow is dimensionlessly converted into a percentage of the remaining transmission capacity, laying the foundation for the subsequent line flow fluctuation analysis and effective identification of key constraints.

[0076] Furthermore, in S4, the calculation formula used to calculate the period's self-fluctuation amount is:

[0077]

[0078]

[0079] in, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period of the self-volatility, and Respectively indicate lines Time a first forward remaining transmission capacity and a first reverse remaining transmission capacity, and Respectively indicate lines Time a second positive remaining capacity and a second reverse remaining capacity;

[0080] The first net remaining transmission capacity is calculated using the following formula:

[0081]

[0082]

[0083] in, Indicates line Time The first positive net remaining transmission capacity, Indicates line Time The first reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility;

[0084] The second net remaining transmission capacity is calculated using the following formula:

[0085]

[0086]

[0087] in, Indicates line Time The second positive net remaining transmission capacity, Indicates line Time The second reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility;

[0088] According to the first net remaining transmission capacity and the second net remaining transmission capacity, the lines that violate the line flow safety constraints in any period of the power system are counted, and the calculation formula used is:

[0089]

[0090]

[0091]

[0092]

[0093] in, represents the first subset of the first forward critical path set, represents the first subset of the first reverse critical path set, represents the second subset of the first forward critical path set, representing a second subset of the first reverse critical path set;

[0094] The first key line set is:

[0095]

[0096]

[0097] in, and Represents the first critical line set, where if the line The first positive net remaining transmission capacity or the second positive net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first forward critical path set in the first critical path set; if path The first reverse net remaining transmission capacity or the second reverse net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first reverse critical path set in the first critical path set.

[0098] The beneficial effect of the above further scheme is that by calculating the fluctuation of line flow in each period of the power system when no new energy is connected and when the power generation is at maximum, the key lines that are highly sensitive to the power generation of new energy can be identified.

[0099] Further, in S5, the maximum value and the minimum value of the first net remaining transmission capacity and the second net remaining transmission capacity are respectively counted, and the calculation formula used is:

[0100]

[0101]

[0102]

[0103]

[0104] in, Indicates line The maximum net forward transmission remaining capacity throughout the day, Indicates line The minimum net forward transmission remaining capacity throughout the day, Indicates line The maximum net reverse transmission remaining capacity for the whole day, Indicates line The minimum net reverse transmission remaining capacity throughout the day;

[0105] The calculation formula used to calculate the daily fluctuation of each line in the power system is:

[0106]

[0107]

[0108] in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling;

[0109] The time period self-fluctuation is analyzed to obtain the first element position in the time period self-fluctuation that is less than the preset threshold, and the second element corresponding to the first element position in the full-day fluctuation is set to 0. The calculation formula used is:

[0110]

[0111]

[0112] in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling;

[0113] The third net remaining transmission capacity is calculated based on the first net remaining transmission capacity and the fluctuation amount throughout the day. The calculation formula used is:

[0114]

[0115]

[0116] in, represents the third positive net remaining transmission capacity, represents the first positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the third reverse net remaining transmission capacity, represents the first reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling;

[0117] According to the second net remaining transmission capacity and the fluctuation amount throughout the day, the fourth net remaining transmission capacity is calculated using the following calculation formula:

[0118]

[0119]

[0120] in, represents the fourth positive net remaining transmission capacity, represents the second positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the fourth reverse net remaining transmission capacity, represents the second reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling;

[0121] To count the lines that violate the line flow safety constraints in any period of time in the power system, the calculation formula used is:

[0122]

[0123]

[0124]

[0125]

[0126] in, represents the first subset of the second forward critical path set, represents a first subset of the second reverse critical path set, represents the second subset of the second forward critical path set, a second subset representing a second reverse critical path set;

[0127] The second key line set is:

[0128]

[0129]

[0130] in, and represents the second critical path set, where if the path The calculation result of the forward remaining transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second forward critical path set in the second critical path set; if path The calculation result of the reverse residual transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second reverse critical path set in the second critical path set.

[0131] The beneficial effect of the above further scheme is: by calculating the maximum and minimum values ​​of the current of each line in the power system under the conditions of no new energy access and maximum power generation throughout the day, and then calculating the fluctuation amount throughout the day, it is possible to effectively identify key lines that are highly sensitive to the supply and demand changes throughout the day.

[0132] Furthermore, in S6, the third unit combination optimization model is:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] in, is an infinite value, indicating that no constraints are imposed on the lines that do not belong to the first critical line set and the second critical line set. If the line belongs to the first critical line set or the second critical line set, the corresponding line flow safety constraints must be imposed in all time periods.

[0139] The simplified model of robust unit commitment is:

[0140]

[0141]

[0142] in, , , , , ,vector is the first-stage decision variable, the vector is the second-stage decision variable, vector is restricted to the robust uncertainty set The uncertain parameters, namely the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model.

[0143] The beneficial effect of the above further scheme is: by pre-identifying the set of critical lines that need to be subject to line flow safety constraints before solving the robust unit combination, redundant line flow safety constraints that do not work in the optimization process can be directly eliminated, thereby effectively reducing the complexity of the optimization model and significantly improving the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 The figure is a flow chart of a robust unit commitment optimization method based on fluctuation analysis to identify key constraints. DETAILED DESCRIPTION

[0145] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to explain the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0146] like Figure 1 As shown, a robust unit commitment optimization method for identifying key constraints based on fluctuation analysis includes the following steps:

[0147] S1. Obtain the day-ahead optimal dispatching data of the power system, and build a unit combination optimization model without considering line flow safety constraints based on the day-ahead optimal dispatching data of the power system.

[0148] In this embodiment, the day-ahead optimization dispatching data of the power system includes the power grid topology data, the cost data of the units participating in the optimization, the operation characteristic data of the units participating in the optimization, the day-ahead load forecast data and the day-ahead output forecast data of the new energy station;

[0149] According to the day-ahead optimized dispatching data of the power system, a unit commitment optimization model without considering the line flow safety constraints is constructed, including:

[0150] According to the day-ahead optimal dispatching data of the power system, a unit commitment optimization model without considering the line flow safety constraints and the corresponding constraint conditions of the unit commitment optimization model are constructed;

[0151] The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop status, minimum continuous start and stop constraints, unit ramp constraints, system supply and demand balance constraints, and new energy station power abandonment constraints;

[0152] Among them, the unit commitment optimization model is:

[0153]

[0154] in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of Indicates the units involved in optimization In the period The corresponding no-load charges, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost corresponding to the startup event. Indicates the unit In the period Whether a downtime event occurred, Indicates the downtime cost corresponding to the downtime event; Indicates time period unit The output level, Indicates the unit The variable generation cost function; It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted electricity; represents the set of units participating in the optimization, Represents the collection of new energy stations, represents a set of scheduling periods;

[0155] The upper and lower limits of the unit output are:

[0156]

[0157] in, and Respectively represent the unit The minimum and maximum technical output of

[0158] The start and stop state constraints of the unit are:

[0159]

[0160]

[0161] in, Indicates the units involved in optimization In the period The operating status of

[0162] The minimum continuous start and stop constraints of the unit are:

[0163]

[0164]

[0165] in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred, Indicates the unit Minimum continuous power-on time parameter, Indicates the unit Minimum continuous shutdown time parameter;

[0166] The unit climbing constraint is:

[0167]

[0168] in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level;

[0169] The system supply and demand balance constraint is:

[0170]

[0171] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The predicted deviation between the day-ahead predicted output and the next day's actual output is 0 when the uncertainty of new energy is not considered. Representation Node In the period The load forecast value of the day ahead is is the set of load nodes;

[0172] The constraints on power abandonment at new energy stations are:

[0173]

[0174] in, Represents new energy station In the period of wasted electricity.

[0175] Exemplarily, the day-ahead optimization dispatching data of the power system may include the following data:

[0176] Grid topology data: connection relationship between power network nodes and transmission lines, output power transfer distribution factor matrix of each type of unit and node load to each transmission line;

[0177] Cost data of the units involved in optimization: startup costs, shutdown costs, no-load costs, and variable power generation cost functions of the units at different time periods;

[0178] Participate in optimizing the unit's operating characteristic data: minimum / maximum technical output of the unit, the unit's climbing ability at different time periods, and the unit's minimum continuous start-up and shutdown time;

[0179] Load day-ahead forecast data: Load day-ahead forecast data for each node, with a time resolution of 1 hour or 15 minutes;

[0180] The day-ahead predicted output data of new energy stations; the day-ahead predicted output data of new energy stations has a time resolution of 1 hour or 15 minutes.

[0181] Optionally, Represents new energy station In the period The predicted deviation between the day-ahead predicted output and the next day's actual output. Its true value can only be calculated when the next day's actual output is known. The estimated value is 0 when the uncertainty of new energy is not considered. When the uncertainty of new energy is considered, it is a variable restricted by the robust uncertainty set.

[0182] S2. Set the day-ahead predicted output of the new energy station in the unit combination optimization model to 0, obtain the first unit combination optimization model, solve the first unit combination optimization model, and calculate the first remaining transmission capacity of each line in the power system in each time period according to the solution result.

[0183] In this embodiment, the first unit combination optimization model is:

[0184]

[0185]

[0186] in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output;

[0187] The first remaining transmission capacity of each line in the power system in each period is calculated according to the solution results, specifically including:

[0188] S21, calculating the first power flow of each line in each time period according to the solution result;

[0189] S22, calculating a first remaining transmission capacity according to the first power flow of each line in each time period;

[0190] According to the solution results, the power flow of each line in each period is calculated using the following calculation formula:

[0191]

[0192] in, Indicates line Time The first trend, Represents the first unit combination optimization model Time The optimal power generation plan, and Respectively represent the unit and nodes To line The power transfer distribution factor, represents a collection of transmission lines;

[0193] The first remaining transmission capacity is calculated using the following formula:

[0194]

[0195]

[0196] in, Indicates line The steady-state power flow limit value is Indicates line Time The first trend, and Respectively indicate lines Time The first forward remaining transmission capacity and the first reverse remaining transmission capacity.

[0197] S3. Obtain the historical day-ahead predicted output data and historical real-time output data of the new energy station and calculate the output prediction deviation, construct a robust uncertainty set, determine the maximum output deviation of the new energy station in each time period based on the historical day-ahead predicted output data and historical real-time output data, and substitute it into the unit combination optimization model to obtain the second unit combination optimization model, solve the second unit combination optimization model, and calculate the second residual transmission capacity of each line in the power system in each time period based on the solution results.

[0198] In this embodiment, the force prediction deviation is calculated using the following calculation formula:

[0199]

[0200] in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time Real-time output, Represents new energy station No. Day time The day-ahead forecast output;

[0201] The robust uncertainty set is:

[0202]

[0203] in, represents the robust uncertainty set, for Tiantian New Energy Station Time The mean of the output forecast deviation, for Tiantian New Energy Station Time The standard deviation of the output forecast deviation, is the conservative control coefficient. The size of the conservative control coefficient is related to the confidence level requirement. budgeting for uncertain sets;

[0204] The second unit combination optimization model is:

[0205]

[0206] in, is the conservative control coefficient;

[0207] The second remaining transmission capacity of each line in the power system in each period is calculated according to the solution results, specifically including:

[0208] S31, calculating the second power flow of each line in each time period according to the solution result;

[0209] S32, calculating the second remaining transmission capacity according to the second power flow of each line in each time period;

[0210] According to the solution results, the second power flow of each line in each period is calculated using the following calculation formula:

[0211]

[0212] in, Indicates line Time The second trend, Represents the unit in the second unit combination optimization model Time The optimal power generation plan, Represents the new energy station in the second unit combination optimization model Time of wasted power, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, represents a collection of transmission lines;

[0213] The second remaining transmission capacity is calculated using the following formula:

[0214]

[0215]

[0216] in, Indicates line The steady-state power flow limit value is Indicates line Time The second trend, and Respectively indicate lines Time The second positive remaining capacity and the second reverse remaining capacity.

[0217] S4. Calculate the spontaneous fluctuation momentum of the time period according to the first remaining transmission capacity and the second remaining transmission capacity, calculate the first net remaining transmission capacity according to the first remaining transmission capacity and the spontaneous fluctuation momentum of the time period, calculate the second net remaining transmission capacity according to the second remaining transmission capacity and the spontaneous fluctuation momentum of the time period, and count the lines that violate the line flow safety constraints in any time period in the power system according to the first net remaining transmission capacity and the second net remaining transmission capacity, respectively, to obtain a first critical line set.

[0218] In this embodiment, the calculation formula used to calculate the period self-fluctuation amount is:

[0219]

[0220]

[0221] in, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period of the self-volatility, and Respectively indicate lines Time a first forward remaining transmission capacity and a first reverse remaining transmission capacity, and Respectively indicate lines Time a second positive remaining capacity and a second reverse remaining capacity;

[0222] The first net remaining transmission capacity is calculated using the following formula:

[0223]

[0224]

[0225] in, Indicates line Time The first positive net remaining transmission capacity, Indicates line Time The first reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility;

[0226] The second net remaining transmission capacity is calculated using the following formula:

[0227]

[0228]

[0229] in, Indicates line Time The second positive net remaining transmission capacity, Indicates line Time The second reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility;

[0230] According to the first net remaining transmission capacity and the second net remaining transmission capacity, the lines that violate the line flow safety constraints in any period of the power system are counted, and the calculation formula used is:

[0231]

[0232]

[0233]

[0234]

[0235] in, represents the first subset of the first forward critical path set, represents the first subset of the first reverse critical path set, represents the second subset of the first forward critical path set, representing a second subset of the first reverse critical path set;

[0236] The first key line set is:

[0237]

[0238]

[0239] in, and Represents the first critical line set, where if the line The first positive net remaining transmission capacity or the second positive net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first forward critical path set in the first critical path set; if path The first reverse net remaining transmission capacity or the second reverse net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first reverse critical path set in the first critical path set.

[0240] S5. Count the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity respectively, and calculate the all-day fluctuation of each line in the power system based on the statistical results of the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity, analyze the time period self-fluctuation momentum, obtain the first element position that is less than a preset threshold in the time period self-fluctuation momentum, and set the second element corresponding to the first element position in the all-day fluctuation momentum to 0, calculate the third net remaining transmission capacity based on the first net remaining transmission capacity and the all-day fluctuation, calculate the fourth net remaining transmission capacity based on the second net remaining transmission capacity and the all-day fluctuation, and count the lines that violate the line flow safety constraints in any time period in the power system based on the third net remaining transmission capacity and the fourth net remaining transmission capacity to obtain a second critical line set.

[0241] In this embodiment, the maximum value and the minimum value of the first net remaining transmission capacity and the second net remaining transmission capacity are respectively counted, and the calculation formula used is:

[0242]

[0243]

[0244]

[0245]

[0246] in, Indicates line The maximum net forward transmission remaining capacity throughout the day, Indicates line The minimum net forward transmission remaining capacity throughout the day, Indicates line The maximum net reverse transmission remaining capacity for the whole day, Indicates line The minimum net reverse transmission remaining capacity throughout the day;

[0247] The calculation formula used to calculate the daily fluctuation of each line in the power system is:

[0248]

[0249]

[0250] in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling;

[0251] The time period self-fluctuation is analyzed to obtain the first element position in the time period self-fluctuation that is less than the preset threshold, and the second element corresponding to the first element position in the full-day fluctuation is set to 0. The calculation formula used is:

[0252]

[0253]

[0254] in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling;

[0255] The third net remaining transmission capacity is calculated based on the first net remaining transmission capacity and the fluctuation amount throughout the day. The calculation formula used is:

[0256]

[0257]

[0258] in, represents the third positive net remaining transmission capacity, represents the first positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the third reverse net remaining transmission capacity, represents the first reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling;

[0259] According to the second net remaining transmission capacity and the fluctuation amount throughout the day, the fourth net remaining transmission capacity is calculated using the following calculation formula:

[0260] According to the second net remaining transmission capacity and the fluctuation amount throughout the day, the fourth net remaining transmission capacity is calculated using the following calculation formula:

[0261]

[0262]

[0263] in, represents the fourth positive net remaining transmission capacity, represents the second positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the fourth reverse net remaining transmission capacity, represents the second reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling;

[0264] To count the lines that violate the line flow safety constraints in any period of time in the power system, the calculation formula used is:

[0265]

[0266]

[0267]

[0268]

[0269] in, represents the first subset of the second forward critical path set, represents a first subset of the second reverse critical path set, represents the second subset of the second forward critical path set, a second subset representing a second reverse critical path set;

[0270] The second key line set is:

[0271]

[0272]

[0273] in, and represents the second critical path set, where if the path The calculation result of the forward remaining transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second forward critical path set in the second critical path set; if path The calculation result of the reverse residual transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second reverse critical path set in the second critical path set.

[0274] S6. According to the first critical line set and the second critical line set, the line flow safety constraints are added to all time periods of the corresponding lines of the power system to obtain the third unit combination optimization model, and according to the robust uncertainty set, a robust unit combination simplified model is constructed and solved to accelerate the optimization of the robust unit combination problem of the power system.

[0275] In this embodiment, the third unit combination optimization model is:

[0276]

[0277]

[0278]

[0279]

[0280]

[0281] in, is an infinite value, indicating that no constraints are imposed on the lines that do not belong to the first critical line set and the second critical line set. If the line belongs to the first critical line set or the second critical line set, the corresponding line flow safety constraints must be imposed in all time periods.

[0282] The simplified model of robust unit commitment is:

[0283]

[0284]

[0285] in, , , , , ,vector is the first-stage decision variable, the vector is the second-stage decision variable, vector is restricted to the robust uncertainty set The uncertain parameters, namely the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model.

[0286] Optionally, in the robust unit commitment simplified model, the vectors are all based on the robust uncertainty set The corresponding elements of the unit commitment optimization model are arranged in sequence. Finally, the column and constraint generation algorithm can be used to solve the robust unit commitment simplified model to achieve accelerated optimization of the robust unit commitment problem of the power system.

[0287] In this embodiment, by effectively identifying the key line flow safety constraints in the day-ahead robust unit combination that are highly sensitive to the fluctuations in renewable energy power generation and changes in supply and demand throughout the day, the problems in the prior art that the key constraint identification does not consider time correlation, is too conservative, and the identification results cannot be directly applied to actual daily operations are solved, thereby improving the safety of the system during daily operation.

[0288] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A robust unit commitment optimization method based on fluctuation analysis to identify key constraints, characterized in that: The method comprises: S1. Obtaining the day-ahead optimal dispatching data of the power system, and constructing a unit commitment optimization model without considering line flow safety constraints according to the day-ahead optimal dispatching data; S2. Setting the day-ahead predicted output of the new energy station in the unit combination optimization model to 0, obtaining a first unit combination optimization model, solving the first unit combination optimization model, and calculating the first remaining transmission capacity of each line of the power system in each time period according to the solution result; S3, obtaining the historical day-ahead predicted output data and the historical real-time output data of the new energy station and calculating the output prediction deviation, constructing a robust uncertainty set, determining the maximum output deviation of the new energy station in each time period according to the historical day-ahead predicted output data and the historical real-time output data, and substituting it into the unit combination optimization model to obtain a second unit combination optimization model, solving the second unit combination optimization model, and calculating the second residual transmission capacity of each line of the power system in each time period according to the solution result; S4, calculating the period spontaneous fluctuation momentum according to the first remaining transmission capacity and the second remaining transmission capacity, calculating the first net remaining transmission capacity according to the first remaining transmission capacity and the period spontaneous fluctuation momentum, calculating the second net remaining transmission capacity according to the second remaining transmission capacity and the period spontaneous fluctuation momentum, and counting the lines violating the line flow safety constraint in any period in the power system according to the first net remaining transmission capacity and the second net remaining transmission capacity, respectively, to obtain a first critical line set; S5. Count the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity respectively, and calculate the all-day fluctuation amount of each line in the power system according to the statistical results of the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity, analyze the self-fluctuating amount in the time period, obtain the first element position that is less than a preset threshold in the self-fluctuating amount in the time period, and set the second element corresponding to the first element position in the all-day fluctuation amount to 0, calculate the third net remaining transmission capacity according to the first net remaining transmission capacity and the all-day fluctuation amount, calculate the fourth net remaining transmission capacity according to the second net remaining transmission capacity and the all-day fluctuation amount, and count the lines that violate the line flow safety constraints in any time period in the power system according to the third net remaining transmission capacity and the fourth net remaining transmission capacity to obtain a second critical line set; S6. According to the first critical line set and the second critical line set, the line flow safety constraints are added to all time periods of the corresponding lines of the power system to obtain a third unit combination optimization model, and according to the robust uncertainty set, a robust unit combination simplified model is constructed and solved to accelerate the optimization of the robust unit combination problem of the power system.

2. The method according to claim 1, characterized in that In S1, the power system day-ahead optimization dispatching data includes power grid topology data, cost data of the units participating in the optimization, operation characteristic data of the units participating in the optimization, load day-ahead forecast data, and new energy station day-ahead forecast output data; The step of constructing a unit commitment optimization model without considering line power flow safety constraints according to the day-ahead optimized dispatching data specifically includes: According to the day-ahead optimal dispatching data of the power system, a unit combination optimization model without considering line power flow safety constraints and constraint conditions corresponding to the unit combination optimization model are constructed; The constraints corresponding to the unit combination optimization model include upper and lower limits of unit output, unit start and stop state constraints, minimum continuous start and stop constraints, unit climbing constraints, system supply and demand balance constraints and new energy station power abandonment constraints; Wherein, the unit commitment optimization model is: in, represents the total operating cost, Indicates the units involved in optimization In the period The operating status of Indicates the units involved in optimization In the period The corresponding no-load charges, Indicates the unit In the period Whether a boot event occurs, Indicates the startup cost corresponding to the startup event. Indicates the unit In the period Whether a downtime event occurred, Indicates the downtime cost corresponding to the downtime event; Indicates time period unit The output level, Indicates the unit The variable generation cost function; It represents the penalty fee for abandoning electricity from new energy sources. Represents new energy station In the period of wasted electricity; represents the set of units participating in the optimization, Represents the collection of new energy stations, represents a set of scheduling periods; The upper and lower limits of the unit output are: in, and Respectively represent the unit The minimum and maximum technical output of The start and stop state constraints of the unit are: in, Indicates the units involved in optimization In the period The operating status of The minimum continuous start and stop constraints of the unit are: in, Indicates the unit In the period Whether a boot event occurs, Indicates the unit In the period Whether a downtime event occurred, Indicates the unit Minimum continuous power-on time parameter, Indicates the unit Minimum continuous shutdown time parameter; The unit climbing constraint is: in, and They represent the maximum down-ramp rate and the maximum up-ramp rate of the unit respectively. Indicates time period unit Output level; The system supply and demand balance constraint is: in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The predicted deviation between the day-ahead predicted output and the next day's actual output is 0 when the uncertainty of new energy is not considered. Representation Node In the period The load forecast value of the day ahead is is the set of load nodes; The constraints on power abandonment at new energy stations are: in, Represents new energy station In the period of wasted electricity.

3. The method according to claim 2, characterized in that In S2, the first unit combination optimization model is: in, Represents new energy station In the period The day-ahead forecast output is Represents new energy station In the period The forecast deviation between the day-ahead forecast output and the next day's actual output; Calculating the first remaining transmission capacity of each line of the power system in each time period according to the solution result specifically includes: S21, calculating the first power flow of each line in each time period according to the solution result; S22, calculating a first remaining transmission capacity according to the first power flow of each line in each time period; The flow of each line in each time period is calculated according to the solution result, and the calculation formula used is: in, Indicates line Time The first trend, Represents the first unit combination optimization model Time The optimal power generation plan, and Respectively represent the unit and nodes To line The power transfer distribution factor, represents the set of transmission lines, Representation Node In the period The day-ahead load forecast value; The calculation formula used in calculating the first remaining transmission capacity is: in, Indicates line The steady-state power flow limit value is Indicates line Time The first trend, and Respectively indicate lines Time The first forward remaining transmission capacity and the first reverse remaining transmission capacity.

4. The method according to claim 3, characterized in that In S3, the calculation formula used for calculating the output prediction deviation is: in, Represents new energy station No. Day time The output forecast deviation, Represents new energy station No. Day time Real-time output, Represents new energy station No. Day time The day-ahead forecast output; The robust uncertainty set is: in, represents the robust uncertainty set, for Tiantian New Energy Station Time The mean of the output forecast deviation, for Tiantian New Energy Station Time The standard deviation of the output forecast deviation, is the conservative control coefficient. The size of the conservative control coefficient is related to the confidence level requirement. budgeting for uncertain sets; The second unit combination optimization model is: in, is the conservative control coefficient; Calculating the second remaining transmission capacity of each line of the power system in each time period according to the solution result specifically includes: S31, calculating the second power flow of each line in each time period according to the solution result; S32, calculating the second remaining transmission capacity according to the second power flow of each line in each time period; The second power flow of each line in each time period is calculated according to the solution result, and the calculation formula used is: in, Indicates line Time The second trend, Represents the unit in the second unit combination optimization model Time The optimal power generation plan, Represents the new energy station in the second unit combination optimization model Time of wasted power, , and Respectively represent the unit 、New Energy Station and nodes To line The power transfer distribution factor, represents the set of transmission lines, Represents new energy station In the period The day-ahead forecast output is Representation Node To line The power transfer distribution factor, Representation Node In the period The day-ahead load forecast value; The calculation formula used in calculating the second remaining transmission capacity is: in, Indicates line The steady-state power flow limit value is Indicates line Time The second trend, and Respectively indicate lines Time The second positive remaining capacity and the second reverse remaining capacity.

5. The method according to claim 4, characterized in that In S4, the calculation formula used for calculating the time period self-fluctuation amount is: in, Indicates line Time The positive period of the self-volatility, Indicates line Time The reverse period of the self-volatility, and Respectively indicate lines Time a first forward remaining transmission capacity and a first reverse remaining transmission capacity, and Respectively indicate lines Time a second positive remaining capacity and a second reverse remaining capacity; The calculation formula used in calculating the first net remaining transmission capacity is: in, Indicates line Time The first positive net remaining transmission capacity, Indicates line Time The first reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility; The calculation formula used in calculating the second net remaining transmission capacity is: in, Indicates line Time The second positive net remaining transmission capacity, Indicates line Time The second reverse net remaining transmission capacity, Indicates line Time The positive period of the automatic fluctuation volume, Indicates line Time The reverse period's self-volatility; The calculation formula used for counting the lines that violate the line power flow safety constraint in any period of time in the power system according to the first net remaining transmission capacity and the second net remaining transmission capacity is: in, represents the first subset of the first forward critical path set, represents the first subset of the first reverse critical path set, represents the second subset of the first forward critical path set, representing a second subset of the first reverse critical path set; The first key line set is: in, and Represents the first critical line set, where if the line The first positive net remaining transmission capacity or the second positive net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first forward critical path set in the first critical path set; if path The first reverse net remaining transmission capacity or the second reverse net remaining transmission capacity If there is any period of time during the day that is less than 0, then the line is an element of the first reverse critical path set in the first critical path set.

6. The method according to claim 5, characterized in that In S5, the maximum and minimum values ​​of the first net remaining transmission capacity and the second net remaining transmission capacity are respectively counted, and the calculation formula used is: in, Indicates line The maximum net forward transmission remaining capacity throughout the day, Indicates line The minimum net forward transmission remaining capacity throughout the day, Indicates line The maximum net reverse transmission remaining capacity for the whole day, Indicates line The minimum net reverse transmission remaining capacity throughout the day; The calculation formula used to calculate the daily fluctuation of each line of the power system is: in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling; The self-fluctuation amount of the time period is analyzed to obtain the first element position of the self-fluctuation amount of the time period that is less than the preset threshold, and the second element corresponding to the first element position in the all-day fluctuation amount is set to 0, and the calculation formula used is: in, Indicates line The positive daily volatility of The result after equal value filling is: Indicates line The reverse daily volatility in the period The result after equal value filling; The third net remaining transmission capacity is calculated according to the first net remaining transmission capacity and the all-day fluctuation, and the calculation formula used is: in, represents the third positive net remaining transmission capacity, represents the first positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the third reverse net remaining transmission capacity, represents the first reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling; The fourth net remaining transmission capacity is calculated according to the second net remaining transmission capacity and the all-day fluctuation, and the calculation formula used is: in, represents the fourth positive net remaining transmission capacity, represents the second positive net remaining transmission capacity, Indicates line The positive daily volatility of The result after equal value filling is: represents the fourth reverse net remaining transmission capacity, represents the second reverse net remaining transmission capacity, Indicates line The reverse daily volatility in the period The result after equal value filling; The calculation formula used for counting the lines that violate the line flow safety constraints in any period of time in the power system is: in, represents the first subset of the second forward critical path set, represents a first subset of the second reverse critical path set, represents the second subset of the second forward critical path set, a second subset representing a second reverse critical path set; The second key path set is: in, and represents the second critical path set, where if the path The calculation result of the forward remaining transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second forward critical path set in the second critical path set; if path The calculation result of the reverse residual transmission capacity is and If the value is less than 0 at any time during the day, the line is an element of the second reverse critical path set in the second critical path set.

7. The method according to claim 6, characterized in that In S6, the third unit combination optimization model is: in, is an infinite value, indicating that there is no constraint on the lines that do not belong to the first critical line set and the second critical line set. If the line belongs to the first critical line set or the second critical line set, the corresponding line flow safety constraint must be applied in all time periods. Represents new energy station In the period The day-ahead forecast output is Representation Node To line The power transfer distribution factor, Representation Node In the period The day-ahead load forecast value; The simplified model of robust unit commitment is: in, , , , , ,vector is the first-stage decision variable, the vector is the second-stage decision variable, vector is restricted to the robust uncertainty set The uncertain parameters, namely the new energy prediction deviation, It's about vector and vector The value function of is the parameter vector of the no-load cost, startup cost and shutdown cost of the unit involved in the optimization, is the variable cost parameter of the optimized units and the penalty cost parameter vector of the power abandonment of the new energy station, the matrix and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the first stage, corresponding to the unit start-stop state constraints and the unit minimum continuous start-stop constraints in the unit combination optimization model. and vector They are the coefficient matrix and constant vector of the independent inequality constraints in the second stage, corresponding to the line safety constraints and the power abandonment constraints of the new energy station in the unit commitment optimization model. ,matrix and vector are the two coefficient matrices and constant vector of the inequality constraints coupled in the first and second stages, corresponding to the upper and lower limits of unit output and the unit ramp constraints in the unit combination optimization model; the matrix ,matrix ,matrix and vector They are the three coefficient matrices and constant vectors of the equality constraints of the second stage and the robust uncertainty set coupling, corresponding to the system supply and demand balance constraints in the unit commitment optimization model.

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