Power system energy and standby scheduling method and device, electronic equipment and medium

Abstract

The invention discloses an electric power system energy and standby scheduling method and device, electronic equipment and a medium, which are used for solving the technical problem of how to consume high-permeability new energy and ensure the safety of an electric power system through electric power system energy and standby scheduling. New energy uncertainty is considered, truncation constraints are introduced, and an energy and standby scheduling model of the power system is constructed; the optimization problem of the energy and standby scheduling model is composed of a first-stage problem and a second-stage problem; performing approximate segmentation considering the total fluctuation of the new energy on the second-stage problem to obtain a plurality of piecewise linear problems; based on a plurality of piecewise linear problems, an energy and standby scheduling model is converted into a deterministic semi-definite programming model in combination with dual variables; and performing optimization solution on the semi-definite programming model through main-sub problem alternative optimization to obtain an energy and standby scheduling result of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimal scheduling, and in particular to a power system energy and reserve scheduling method and device, an electronic equipment and a medium. BACKGROUND

[0002] With the deepening of energy structure transformation, large-scale deployment of new energy has become an important way to achieve energy saving and emission reduction and sustainable development of the power system. New energy generation, especially wind power and photovoltaic power generation, is considered as a key technology to address climate change and reduce carbon emissions due to its cleanliness and renewability.

[0003] However, new energy generation has significant volatility and uncertainty. This poses a serious challenge to the stable and safe operation of the power system. Wind power is affected by wind speed changes, and power output can fluctuate sharply in a short period of time. Photovoltaic power generation is affected by light intensity and weather conditions, and the output shows a clear day-night variation rule. This volatility makes it difficult for the power system to maintain the traditional supply-demand balance, increasing the difficulty of grid peak shaving, frequency modulation and pressure regulation.

[0004] To ensure the safe and stable operation of the power system, the traditional approach is to reserve sufficient reserve capacity during dispatch, mainly relying on fast-regulating power sources such as gas turbines to balance the fluctuations of new energy. However, this approach has obvious limitations. On the one hand, the frequent start-stop of reserve capacity leads to energy waste and increased operating costs. On the other hand, the regulation capacity of traditional power sources is limited, making it difficult to meet the system requirements in the scenario of high penetration rate of new energy. Therefore, how to consume high-penetration new energy and ensure the safety of the power system through scientific and reasonable power system energy and reserve scheduling strategy has become a key problem to be solved in the field of power system planning and operation. SUMMARY

[0005] The present application provides a power system energy and reserve scheduling method, device, electronic equipment and medium, which is used to solve or partially solve the technical problem of how to consume high-penetration new energy and ensure the safety of the power system through power system energy and reserve scheduling.

[0006] The present application provides a power system energy and reserve scheduling method, which comprises:

[0007] Considering the uncertainty of new energy, a truncated constraint is introduced to construct an energy and reserve scheduling model of the power system; the optimization problem of the energy and reserve scheduling model is composed of a first stage problem and a second stage problem;

[0008] The second stage problem is approximately segmented considering the total fluctuation of new energy, obtaining a plurality of segmented linear problems;

[0009] transforming the energy and reserve scheduling model into a deterministic semi-definite programming model in combination with dual variables based on the plurality of piecewise linear problems;

[0010] optimizing and solving the semi-definite programming model through master-slave problem alternation optimization to obtain the energy and reserve scheduling result of the power system.

[0011] Optionally, the energy and reserve scheduling model of the power system is constructed by considering the new energy uncertainty and introducing a truncation constraint, including:

[0012] introducing a truncation constraint as an auxiliary uncertainty in combination with the new energy uncertainty to construct a new energy fuzzy set;

[0013] considering the upper and lower boundaries of the new energy fluctuation and the upper boundary of the auxiliary uncertainty variable to construct an uncertainty set;

[0014] taking the base output of the units and new energy stations in the power system and the reserve capacity provided by the units as first-stage decision variables, and taking the actual output of the units and new energy stations in the power system as second-stage decision variables;

[0015] constructing a first-stage pre-scheduling cost model based on the first-stage decision variables; the optimization problem of the pre-scheduling cost model is a first-stage problem;

[0016] constructing a second-stage optimal operation cost model based on the first-stage decision variables in combination with the real-time observed new energy fluctuation according to the second-stage decision variables; the optimization problem of the optimal operation cost model is a second-stage problem;

[0017] considering the expected value of the new energy prediction to construct a first constraint condition set of the first-stage problem;

[0018] considering the actual value of the new energy to construct a second constraint condition set of the second-stage problem;

[0019] considering the first constraint condition set and the second constraint condition set to minimize the sum of the first-stage pre-scheduling cost and the second-stage expected operation cost as the target, constructing an initial scheduling model of the power system according to the pre-scheduling cost model and the optimal operation cost model;

[0020] simplifying the initial scheduling model based on the new energy fuzzy set, and integrating the simplified model and the uncertainty set as the energy and reserve scheduling model of the power system.

[0021] Optionally, the second-stage problem is considered to be approximately segmented in terms of the total fluctuation of the new energy to obtain a plurality of piecewise linear problems, including:

[0022] extracting an uncertainty set from the energy and reserve scheduling model; the uncertainty set is used to describe a scenario range of new energy uncertainty and introduced auxiliary uncertainty;

[0023] performing linear segmentation on the uncertainty set based on equal probability division according to new energy total fluctuation, to obtain multiple uncertainty set segments;

[0024] performing segmented linearization processing on the second stage problem based on the multiple uncertainty set segments, to obtain multiple segmented operation costs under the second stage; each segmented operation cost is composed of a segmented benchmark generation cost and a segmented adjustment cost;

[0025] respectively arranging optimal problems of each segmented adjustment cost into linear adjustment cost problems, to obtain multiple segmented linear problems.

[0026] Optionally, the performing linear segmentation on the uncertainty set based on equal probability division according to new energy total fluctuation, to obtain multiple uncertainty set segments, comprises:

[0027] obtaining a new energy output fluctuation scenario set of the power system; the new energy output fluctuation scenario set corresponds to different time periods, and each time period corresponds to multiple new energy output fluctuation scenarios;

[0028] dividing the uncertainty set by time period based on the number of time periods corresponding to the new energy output fluctuation scenario set, to obtain uncertainty subsets under different time periods;

[0029] for each time period:

[0030] respectively performing new energy fluctuation summation within each new energy output fluctuation scenario, to obtain new energy total fluctuation corresponding to each new energy output fluctuation scenario;

[0031] arranging each new energy total fluctuation in ascending order, and determining a new energy total fluctuation upper bound corresponding to each segment point based on equal probability principle according to a cumulative probability value;

[0032] performing linear segmentation on the uncertainty subset based on the new energy total fluctuation upper bound of each segment point, to obtain multiple uncertainty subset segments as the uncertainty set segments under the time period.

[0033] Optionally, the converting the energy and reserve scheduling model into a deterministic semi-definite programming model based on the multiple segmented linear problems and the dual variables comprises:

[0034] converting the second stage problem into a dual problem according to an infinite programming duality theory;

[0035] introducing a dual variable constraint of the dual problem based on the plurality of piecewise linear problems;

[0036] transforming the dual variable constraint into a constraint containing free uncertainty according to an S-Lemma theorem, and then equivalently transforming the constraint containing free uncertainty into a deterministic semi-definite constraint;

[0037] optimizing a second-stage objective and multiple constraints of the energy and reserve scheduling model based on the dual problem, the constraint containing free uncertainty, and the semi-definite constraint, to obtain a deterministic semi-definite programming model.

[0038] Optionally, the introducing a dual variable constraint of the dual problem based on the plurality of piecewise linear problems comprises:

[0039] constructing a distribution robust cost constraint of the dual problem based on the plurality of piecewise linear problems;

[0040] transforming the distribution robust cost constraint into a dual variable constraint containing polyhedral uncertainty by introducing a vertex-fixed dual variable.

[0041] Optionally, the optimizing and solving the semi-definite programming model by main-sub problem alternation optimization comprises:

[0042] Step S1: taking the semi-definite programming model as a main problem to be solved, and taking minimization of a feasibility of the dual variable constraint as a sub-problem to be solved; the dual variable constraint corresponds to a dual vertex set;

[0043] Step S2: initializing the dual vertex set, and initializing an iteration step number;

[0044] Step S3: traversing the dual vertex set, solving the main problem, and obtaining a first-stage decision variable and a dual variable of the new energy fuzzy set under an optimal solution;

[0045] Step S4: fixing the first-stage decision variable and the dual variable of the new energy fuzzy set as constants, respectively solving the sub-problem for each piece, determining a dual vertex having the greatest impact on the constraint based on a solving result, and obtaining a sub-problem objective value of each piece;

[0046] Step S5: determining whether each sub-problem objective value meets a preset convergence threshold condition, or whether a current iteration number reaches a maximum iteration number; if one of the conditions is met, ending iteration, and outputting the optimal solution as an energy and reserve scheduling result of the power system; if none of the conditions is met, adding a newly generated dual vertex to the dual vertex set, increasing the iteration step number by 1, and returning to execute Step S3.

[0047] The application further provides a power system energy and reserve scheduling device, comprising:

[0048] A scheduling model construction unit is configured to construct an energy and reserve scheduling model of the power system by considering new energy uncertainty and introducing a truncation constraint; and an optimization problem of the energy and reserve scheduling model is composed of a first stage problem and a second stage problem;

[0049] An approximate segmentation unit is configured to perform approximate segmentation on the second stage problem by considering new energy total fluctuation to obtain a plurality of segmented linear problems;

[0050] A model transformation unit is configured to transform the energy and reserve scheduling model into a deterministic semi-definite programming model based on the plurality of segmented linear problems and in combination with dual variables;

[0051] An optimization solving unit is configured to perform optimization solving on the semi-definite programming model by main-sub problem alternation optimization to obtain an energy and reserve scheduling result of the power system.

[0052] The application further provides an electronic device, which comprises a processor and a memory:

[0053] The memory is configured to store program code and transmit the program code to the processor;

[0054] The processor is configured to execute the power system energy and reserve scheduling method according to the instructions in the program code.

[0055] The application further provides a computer readable storage medium, which is configured to store program code, and the program code is configured to execute the power system energy and reserve scheduling method.

[0056] As can be seen from the above technical solutions, the application has the following advantages:

[0057] A power system energy and reserve scheduling method is provided. Firstly, in order to realize stable and safe operation of the system while consuming new energy, the energy and reserve scheduling model of the power system is constructed by considering the uncertainty of new energy in the energy and reserve distribution robust scheduling of the power system, and introducing a truncation constraint. By introducing the truncation constraint, the possible range of new energy distribution is limited, thereby reducing the conservativeness of the energy and reserve distribution robust scheduling, that is, reducing the system operation cost. The optimization problem of the energy and reserve scheduling model is composed of a first stage problem and a second stage problem. The second stage problem is considered to be approximately segmented according to the total fluctuation of new energy, and a plurality of segmented linear problems are obtained. Thus, by approximating the second stage optimal operation quadratic problem to a segmented linear problem, the difficulty of solving the model is reduced. Based on the plurality of segmented linear problems, the energy and reserve scheduling model is converted into a deterministic semi-definite programming model by combining the dual variables, and the semi-definite programming model is optimized and solved by main-sub problem alternation optimization, to obtain the energy and reserve scheduling result of the power system. The technical scheme provided in the present application can realize economic and safe operation of the power system under the uncertainty of new energy. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0059] Figure 1 A step flow chart of a power system energy and reserve scheduling method;

[0060] Figure 2 An iteration step flow chart of a dual vertex generation method;

[0061] Figure 3 A schematic diagram of the overall flow of a power system energy and reserve scheduling method;

[0062] Figure 4 A structural block diagram of a power system energy and reserve scheduling device. DETAILED DESCRIPTION

[0063] The embodiments of the present application provide a power system energy and reserve scheduling method, device, electronic equipment and medium, which are used to solve or partially solve the technical problem of how to consume high-penetration new energy and ensure the safety of the power system through power system energy and reserve scheduling.

[0064] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] As an example, renewable energy generation exhibits significant volatility and uncertainty. This volatility makes it difficult for the power system to maintain a traditional supply-demand balance, increasing the difficulty of peak shaving, frequency regulation, and voltage regulation. To ensure the safe and stable operation of the power system, the traditional approach is to reserve sufficient standby capacity during dispatching, mainly relying on fast-regulating power sources such as gas turbine units to balance the fluctuations of renewable energy. However, this approach has obvious limitations. On the one hand, frequent start-ups and shutdowns of standby capacity lead to energy waste and increased operating costs. On the other hand, the regulation capacity of traditional power sources is limited, making it difficult to meet the system demands in scenarios with high penetration of renewable energy. Therefore, how to absorb high-penetration renewable energy and ensure power system security through a scientific and reasonable power system energy and standby dispatching strategy has become a key issue that urgently needs to be addressed in the field of power system planning and operation.

[0066] Therefore, one of the core inventive points of this invention is to provide a power system energy and reserve scheduling method based on approximate adaptive partial Bruker optimization. First, to ensure stable and safe operation of the system while absorbing new energy sources, the uncertainty of new energy sources is considered in the power system energy and reserve partial Bruker scheduling. Second, to limit the possible range of new energy distribution, truncation constraints are introduced into the fuzzy set of new energy sources, thereby reducing the conservatism of energy and reserve partial Bruker scheduling, i.e., reducing system operating costs. Finally, to reduce the difficulty of solving the model, the piecewise linearization of the second-stage optimal operation quadratic problem can meet the computational speed requirements during intraday operation.

[0067] Reference Figure 1 The diagram illustrates a flowchart of a power system energy and reserve dispatching method according to an embodiment of the present invention, which may specifically include the following steps:

[0068] Step 101: Considering the uncertainty of new energy sources and introducing truncation constraints, construct an energy and reserve dispatch model for the power system; the optimization problem of the energy and reserve dispatch model consists of a first-stage problem and a second-stage problem.

[0069] This step primarily considers uncertainties and introduces truncation constraints to construct an energy and reserve dispatch model for the power system. Specifically, this process can be achieved by executing the following steps S1011 to S1019:

[0070] Step S1011: Introduce truncation constraints as auxiliary uncertainties, and construct a new energy fuzzy set by combining them with new energy uncertainties;

[0071] Step S1012: Simultaneously consider the upper and lower boundaries of new energy fluctuations and the upper boundary of auxiliary uncertainty variables to construct an uncertainty set;

[0072] Step S1013: The baseline output of generating units and renewable energy plants in the power system, as well as the reserve capacity provided by the generating units, are used as the decision variables for the first stage, and the actual output of generating units and renewable energy plants in the power system are used as the decision variables for the second stage.

[0073] Step S1014: Based on the decision variables of the first stage, construct the pre-scheduling cost model for the first stage; the optimization problem of the pre-scheduling cost model is the problem of the first stage.

[0074] Step S1015: Based on the decision variables of the first stage and combined with the real-time observed fluctuations in new energy sources, construct the optimal operating cost model for the second stage according to the decision variables of the second stage; the optimization problem of the optimal operating cost model is the problem of the second stage.

[0075] Step S1016: Considering the expected value of new energy prediction, construct the first set of constraints for the first stage problem;

[0076] Step S1017: Considering the actual value of new energy, construct the second set of constraints for the second stage problem;

[0077] Step S1018: Simultaneously consider the first set of constraints and the second set of constraints, with the goal of minimizing the sum of the pre-scheduling cost of the first stage and the expected operating cost of the second stage, construct the initial scheduling model of the power system based on the pre-scheduling cost model and the optimal operating cost model;

[0078] Step S1019: Simplify the initial scheduling model based on the new energy fuzzy set, and integrate the simplified model and uncertainty set as the energy and reserve scheduling model of the power system.

[0079] The following, in conjunction with the aforementioned embodiments, details the specific implementation process of establishing an energy and reserve dispatch model for a power system that considers the uncertainties of new energy sources (i.e., a power system energy and reserve distributed bar dispatch model).

[0080] The first step is to establish an initial dispatch model for power system energy and reserves.

[0081] Power system energy and reserve distribution rod dispatch is performed during the intraday operation phase. It is essentially a two-stage optimization problem. The first stage pre-determines the baseline output of generating units and renewable energy plants, as well as the reserve capacity provided by the units. The second stage adjusts the generating units within their reserve capacity range based on real-time observed fluctuations in renewable energy supply, thus balancing these fluctuations in a timely manner.

[0082] The power system energy and reserve dispatch model aims to minimize the sum of the expected pre-dispatch costs in the first phase and the expected operating costs in the second phase. An initial dispatch model can be constructed as shown below:

[0083] (1a)

[0084] st (the set of first constraints)

[0085] (1b)

[0086] (1c)

[0087] (1d)

[0088] (1e)

[0089] (1f)

[0090] (1g)

[0091] Equation (1a) represents the initial scheduling model; Equations (1b) to (1g) are the set of constraints for the first stage problem (defined as the first set of constraints for ease of distinction). The constraints for the first stage are set for the expected value of new energy prediction.

[0092] In equation (1a), Index for scheduling periods; and The units and new energy power stations The reference output; and For the unit The lower and upper limits of the provided reserve capacity; and These are the lower and upper reserve cost coefficients, respectively. This indicates the uncertainty in the distribution of new energy sources; For new energy fuzzy set, that is, the maximum possible range of uncertainty in the distribution of new energy; In the first phase Determine the uncertainty of real-time new energy fluctuations in the second phase. The optimal operating cost for the subsequent second stage was observed; To find the expected value function; first-stage decision variables .

[0093] Equation (1b) represents the upper and lower limits of the unit's reference output. and For the unit The lower and upper limits of the output. Equations (1c) and (1d) are the unit ramp-up constraints; and Separate units The downward and upward ramp rates. Equation (1e) represents the benchmark output constraint for new energy sources; For new energy power stations The expected available output power. Equation (1f) is the line reference transmission power limit; , , The power transfer factor represents the effect of changes in power injection at each node on the branch. The impact of transmission power; For nodes The load; and branch road The lower and upper bounds of the transmission power. Equation (1g) is the power balance constraint under the reference scenario.

[0094] In the second phase, the system observes fluctuations in renewable energy sources and adjusts accordingly to restore power balance. The problem in the second phase is:

[0095] (2a)

[0096] st (second set of constraints)

[0097] (2b)

[0098] (2c)

[0099] (2d)

[0100] (2e)

[0101] In the formula, equation (2a) is the objective function of the second-stage problem, and equations (2b) to (2e) are the set of constraints for the second-stage problem (defined as the second constraint set for ease of distinction). The constraints in the second stage are set for the actual values ​​of new energy.

[0102] In equation (2a), For the second phase of the problem, the unit Actual output; , , For the unit The power generation cost coefficient; For new energy power stations Available output fluctuations; To truly contribute to it; The cost coefficient for wind curtailment penalties.

[0103] Equation (2b) represents the upper and lower limits of the actual output of the generating unit; Equation (2c) represents the actual output of new energy sources; Equation (2d) represents the actual transmission power limit of the line; and Equation (2e) represents the power balance constraint in the actual scenario.

[0104] Equations (1) to (2) are detailed models of power system energy and reserve dispatch, and their compact form is as follows:

[0105] (3)

[0106] In the formula,

[0107] (4)

[0108] In the formula, and These are the decision variables for the first and second stages, respectively. ; It is a quadratic function; Let be a vector of cost coefficients for uncertain variables; , , , , It is a constant matrix; , It is a constant vector.

[0109] A fuzzy set of new energy sources represents the maximum possible range of the probability distribution of new energy sources. The method is based on moment information. Furthermore, this invention introduces distribution constraints to reduce the conservatism of the new energy fuzzy set.

[0110] (5)

[0111] In the formula, The introduced auxiliary uncertainty is used to represent the distribution constraint; the first row represents the uncertainty. Falling on the set of uncertainty Inside, This refers to probability, i.e., scenario. Falling into the set The probability is equal to 1, in other words, the scenario The maximum fluctuation range is the set The second line represents the expected value of the uncertainty in the fluctuation of new energy sources. The third line represents the introduced distribution constraints, used to limit the possible range of new energy distribution; the fourth line represents the covariance constraints. This is the covariance matrix of the new energy fluctuations.

[0112] Distributed constraints are represented by the following truncation constraints:

[0113] (6a)

[0114] (6b)

[0115] in, This is the cutoff constant; To truncate the constraint index; To truncate the number of constraints.

[0116] This invention reduces the conservatism of energy and reserve distribution scheduling by introducing truncation constraints into the fuzzy set of new energy sources to limit the possible range of new energy distribution.

[0117] Uncertainty set The purpose of this study is to describe the uncertainty of new energy fluctuations. and auxiliary uncertainty Scope of scenarios:

[0118] (7)

[0119] In the formula, and These represent the lower and upper bounds of new energy fluctuations. This serves as an upper bound for uncertain variables.

[0120] Step 102: Approximately segment the second stage problem by considering the total fluctuation of new energy sources, and obtain multiple piecewise linear problems;

[0121] This step primarily involves segmenting the uncertainty set of new energy sources based on the total fluctuation of new energy sources, and approximating the optimal quadratic problem of the second stage as a piecewise linear problem. Specifically, the second stage problem is approximated by segmenting it to account for the total fluctuation of new energy sources, resulting in multiple piecewise linear problems.

[0122] More specifically, the process of approximating the piecewise segmentation of the total fluctuation of new energy sources to obtain multiple piecewise linear problems in the second stage problem can be achieved by executing the following steps S1021 to S1024:

[0123] Step S1021: Extract the uncertainty set from the energy and reserve scheduling model; the uncertainty set is used to describe the scenario range of new energy uncertainty and introduced auxiliary uncertainty;

[0124] Step S1022: Based on the total fluctuation of new energy sources, perform linear segmentation of the uncertainty set based on equal probability partitioning to obtain multiple uncertainty set segments;

[0125] Step S1023: Based on multiple uncertainty sets, the second-stage problem is segmented and linearized to obtain multiple segmented operating costs under the second stage; each segmented operating cost consists of the segmented benchmark power generation cost and the segmented adjustment cost;

[0126] Step S1024: The optimization problem of each piecewise adjustment cost is reorganized into a linear adjustment cost problem, resulting in multiple piecewise linear problems.

[0127] In some embodiments, the implementation process of step S1022 may include the following steps S11 to S15:

[0128] Step S11: Obtain the set of new energy power output fluctuation scenarios of the collected power system; the set of new energy power output fluctuation scenarios corresponds to different time periods, and each time period corresponds to multiple new energy power output fluctuation scenarios;

[0129] Step S12: Based on the number of time periods corresponding to the set of new energy power output fluctuation scenarios, divide the uncertainty set into time periods to obtain the uncertainty subsets under different time periods;

[0130] For each time period, there are steps S13 to S15.

[0131] Step S13: Sum the new energy fluctuations within each new energy output fluctuation scenario to obtain the total new energy fluctuation corresponding to each new energy output fluctuation scenario;

[0132] Step S14: Sort the total fluctuations of each new energy source in ascending order, and determine the upper bound of the total fluctuations of the new energy source corresponding to each segment point based on the principle of equal probability according to the cumulative probability value.

[0133] Step S15: Based on the upper bound of the total fluctuation of new energy at each segment point, perform linear segmentation on the uncertainty subset to obtain multiple uncertainty subset segments, which serve as uncertainty set segments for the time period.

[0134] The following describes in detail the specific implementation process of approximating the second-stage optimal quadratic problem as a piecewise linear problem, based on the content of the foregoing embodiments.

[0135] As shown in equation (2), the second-stage problem is a quadratic problem, which makes the energy and reserve distribution of the power system difficult to solve. Therefore, the second-stage problem can be piecewise linearized.

[0136] First, the uncertainty set of new energy sources for each time period is segmented according to the total fluctuation of new energy sources:

[0137] (8)

[0138] In the formula, For segmented indexes; For time period Segmentation The upper limit of the total fluctuation of new energy sources.

[0139] Secondly, the second-stage quadratic problem is piecewise linearized:

[0140] (9)

[0141] In the formula, The baseline output of the units in each segment; The baseline power generation cost for each segment; Adjustment cost for each segment.

[0142] The detailed calculation formula for adjustment costs under each segment is as follows:

[0143] (10a)

[0144] st (constraints):

[0145] (10b)

[0146] (10c)

[0147] (10d)

[0148] (10e)

[0149] In the formula, For segmentation The unit adjustment cost coefficient in the text; For segmentation medium-sized units Power adjustment; For segmentation China's new energy scenarios The actual output; equations (10b) to (10e) are respectively segmented The constraints include unit output constraints, new energy output constraints, linear transmission power constraints, and power balance constraints.

[0150] The second-stage adjustment cost linearity problem in segmentation This can be summarized in the following compact form:

[0151] (11)

[0152] Furthermore, this invention proposes an equal probability principle to achieve high-quality piecewise linearization. Specifically, firstly, a sufficient number of renewable energy output fluctuation scenarios are sampled based on the empirical distribution of renewable energy; then, the data is sorted in ascending order according to the sum of the total fluctuations; finally, based on the cumulative probability value, the upper bound of the total renewable energy fluctuation corresponding to each segment point is determined with equal probability. (Assuming the total number of segments is) ).

[0153] More specifically, assuming sampling Each new energy scenario, for each time period, is specifically designed for... For each new energy scenario, sum the fluctuations in new energy within that scenario and arrange them in ascending order (from smallest to largest). Then, each time period corresponds to... Total fluctuation value in ascending order The first segment endpoint Take the minimum value And the rest of the first Each segment endpoint represents the upper bound of the total fluctuation of new energy. correspond The index value is Here, round{} is the rounding function.

[0154] Furthermore, in addition to optimizing the segment endpoints, the number of segments can also be optimized. Specifically, 20 segment numbers are selected from 1 to 20, and the model is optimized independently for each number. Then, the results for each number of segments are compared, including computational speed and the degree of approximation to the original quadratic problem. Finally, based on scheduling requirements, an optimal number of segments is selected as a compromise.

[0155] This invention proposes an equal probability principle to optimize the segment endpoints and number of segments of the uncertainty set of new energy sources, thereby achieving high-quality piecewise linearization of the second-stage quadratic problem.

[0156] Step 103: Based on the multiple piecewise linear problems, the energy and reserve scheduling model is transformed into a deterministic semidefinite programming model by combining dual variables;

[0157] This step is primarily based on the duality theory of infinite programming, and utilizes the relaxation of the optimal operating cost in the second stage at the dual vertex to reduce the uncertainty of new energy distribution to scenario uncertainty. Then, the S-Lemma theorem is used to transform the distributed bar cost constraint considering scenario uncertainty into a deterministic semidefinite constraint, thereby converting the scheduling model containing the uncertainty of new energy fluctuations into a deterministic semidefinite programming model.

[0158] Specifically, the process of transforming the energy and reserve scheduling model into a deterministic semidefinite programming model based on multiple piecewise linear problems and combining dual variables can include the following steps S1031 to S1034:

[0159] Step S1031: Based on the duality theory of infinite programming, transform the second-stage problem into a dual problem;

[0160] Step S1032: Based on multiple piecewise linear problems, introduce dual variables to construct dual variable constraints for the dual problem;

[0161] Step S1033: According to the S-Lemma theorem, the dual variable constraint is transformed into a constraint with free uncertainty, and then the constraint with free uncertainty is equivalent to a deterministic semidefinite constraint.

[0162] Step S1034: Based on the dual problem, constraints with free uncertainty and semidefinite constraints, optimize the second-stage solution objective and multiple constraints of the energy and reserve scheduling model to obtain a deterministic semidefinite programming model.

[0163] Furthermore, based on multiple piecewise linear problems, dual variables are introduced to construct dual variable constraints for the dual problem. Specifically, this can include: constructing the partial Brussels cost constraint for the dual problem based on multiple piecewise linear problems; introducing dual variables with fixed vertices (i.e., fixing the dual variables to the vertices of their feasible domains) to transform the partial Brussels cost constraint into a dual variable constraint containing polyhedral uncertainty.

[0164] The following describes in detail the specific implementation process of transforming the energy and reserve scheduling model into a deterministic semidefinite programming model, in conjunction with the aforementioned embodiments.

[0165] First, the uncertainty of new energy distribution is reduced to scenario uncertainty.

[0166] Specifically, based on the duality theory of infinite programming, the max problem in the energy and reserve scheduling model is addressed, namely... Dualizing it, we get:

[0167] (12a)

[0168] (12b)

[0169] In the formula, , , , Let be the dual variable corresponding to each row of constraints in the new energy fuzzy set (5); ; For segmentation The benchmark generation cost in the data. Constraint (12b) is the sub-Bluerg cost constraint.

[0170] Second-stage adjustment cost in constraint (12b) The dual problem is:

[0171] (13)

[0172] In the formula, For terms that do not contain uncertain variables; Let be the dual variable corresponding to each row constraint in the second-stage adjustment cost linear problem (11).

[0173] Adjust the second stage cost in constraint (12b). Expressed in terms of its dual problem, constraint (12b) becomes:

[0174] (14)

[0175] In equation (14), the uncertainty variable and dual variables Simultaneously maximizing the constraint infeasibility, and both constituting a bilinear term. The dual variable can be... If the vertex is fixed, then equation (14) becomes:

[0176] (15)

[0177] In the formula, ; dual variables The index of the vertex; It is the set of dual vertices.

[0178] Then, the uncertain cost constraints of the sub-bars are transformed into deterministic semi-deterministic constraints.

[0179] According to the S-Lemma theorem, uncertainty involving polyhedra The constraint (15) can be transformed into one containing free uncertainty. Constraints:

[0180] (16)

[0181] In the formula, and In segmentation and apex Below, uncertainty set That is, the dual multipliers of the upper and lower bound constraints in the first row of equation (8); and For the dual multipliers of the upper and lower bound constraints in the second row of equation (8); For the dual multiplier of the constraint in the third row of equation (8); and The dual multiplier of the upper and lower bound constraints in the fourth row of equation (8).

[0182] Constraint (16) can be expressed in the following matrix form:

[0183] (17)

[0184] In the formula,

[0185] (18)

[0186] (19)

[0187] (20)

[0188] Then constraint (17) can be further equivalent to the semidefinite constraint shown below:

[0189] (twenty one)

[0190] Therefore, power system energy and reserve dispatch is ultimately transformed into:

[0191] (twenty two)

[0192] Step 104: The semidefinite programming model is optimized and solved by alternating principal-subproblem optimization to obtain the energy and reserve scheduling results of the power system.

[0193] This invention proposes a finite traversal technique based on dual vertex generation. By alternately optimizing the transformed semidefinite programming model through master-subproblem optimization, the effective dual vertices are uploaded to the master problem during the iterative solution process, thus tightening the optimal operating cost in the second stage. Specifically, the semidefinite programming model is optimized through alternating master-subproblem optimization to obtain the energy and reserve scheduling results of the power system. Further, this process can be implemented by executing the following steps S1 to S5:

[0194] Step S1: Treat the semidefinite programming model as the main problem to be solved, and minimize the feasibility of the dual variable constraints as the subproblems to be solved; the dual variable constraints correspond to the dual vertex set;

[0195] Step S2: Initialize the dual vertex set and the iteration step number;

[0196] Step S3: Traverse the dual vertex set, solve the main problem, and obtain the first-stage decision variables under the optimal solution and the dual variables of the new energy fuzzy set;

[0197] Step S4: Fix the dual variables of the first-stage decision variables and the new energy fuzzy set to constants, solve the sub-problems for each segment, determine the dual vertex with the greatest impact on the constraints based on the solution results, and obtain the target value of the sub-problem for each segment.

[0198] Step S5: Determine whether the objective values ​​of each subproblem meet the preset convergence threshold condition, or whether the current iteration number has reached the maximum iteration number; if either condition is met, end the iteration, output the optimal solution as the energy and reserve scheduling result of the power system; if neither condition is met, add the newly generated dual vertex to the dual vertex set, increment the iteration step by 1, and return to execute step S3.

[0199] The following is a combination of the content of the foregoing embodiments and Figure 2 The optimization solution process of the semidefinite programming model is explained in detail.

[0200] In extreme cases, dual variables All vertices should be included to ensure that the cost constraint (12b) is tight. However, the number of dual vertices is very large, and traversing all vertices is obviously impractical. Therefore, we can find the dual vertices that have the greatest impact on constraint (12b), and traversing only these finite dual vertices can ensure that constraint (12b) is tight. Based on this, the specific process of using the dual vertex generation method to calculate problem (22) is as follows:

[0201] 1) Initialization: Initialize the dual vertex set The set is empty, so the iteration step n = 0 is initialized.

[0202] 2) Solving the main problem: Solve the main problem (22) to obtain the first-stage decision variables under the optimal solution. And the dual variables of new energy fuzzy sets , , , .

[0203] 3) Subproblem solving: Fix the above variables as constants, and solve the following subproblems for each segment:

[0204] (twenty three)

[0205] The above problem will yield a dual vertex that has the greatest impact on the constraints. Define the objective value of each subproblem as follows: .

[0206] 4) Convergence check: If Established, among which If the value is a very small positive constant, the algorithm converges, and the optimal solution is output as the model solution result; otherwise, the newly generated dual vertex is... Add to collection Let n = n + 1, and return to step 2.

[0207] This invention provides a power system energy and reserve scheduling method based on approximate adaptive partial Blule bar optimization. First, to ensure stable and safe operation of the system while absorbing renewable energy, a power system energy and reserve partial Blule bar scheduling model considering the uncertainty of renewable energy is established. Simultaneously, by introducing truncation constraints into the fuzzy set of renewable energy sources, the possible range of renewable energy distribution is limited, thereby reducing the conservatism of energy and reserve partial Blule bar scheduling, i.e., reducing system operating costs. Second, by approximating the second-stage optimal operation quadratic problem as a piecewise linear problem, the difficulty of solving the model is reduced. Furthermore, through the proposed equal probability principle, high-quality piecewise linearization of the second-stage quadratic problem is achieved.

[0208] To solve the model, firstly, the cost constraints of the biscrow bars are generated based on the dual theory of infinite programming. Secondly, the optimal operating cost of the second stage is relaxed using the dual vertices, and the biscrow bar cost constraints are transformed into deterministic semidefinite constraints based on the S-Lemma theorem. Finally, a dual vertex generation method is proposed, and the transformed model is solved by alternately optimizing the principal and subproblems to obtain the final scheduling result. The method proposed in this embodiment of the invention can achieve economical and safe operation of the power system under the uncertainty of new energy sources.

[0209] For better illustration, refer to Figure 3 This diagram illustrates the overall flow of a power system energy and reserve dispatching method according to an embodiment of the present invention. It should be noted that this embodiment only provides a brief description of the general flow of power system energy and reserve dispatching. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments, and will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0210] The first step is the model building and piecewise linearization process.

[0211] Specifically, a power system energy and reserve distributed bar scheduling model that takes into account the uncertainty of new energy sources is first established, and then the second-stage optimal operation quadratic problem of the model is approximated as a piecewise linear problem.

[0212] The second step is to transform uncertain linear constraints into deterministic constraints.

[0213] Specifically, based on the piecewise linear problem, the cost constraints of the sub-Brubars are first generated using the dual theory of wireless planning. Then, the second-stage optimal operating cost of the dual vertex relaxation model is used. Finally, based on the S-Lemma theorem, the cost constraints of the sub-Brubars are transformed into deterministic semidefinite constraints, so as to transform the energy and spare sub-Brubars scheduling model into a deterministic semidefinite programming model.

[0214] Finally, there is the final iterative solution process for the model.

[0215] Specifically, the dual vertex generation method is used to solve the transformed model by alternately optimizing the main and subproblems to obtain the final scheduling result.

[0216] Reference Figure 4 The diagram illustrates a structural block diagram of a power system energy and reserve dispatching device according to an embodiment of the present invention, which may specifically include:

[0217] The scheduling model construction unit 401 is used to consider the uncertainty of new energy sources and introduce truncation constraints to construct the energy and reserve scheduling model of the power system; the optimization problem of the energy and reserve scheduling model consists of a first-stage problem and a second-stage problem.

[0218] The approximate piecewise unit 402 is used to approximate piecewise division of the second-stage problem considering the total fluctuation of new energy sources, thereby obtaining multiple piecewise linear problems;

[0219] Model transformation unit 403 is used to transform the energy and reserve scheduling model into a deterministic semidefinite programming model based on the multiple piecewise linear problems and in combination with dual variables.

[0220] The optimization solution unit 404 is used to optimize the semidefinite programming model by alternating principal-subproblem optimization to obtain the energy and reserve scheduling results of the power system.

[0221] In one optional embodiment, the scheduling model construction unit 401 includes:

[0222] The new energy fuzzy set construction unit is used to introduce truncation constraints as auxiliary uncertainties and combine them with new energy uncertainties to construct new energy fuzzy sets;

[0223] Uncertainty set construction unit, used to simultaneously consider the upper and lower boundaries of new energy fluctuations and the upper boundary of auxiliary uncertainty variables to construct uncertainty sets;

[0224] The decision variable determination unit is used to take the benchmark output of the generating units and new energy power plants in the power system, as well as the reserve capacity provided by the generating units, as the first-stage decision variables, and the actual output of the generating units and new energy power plants in the power system as the second-stage decision variables.

[0225] The pre-scheduling cost model construction unit is used to construct a pre-scheduling cost model for the first stage based on the decision variables of the first stage; the optimization problem of the pre-scheduling cost model is the problem of the first stage.

[0226] The optimal operating cost model construction unit is used to construct the optimal operating cost model for the second stage based on the decision variables of the first stage, combined with the real-time observed fluctuations of new energy sources, and according to the decision variables of the second stage; the optimization problem of the optimal operating cost model is the second stage problem.

[0227] The first constraint set construction unit is used to consider the expected value of new energy prediction and construct the first constraint set of the first stage problem.

[0228] The second constraint set construction unit is used to consider the actual value of new energy and construct the second constraint set of the second stage problem.

[0229] The initial scheduling model construction unit is used to simultaneously consider the first set of constraints and the second set of constraints, with the goal of minimizing the sum of the pre-scheduling cost of the first stage and the expected operating cost of the second stage, and to construct the initial scheduling model of the power system based on the pre-scheduling cost model and the optimal operating cost model.

[0230] The model integration unit is used to simplify the initial scheduling model based on the new energy fuzzy set, and integrate the simplified model and the uncertainty set as the energy and reserve scheduling model of the power system.

[0231] In one alternative embodiment, the approximate segmentation unit 402 includes:

[0232] An uncertainty set extraction unit is used to extract an uncertainty set from the energy and reserve scheduling model; the uncertainty set is used to describe the scenario range of new energy uncertainty and introduced auxiliary uncertainty;

[0233] A linear segmentation unit is used to perform linear segmentation of the uncertainty set based on equal probability partitioning according to the total fluctuation of new energy sources, thereby obtaining multiple uncertainty set segments;

[0234] The piecewise linearization processing unit is used to perform piecewise linearization processing on the second-stage problem based on the multiple uncertainty sets to obtain multiple segmented operating costs under the second stage; each segmented operating cost consists of segmented benchmark power generation cost and segmented adjustment cost.

[0235] The piecewise adjustment cost sorting unit is used to sort out each of the piecewise adjustment cost optimization problems into adjustment cost linear problems, thereby obtaining multiple piecewise linear problems.

[0236] In one alternative embodiment, the linear segmentation unit includes:

[0237] A new energy power output fluctuation scenario set acquisition unit is used to acquire the new energy power output fluctuation scenario set of the power system; the new energy power output fluctuation scenario set corresponds to different time periods, and each time period corresponds to multiple new energy power output fluctuation scenarios;

[0238] An uncertainty subset partitioning unit is used to partition the uncertainty set according to the number of time periods corresponding to the new energy output fluctuation scenario set, thereby obtaining uncertainty subsets under different time periods;

[0239] The total fluctuation calculation unit for new energy is used to sum the new energy fluctuations within each of the new energy output fluctuation scenarios to obtain the total fluctuation of new energy corresponding to each new energy output fluctuation scenario.

[0240] The unit for determining the upper bound of total new energy fluctuations is used to arrange the total new energy fluctuations in ascending order and determine the upper bound of total new energy fluctuations corresponding to each segment point based on the principle of equal probability and the cumulative probability value.

[0241] The linear segmentation subunit is used to linearly segment the uncertainty subset based on the upper bound of the total fluctuation of new energy at each segmentation point, thereby obtaining multiple uncertainty subset segments as uncertainty set segments under the time period.

[0242] In one optional embodiment, the model conversion unit 403 includes:

[0243] The dual problem transformation unit is used to transform the second-stage problem into a dual problem according to the duality theory of infinite programming.

[0244] The dual variable constraint construction unit is used to introduce dual variables to construct the dual variable constraints of the dual problem based on the multiple piecewise linear problems;

[0245] The semidefinite constraint construction unit is used to transform the dual variable constraint into a constraint with free uncertainty according to the S-Lemma theorem, and then transform the constraint with free uncertainty into a deterministic semidefinite constraint.

[0246] The model optimization unit is used to optimize the second-stage solution objective and multiple constraint conditions of the energy and reserve scheduling model based on the dual problem, the constraints containing free uncertainty, and the semidefinite constraints, so as to obtain a deterministic semidefinite programming model.

[0247] In one alternative embodiment, the dual variable constraint construction unit includes:

[0248] The sub-Bruker cost constraint construction unit is used to construct the sub-Bruker cost constraint of the dual problem based on the multiple piecewise linear problems.

[0249] The dual variable constraint transformation unit is used to introduce dual variables with fixed vertices to transform the bibliometric cost constraint into a dual variable constraint containing polyhedral uncertainty.

[0250] In one optional embodiment, the optimization solving unit 404 is specifically used to perform the following steps S1 to S5:

[0251] Step S1: Take the semidefinite programming model as the main problem to be solved, and take minimizing the feasibility of the dual variable constraints as the subproblem to be solved; the dual variable constraints correspond to the dual vertex set;

[0252] Step S2: Initialize the dual vertex set and the iteration step number;

[0253] Step S3: Traverse the set of dual vertices, solve the main problem, and obtain the first-stage decision variables under the optimal solution and the dual variables of the new energy fuzzy set;

[0254] Step S4: Fix the decision variables of the first stage and the dual variables of the new energy fuzzy set to constants, solve the sub-problem for each segment, determine the dual vertex with the greatest impact on the constraints based on the solution results, and obtain the sub-problem objective value for each segment;

[0255] Step S5: Determine whether the objective values ​​of each subproblem meet the preset convergence threshold condition, or whether the current iteration number has reached the maximum iteration number; if either condition is met, end the iteration and output the optimal solution as the energy and reserve scheduling result of the power system; if neither condition is met, add the newly generated dual vertex to the dual vertex set, increment the iteration step by 1, and return to execute step S3.

[0256] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0257] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, the embodiments of the present invention use "first" and "second" to distinguish and describe some technical features. "First" and "second" are only used to distinguish data and have no other special meaning. It is understood that the present invention does not impose any limitations on them.

[0258] This invention also provides an electronic device, which includes a processor and a memory:

[0259] The memory is used to store program code and transfer the program code to the processor;

[0260] The processor is used to execute the power system energy and reserve scheduling method of any embodiment of the present invention according to the instructions in the program code.

[0261] This invention also provides a computer-readable storage medium for storing program code for executing the power system energy and reserve dispatch method of any embodiment of this invention.

[0262] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0263] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0264] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0265] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0266] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0267] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power system energy and reserve dispatching method, characterized in that, include: Considering the uncertainties of new energy sources and introducing truncation constraints, an energy and reserve dispatch model for the power system is constructed. The optimization problem of the energy and reserve scheduling model consists of a first-stage problem and a second-stage problem; The second-stage problem is approximated by piecewise division considering the total fluctuation of new energy sources, resulting in multiple piecewise linear problems; Based on the aforementioned piecewise linear problems, the energy and reserve scheduling model is transformed into a deterministic semidefinite programming model by combining dual variables. The semidefinite programming model is optimized and solved by alternating principal-subproblem optimization to obtain the energy and reserve scheduling results of the power system.

2. The power system energy and reserve dispatch method according to claim 1, characterized in that, The aforementioned energy and reserve dispatch model for the power system, considering the uncertainties of new energy sources and introducing truncation constraints, includes: A truncation constraint is introduced as an auxiliary uncertainty, and combined with the uncertainty of new energy, a new energy fuzzy set is constructed; Simultaneously considering the upper and lower boundaries of new energy fluctuations and the upper boundary of auxiliary uncertainty variables, an uncertainty set is constructed; The baseline output of generating units and renewable energy power plants in the power system, as well as the reserve capacity provided by the generating units, are used as the decision variables for the first stage, and the actual output of generating units and renewable energy power plants in the power system are used as the decision variables for the second stage. Based on the decision variables of the first stage, a pre-scheduling cost model for the first stage is constructed; the optimization problem of the pre-scheduling cost model is the problem of the first stage. Based on the decision variables of the first stage, and combined with the real-time observed fluctuations in new energy sources, an optimal operating cost model for the second stage is constructed according to the decision variables of the second stage; the optimization problem of the optimal operating cost model is the problem of the second stage. Considering the expected value of new energy predictions, construct the first set of constraints for the first stage problem; Considering the actual value of new energy sources, construct the second set of constraints for the second stage problem; Simultaneously considering the first set of constraints and the second set of constraints, with the goal of minimizing the sum of the first-stage pre-scheduling cost and the second-stage expected operating cost, an initial scheduling model of the power system is constructed based on the pre-scheduling cost model and the optimal operating cost model. The initial scheduling model is simplified based on the new energy fuzzy set, and the simplified model and the uncertainty set are integrated as the energy and reserve scheduling model of the power system.

3. The power system energy and reserve dispatch method according to claim 1, characterized in that, The second-stage problem is approximated by piecewise division considering the total fluctuation of new energy sources, resulting in multiple piecewise linear problems, including: An uncertainty set is extracted from the energy and reserve scheduling model; the uncertainty set is used to describe the scenario range of new energy uncertainty and introduced auxiliary uncertainty; Based on the total fluctuation of new energy sources, the uncertainty set is linearly segmented based on equal probability partitioning to obtain multiple uncertainty set segments; Based on the segmentation of the multiple uncertainty sets, the second-stage problem is subjected to segmented linearization to obtain multiple segmented operating costs under the second stage; each segmented operating cost consists of the segmented benchmark power generation cost and the segmented adjustment cost. The optimization problems of each piecewise adjustment cost are each reorganized into linear adjustment cost problems, resulting in multiple piecewise linear problems.

4. The power system energy and reserve dispatch method according to claim 3, characterized in that, The uncertainty set is linearly segmented based on equal probability partitioning according to the total fluctuation of new energy sources, resulting in multiple uncertainty set segments, including: Obtain a set of new energy power output fluctuation scenarios for the power system; the set of new energy power output fluctuation scenarios corresponds to different time periods, and each time period corresponds to multiple new energy power output fluctuation scenarios. Based on the number of time periods corresponding to the set of new energy output fluctuation scenarios, the uncertainty set is divided into time periods to obtain uncertainty subsets under different time periods; For each time period: The total new energy fluctuation corresponding to each new energy output fluctuation scenario is obtained by summing the new energy fluctuations within the scenario for each new energy output fluctuation scenario. Arrange the total fluctuations of each of the aforementioned new energy sources in ascending order, and determine the upper bound of the total fluctuations of new energy sources corresponding to each segment point based on the principle of equal probability, according to the cumulative probability value. Based on the upper bound of the total fluctuation of new energy at each of the segment points, the uncertainty subset is linearly segmented to obtain multiple uncertainty subset segments, which serve as uncertainty set segments under the time period.

5. The power system energy and reserve dispatch method according to claim 2, characterized in that, The process of transforming the energy and reserve scheduling model into a deterministic semidefinite programming model based on the multiple piecewise linear problems and combining dual variables includes: According to the duality theory of infinite programming, the second-stage problem is transformed into a dual problem; Based on the aforementioned piecewise linear problems, dual variables are introduced to construct dual variable constraints for the dual problems; According to the S-Lemma theorem, the dual variable constraint is transformed into a constraint with uncertainty of freedom, and then the constraint with uncertainty of freedom is equivalent to a deterministic semidefinite constraint. Based on the dual problem, the constraints containing free uncertainty, and the semidefinite constraints, the second-stage solution objective and multiple constraints of the energy and reserve scheduling model are optimized to obtain a deterministic semidefinite programming model.

6. The power system energy and reserve dispatch method according to claim 5, characterized in that, The method of introducing dual variables to construct dual variable constraints for the dual problem based on the multiple piecewise linear problems includes: Based on the multiple piecewise linear problems, construct the bibliometric cost constraint for the dual problem; By introducing dual variables with fixed vertices, the cost constraint of the sub-bar is transformed into a dual variable constraint containing polyhedral uncertainty.

7. The power system energy and reserve dispatch method according to claim 5 or 6, characterized in that, The optimization of the semidefinite programming model through alternating principal-subproblem optimization to obtain the energy and reserve scheduling results of the power system includes: Step S1: Take the semidefinite programming model as the main problem to be solved, and take minimizing the feasibility of the dual variable constraints as the subproblem to be solved; the dual variable constraints correspond to the dual vertex set; Step S2: Initialize the dual vertex set and the iteration step number; Step S3: Traverse the set of dual vertices, solve the main problem, and obtain the first-stage decision variables under the optimal solution and the dual variables of the new energy fuzzy set; Step S4: Fix the decision variables of the first stage and the dual variables of the new energy fuzzy set to constants, solve the sub-problem for each segment, determine the dual vertex with the greatest impact on the constraints based on the solution results, and obtain the sub-problem objective value for each segment; Step S5: Determine whether the objective values ​​of each sub-problem meet the preset convergence threshold condition, or whether the current iteration number has reached the maximum iteration number; if either condition is met, end the iteration, output the optimal solution as the energy and reserve scheduling result of the power system; if neither condition is met, add the newly generated dual vertex to the dual vertex set, increment the iteration step by 1, and return to execute step S3.

8. A power system energy and reserve dispatching device, characterized in that, include: The scheduling model construction unit is used to consider the uncertainty of new energy sources and introduce truncation constraints to construct the energy and reserve scheduling model of the power system; the optimization problem of the energy and reserve scheduling model consists of a first-stage problem and a second-stage problem. The approximate piecewise unit is used to approximate piecewise division of the second-stage problem considering the total fluctuation of new energy sources, resulting in multiple piecewise linear problems; The model transformation unit is used to transform the energy and reserve scheduling model into a deterministic semidefinite programming model based on the multiple piecewise linear problems and in combination with dual variables. The optimization solution unit is used to optimize the semidefinite programming model by alternating principal-subproblem optimization to obtain the energy and reserve scheduling results of the power system.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power system energy and reserve scheduling method according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the power system energy and reserve dispatch method according to any one of claims 1-7.

Images