An adaptive unit commitment optimization method and system based on extreme scenario driving

Abstract

The application discloses an adaptive unit combination optimization method and system based on extreme scenario driving, constructs an uncertainty set in robust optimization; determines an overall model of adaptive robust optimization unit combination according to the constructed uncertainty set; converts the overall model of adaptive robust optimization unit combination into a random programming model with vertices and extreme scenarios, adds a full-scenario feasibility constraint and an unexpected constraint into the random programming model, and realizes adaptive unit combination optimization based on extreme scenario driving. The method solves the defects that the conventional uncertainty set robust optimization and the random programming do not satisfy the unexpectedness and full-scenario feasibility, and the scheduling instruction obtained by the model can be used for high-proportion new energy power market unit combination optimization decision.

Description

Technical Field

[0001] This invention belongs to the field of unit combination technology in the current electricity market clearing process, specifically involving an adaptive unit combination optimization method and system based on extreme scenario driving. Background Technology

[0002] Renewable energy will become the primary energy source for the power system, participating in power generation and system dispatch. While the increased proportion of renewable energy promotes the low-carbon transformation of the power system, the uncertainty inherent in renewable energy will pose challenges to the safe operation of the power system. To address the uncertainties brought about by renewable energy, many studies have proposed two main approaches: robust optimization and stochastic programming, to solve the power system dispatch problem under a high proportion of renewable energy.

[0003] While these two types of methods have been widely applied in market scheduling problems, they cannot guarantee the unpredictability and feasibility of market scheduling instructions across all scenarios. Specifically,

[0004] (1) The second decision of the two-stage robust optimization is given by the known realization value of random factors, which violates the temporal logic of alternating decision-making in multiple time periods, and therefore does not satisfy the unexpectedness.

[0005] (2) Scenario-based methods only consider a limited number of scenarios, and therefore do not meet the feasibility of all scenarios;

[0006] (3) The scene tree method only guarantees the unpredictability of a limited number of scenes in the tree structure, and does not satisfy the feasibility of all scenes;

[0007] (4) The probability distribution of more accurate random factors used in the chance-constraint method does not satisfy the requirement of unpredictability. Unpredictability means that the decision made in a certain period can only rely on the realized part of the random factors and all possible future situations. Full scenario feasibility means that the decision we make can cope with all possible future situations. The logic of the robust optimization model is to assume that all uncertain factors have been realized before giving the scheduling decision, which violates the decision chain of "uncertainty realization - implementation decision - uncertainty realization"; while stochastic programming only considers the case of minimizing the expected objective function in a finite number of typical scenarios, and cannot cope with all future uncertain scenarios.

[0008] To ensure that the decision results meet the requirements of unpredictability and feasibility across all scenarios, a robust uncertainty set should be generated based on historical data. By traversing all extreme points of the uncertainty set, extreme scenarios can be found, thereby improving the traditional two-stage robust optimization method and enabling it to meet the requirements of unpredictability and feasibility across all scenarios. Summary of the Invention

[0009] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing an adaptive unit combination optimization method and system based on extreme scenarios. It focuses on establishing a scheduling model that satisfies both unpredictability and feasibility across all scenarios to cope with the uncertainty of high-proportion renewable energy power systems. This invention addresses the technical problem that conventional uncertainty set robust optimization and stochastic programming do not satisfy unpredictability and feasibility across all scenarios, i.e., they do not consider the unpredictability constraints of economic scheduling, and their infeasibility cannot be identified in the original recipes based on robust optimization and stochastic optimization.

[0010] The present invention adopts the following technical solution:

[0011] An adaptive unit combination optimization method driven by extreme scenarios includes the following steps:

[0012] S1. Construct the uncertainty set in robust optimization;

[0013] S2. Determine the overall model for adaptive robust optimization of unit combination based on the uncertainty set constructed in step S1;

[0014] S3. Transform the overall model of adaptive robust optimization unit combination obtained in step S2 into a stochastic programming model with vertices and extreme scenarios. Add full-scenario feasibility constraints and unexpected constraints to the stochastic programming model to achieve adaptive unit combination optimization driven by extreme scenarios.

[0015] Specifically, in step S1, the output uncertainty set of the renewable energy generator unit during time period t is u. t Represented as:

[0016]

[0017] Where, θ t,k The coefficients are convex combination coefficients, and θ t,k ≥0, NT represents the total number of time periods, u t,k This is the kth extreme point.

[0018] Specifically, in step S2, the objective function of the overall model for adaptive robust optimization of unit combination is:

[0019]

[0020] Where Θ(·) represents the sum of start-up and shutdown costs of the thermal power unit; Φ(·) represents the sum of operating costs of the thermal power unit; and u represents the uncertain output of the renewable energy unit. These are decision variables in the unit combination model; It represents the combined output of renewable energy units throughout all hours. These are decision variables for economic scheduling.

[0021] Furthermore, the constraints are as follows:

[0022] Unit start-up and shutdown costs:

[0023]

[0024] Operating costs are:

[0025]

[0026] Minimum start-stop time constraints:

[0027]

[0028]

[0029] Relationship of variables in unit combination decision:

[0030]

[0031]

[0032] in, Let g be the start / stop state variable of unit g during time period t. Let g be the state transition variable of unit g in time period t. Let g be the output of unit g during time period t. Let g be the operating cost of unit g during time period t. Let g be the nodal price for unit g in time period t. For the output of renewable energy unit g in time period t, For the demand of load d in time period t, For the minimum and maximum power flow constraints of line l, Let g be the minimum start-up and shutdown time of unit g.

[0033] Specifically, in step S3, the stochastic programming model with vertices and extreme scenarios is as follows:

[0034]

[0035]

[0036] Where Θ(·) represents the sum of start-up and shutdown costs of the thermal power unit, and Φ(·) represents the sum of operating costs of the thermal power unit. This represents the convex combination representation of the extreme points of uncertain output of renewable energy units. The Cartesian product represents the uncertain output of renewable energy units throughout the entire time period. To solve the feasible region of the operating cost problem of thermal power units, These are the decision variables in the unit combination model. Let NK be the number of generator units, and θ be the decision variable for economic dispatch. t,k These are the coefficients for convex combinations.

[0037] Specifically, in step S3, adding full-scenario feasibility constraints and unexpected constraints to the stochastic programming model involves:

[0038] S301. Classify the constraints;

[0039] S302. Divide the scenario into basic scenario, vertex scenario, and extreme scenario; determine the constraints obtained in step S301 and the corresponding scenarios, and then apply the decision variables of the basic scenario, vertex scenario, and extreme scenario to the two-stage economic dispatch decision variables of the corresponding constraints to determine the day-ahead economic dispatch model.

[0040] S303. Add full-scenario feasibility constraints and unforeseen constraints to the day-ahead economic scheduling model obtained in step S302. The scheduling decision should be able to cope with all possible outcomes of uncertain factors while satisfying the temporal logic.

[0041] Furthermore, in step S301, there are constraints that are unrelated to the uncertainty set:

[0042] Minimum start-up and shutdown time constraints, standby constraints, thermal power unit output constraints, start-up and shutdown state relationship constraints;

[0043] Single-cycle constraints related to the uncertainty set: power balance constraints, line transmission capacity constraints, which need to satisfy the NK scenario, and the number of constraints is NK * the original number of constraints;

[0044] Intertemporal constraints related to uncertainty sets:

[0045] The ramp-up rate constraint for thermal power plants must satisfy (NK). NT The scenario and the number of constraints are Number of original constraints.

[0046] Furthermore, in step S302, the day-ahead economic dispatch model is as follows:

[0047]

[0048]

[0049] in, and For the purposes of this discussion, V and E are the index sets for the aforementioned vertices and extreme scenarios, respectively. Let Ω represent the output of the g-th generator unit during the peak and extreme scenarios in time period t, where g is the generator unit number. TG A collection of thermal power units. t represents the extreme point number in the rearranged extreme point sequence, and t represents the time period number.

[0050] Furthermore, in step S303, the full-scenario feasibility constraints and unexpected constraints are as follows:

[0051]

[0052]

[0053] in, This represents the minimum output of the thermal power unit during the extreme scenario of ramping uphill. For the output of the g-th unit under peak and extreme scenarios in time period t, This represents the maximum output of the thermal power unit during the extreme scenario of ramping uphill, where g is the unit number. Let V be the set of extreme points, V be the set of vertices, and t be the number of extreme points. odd This is the period of maximum output.

[0054] Secondly, embodiments of the present invention provide an adaptive unit combination optimization system driven by extreme scenarios, comprising:

[0055] The data module constructs the uncertainty set in robust optimization;

[0056] The construction module determines the overall model for adaptive robust optimization of unit combination based on the uncertainty set constructed by the data module;

[0057] The optimization module transforms the overall model of adaptive robust optimization unit combination obtained from the construction module into a stochastic programming model with vertices and extreme scenarios. It adds full-scenario feasibility constraints and unpredictable constraints to the stochastic programming model to achieve adaptive unit combination optimization driven by extreme scenarios.

[0058] Compared with the prior art, the present invention has at least the following beneficial effects:

[0059] An adaptive unit combination optimization method driven by extreme scenarios is proposed. Based on the robust optimization of unit combination scheduling in a high-proportion renewable energy power market, extreme scenarios are constructed from the vertex scenarios of the robust uncertainty set, thereby transforming the uncertainty set into a stochastic programming of a finite number of extreme scenarios. Then, constraints on unpredictability and full scenario feasibility are added to the model. Finally, while simplifying the computational complexity, a decision result that satisfies both unpredictability and full scenario feasibility is obtained.

[0060] Furthermore, the uncertainty set in traditional robust optimization is used for subsequent extraction and improvement of extreme scenarios in the original uncertainty set, as well as a necessary condition for evaluating the effectiveness of this invention.

[0061] Furthermore, the objective function is set as the extreme total cost from the start to the end of the daily dispatch phase. By introducing a set of uncertainties in the spatial coupling of new energy sources, the worst-case scenario of cost over all future time periods resulting from the decision is measured. The solution for the day-ahead dispatch instruction fully considers the fluctuations of new energy units and makes forward-looking adjustments based on the start-up and shutdown costs of thermal power plants, which contributes to the safe operation of the entire system and the absorption of renewable energy.

[0062] Furthermore, by setting constraints on the start-up and shutdown costs and start-up / shutdown times of thermal power units, they can operate under conditions that minimize damage to the units and are more economical, avoiding repeated start-up and shutdown operations and effectively ensuring their service life. Considering the timing relationship of start-up and shutdown, the aforementioned start-up and shutdown states of the units are further constrained by setting timing logic constraints on start-up and shutdown variables.

[0063] Furthermore, the stochastic programming model with vertex and extreme scenarios has two advantages: first, scenario generation depends on the set of uncertainties, and the selected scenarios are highly typical, covering extreme net load ramp-up scenarios and normal fluctuation output scenarios, so that the actual operating effect is quite close to the theoretical calculation value; second, the number of scenarios is small, the amount of computation is moderate, no additional scenario reduction method is required, and the solution speed meets the actual requirements of day-ahead unit combination.

[0064] Furthermore, extreme scenarios are selected at the extreme points of the polyhedral uncertainty set. The convex combination coefficients within the scheduling window are iterated, and when they are 1, they are selected. Based on this approach, the uncertainty set unit combination problem is transformed into a stochastic optimization problem with a finite number of extreme scenario sets. The computational complexity is simplified from the conventional infinite number of uncertainty sets in the robust unit combination optimization model to a finite number of extreme scenario sets, resulting in higher computational efficiency. The three types of scenarios are simplified: the basic scenario can be directly used to accelerate the solution; while the vertex scenario requires reconstruction. For each unit in each period t, all extreme point elements are summed, and the vertex scenarios are sorted in ascending order based on the summation results. From the reconstructed vertex scenarios, extreme scenarios are selected, with the scenario showing the largest fluctuation in unit output between two adjacent time periods defined as the extreme scenario, further subdivided into two types of extreme scenarios.

[0065] Furthermore, the original constraints are divided into single-time constraints and cross-time constraints. To reduce computational burden, the scenario set is divided into basic scenarios, vertex scenarios, and extreme scenarios. To ensure feasibility across all scenarios, single-time constraints related to the uncertainty set must consider all scenarios, while cross-time constraints must consider the extreme scenario ramp-up constraint verification.

[0066] Furthermore, the day-ahead economic dispatch model solves for the optimal power adjustment of generator units under the current unit combination command in extreme and peak scenarios, and influences the unit combination command through the worst-case economic dispatch operating cost objective function. This allows the day-ahead unit combination command to be adjusted according to the fluctuation of new energy sources and extreme ramping scenarios, making it more forward-looking than the static deterministic day-ahead economic dispatch model.

[0067] Furthermore, the unexpected constraint ensures that the unit combination plan generated by the day-ahead-real-time two-stage scheduling framework conforms to the logic of decision-making under uncertain scenarios, avoiding unpredictable scheduling instructions. This ensures that decisions rely solely on the actual output of current renewable energy sources, guaranteeing that unit combination instructions conform to the actual scheduling process. The full-scenario feasibility constraint verifies the feasibility of decisions under extreme ramping events, enabling scheduling decisions to address all future renewable energy fluctuations rather than being limited to a few typical scenarios, thus possessing practical application value.

[0068] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0069] In summary, at the analytical method level, this invention generates vertex and extreme scenarios by searching for the vertices of the box-shaped uncertainty set and the spatially coupled uncertainty set of new energy power output, which can more accurately reflect the multimodal characteristics of the power output prediction error of new energy power plants; at the real-time scheduling level, it generates the worst-case operating cost situation for the traversed scenarios and adjusts the unit combination plan for the first stage, so that the scheduling instructions can cope with all possible future uncertainties and fluctuations in new energy; at the day-ahead scheduling level, it utilizes the unexpectedness and feasibility of all scenarios to obtain the optimal solution for the scheduling plan, ensuring that the scheduling plan preparation process can guide the actual system operation.

[0070] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0071] Figure 1 The node system network diagram used to demonstrate the effectiveness of the extreme scenario driving model in this invention;

[0072] Figure 2 This is a typical output curve of a renewable energy unit in a node system.

[0073] Figure 3 A typical vertex scene curve used to construct extreme scenarios;

[0074] Figure 4 This is a diagram showing the unit combination decision results obtained using a typical vertex scenario set;

[0075] Figure 5This is a diagram showing the unit combination decision results obtained using the uncertainty set. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0078] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0079] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" relationship.

[0080] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0081] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0082] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0083] This invention provides an adaptive unit combination optimization method driven by extreme scenarios. First, extreme scenarios are generated using the vertices of the uncertainty set. Then, the two-stage robust optimization is transformed into a stochastic programming problem under extreme scenarios. The model considers cross-time-period ramping constraints and unexpected constraints under extreme ramping scenarios. Finally, a case study is conducted on an improved IEEE 30-node high-proportion renewable energy system to verify the effectiveness of the invention. In addressing the uncertainty of power supply in power systems with high renewable energy penetration, this invention considers the unexpectedness of the decision-making process and the feasibility of all scenarios in unit combination scheduling instructions, ensuring that the scheduling instructions do not violate temporal logic and are truly feasible.

[0084] This invention provides an adaptive unit combination optimization method based on extreme scenario driving, comprising the following steps:

[0085] S1. Construct the uncertainty set in robust optimization;

[0086] Acquire basic system data, such as the technical parameters of various types of power sources in a high-proportion renewable energy power system, transmission grid and network parameters, load demand, and historical power generation of renewable energy (if there are no actual system parameters, such as the verification example used in this paper, reasonable parameter values ​​can be set for example verification). Then, construct a robust optimization uncertainty set and conduct scenario analysis based on the system parameters. Finally, solve the overall system model to obtain the final unit combination result.

[0087] In two-stage robust optimization, the set of uncertainties is typically modeled in the form of a polyhedron. Each polyhedron has a set of extreme points.

[0088] set up Let C be a polyhedron, and let z be an extreme point of C, indicating that there are no x, y ∈ C and 0 < λ < 1 such that z = (1-λ)x + λy. Intuitively, for a polyhedron, the extreme point of the polyhedron is its vertex, and every point in the polyhedron can be represented by a linear combination of extreme points.

[0089] Therefore, the uncertainty set of robust optimization is replaced by the set of extreme scenarios consisting only of extreme points; let u be the output uncertainty set of the renewable generator unit in time period t. t Then it is represented as:

[0090]

[0091] Among them, Ω NE P is a collection of regeneration units; t NE A vector consisting of the output power of renewable energy units; and The upper and lower limits of the unit's output and the expected output; Ψ t This is a budget constraint parameter, taking values ​​between 0 and Ω. NE The cardinality between Ψ is used to control the size of the uncertainty set; t The larger the value of , the greater the output deviation of the renewable generator set, and the more conservative the result of robust optimization.

[0092] The analysis of the uncertainty set scenario is as follows:

[0093] Due to u t It is represented as a linear combination of a series of extreme points, and each polyhedron uncertainty set has Let there be extreme points, and let... For u t The set of extreme points, u t,k If it is the k-th extreme point, then we have

[0094]

[0095]

[0096]

[0097] Where, θ t,k The coefficients are convex combination coefficients, and θ t,k ≥0, NT represents the total number of time periods.

[0098] S2. Determine the overall model for adaptive robust optimization of unit combination;

[0099] Its objective function is given by equation (5):

[0100]

[0101] The start-up and shutdown cost formula (6) of the unit:

[0102]

[0103] The operating cost is given by equations (7) to (8):

[0104]

[0105]

[0106] The constraints include node power balance constraints (9):

[0107]

[0108] Line transmission capacity constraint (10):

[0109]

[0110] Unit standby capacity constraints (11)~(12):

[0111]

[0112]

[0113] Unit output constraints (13)~(14):

[0114]

[0115]

[0116] Unit ramping constraints (15)~(16):

[0117]

[0118]

[0119] Minimum start-stop time constraints (17)~(18):

[0120]

[0121]

[0122] Relationships of unit combination decision variables (19)~(20):

[0123]

[0124]

[0125] Where Θ(·) represents the sum of start-up and shutdown costs of the thermal power unit; Φ(·) represents the sum of operating costs of the thermal power unit; These are the decision variables in the unit combination model; Let g be the start / stop state variable of unit g during time period t; Let g be the state transition variable of unit g in time period t; These are decision variables for economic scheduling; The output of unit g during time period t; Let g be the operating cost of unit g during time period t; Let g be the nodal price for unit g during time period t; The output of the renewable energy unit g during time period t; The demand for load d during time period t; G represents the minimum and maximum power flow constraints for line l; li The power transfer distribution factor between line l and node i; Constraints on the upward / downward reserve capacity that unit g can provide during time period t; Reserve demand for upward / downward movement in time period t; For the upward / downward ramp constraint of unit g; Let g be the minimum start-up and shutdown time of unit g.

[0126] Since the feasible region of the subproblem in the above objective function (5) is determined by the constraints (8)-(16), the above robust optimization model can be expressed as:

[0127]

[0128] Since the extreme points of the uncertainty set represent the extreme cases constrained by the uncertainty polyhedron, it is not necessary to find the optimal solution over the entire uncertainty polyhedron. The above robust optimization unit combination problem can be solved by finding the optimal solution in the worst case at the extreme points of the uncertainty set.

[0129] Therefore, among the convex combination coefficients of any period, only the worst-case combination coefficient in equation (3) is 1, and the others are 0; based on this discovery, the above robust optimization unit combination problem is transformed into a planning problem of a finite number of extreme value scenarios.

[0130] Specifically:

[0131]

[0132]

[0133] Note that due to the selection and determination of the convex combination coefficients, the set of uncertainties at intermediate levels becomes the set of extreme points of the original set. Since the set of uncertainties in the original robust optimization problem is transformed into a finite number of extreme cases, the original robust optimization problem is transformed into a stochastic programming problem.

[0134] S3. Add constraints to ensure that the decision results obtained from the robust optimization model in step S2 meet the requirements of unpredictability and feasibility across all scenarios.

[0135] S301. Classify the constraints (9) to (20);

[0136] Constraints unrelated to the uncertainty set include minimum start-up and shutdown time constraints (17) to (18), standby constraints (11) to (12), thermal power unit output constraints (13) to (14), start-up and shutdown state relationship constraints (19) to (20), which are unrelated to the number of extreme scenarios.

[0137] Single-cycle constraints related to the uncertainty set: power balance constraint (9), line transmission capacity constraint (10), which need to satisfy the NK scenario, and the number of constraints is NK * the original number of constraints.

[0138] Intertemporal constraints related to the uncertainty set: thermal power plant ramp-up rate constraints (15) to (16), which need to satisfy (NK). NT The scenario and the number of constraints are Number of original constraints.

[0139] S302. To reduce computational complexity, the scenarios are further classified, thereby reducing the number of scenarios with third-class constraints.

[0140] Basic scenario: A scenario can be selected and designed from the predicted or historical output of renewable energy;

[0141] Vertex scenario: Let the set of extreme points of the uncertainty set in time period t be... For each The elements in the array are summed, and the original array is then converted to the desired value based on the summation result. The sequence is reordered, and the index changes from the original index k to... Therefore, for each original After rearrangement, we get:

[0142]

[0143] Process the set of extreme points for each period in ascending order, and take all vertices in the same order as a vertex scene. Then the set of vertex scenes is represented as follows:

[0144]

[0145] The corresponding convex combination coefficients are

[0146] Extreme scenarios: Used to verify the climbing rate constraint, including two extreme scenarios:

[0147] The first scenario is that the output is minimum at time t-1 and maximum at time t;

[0148] The second scenario is that the output is greatest at time t-1 and least at time t.

[0149] Set extreme scenarios The reordered vertex scene described above represents these two extreme scenarios as follows:

[0150]

[0151]

[0152] The scenario decision variables categorized above are applied to the corresponding two-stage economic scheduling decision variables. Let V and E be the index sets of the vertex and extreme scenarios, respectively. Then, the day-ahead economic scheduling model needs to be rewritten as follows:

[0153]

[0154]

[0155] Furthermore, the constraints in (7) to (14) use Substitution, constraints (9) to (10) use Substitution, constraints (15) to (16) use Replacement.

[0156] S303, Add unexpected constraints.

[0157]

[0158]

[0159] In another embodiment of the present invention, an adaptive unit combination optimization system based on extreme scenarios is provided. This system can be used to implement the above-mentioned adaptive unit combination optimization method based on extreme scenarios. Specifically, the adaptive unit combination optimization system based on extreme scenarios includes a data module, a construction module, and an optimization module.

[0160] The data module is used to construct the uncertainty set in robust optimization.

[0161] The construction module determines the overall model for adaptive robust optimization of unit combination based on the uncertainty set constructed by the data module;

[0162] The optimization module transforms the overall model of adaptive robust optimization unit combination obtained from the construction module into a stochastic programming model with vertices and extreme scenarios. It adds full-scenario feasibility constraints and unpredictable constraints to the stochastic programming model to achieve adaptive unit combination optimization driven by extreme scenarios.

[0163] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of an adaptive unit combination optimization method driven by extreme scenarios, including:

[0164] Construct an uncertainty set for robust optimization; determine the overall model for adaptive robust optimization unit combination based on the constructed uncertainty set; transform the overall model for adaptive robust optimization unit combination into a stochastic programming model with vertices and extreme scenarios, and add full-scenario feasibility constraints and unexpected constraints to the stochastic programming model to achieve adaptive unit combination optimization driven by extreme scenarios.

[0165] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0166] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the adaptive unit combination optimization method based on extreme scenario driving in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0167] Construct an uncertainty set for robust optimization; determine the overall model for adaptive robust optimization unit combination based on the constructed uncertainty set; transform the overall model for adaptive robust optimization unit combination into a stochastic programming model with vertices and extreme scenarios, and add full-scenario feasibility constraints and unexpected constraints to the stochastic programming model to achieve adaptive unit combination optimization driven by extreme scenarios.

[0168] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0169] Please see Figure 1 To verify the effectiveness of this invention, an IEEE 30-node system was selected for calculation and analysis of the unit combination. This system includes 6 thermal power units (G1-G6), 5 wind power units (G7-G11), and 3 photovoltaic units (G12-G14), with renewable energy accounting for 43% of the installed capacity. The system comprises 1 to 30 nodes, and the connection relationships of the components are as follows: Figure 1 As shown in the figure, the parameters of various power supply units in the system are shown in Table 1 and Table 2.

[0170] Step 1: Scene Analysis

[0171] The system has a total of 5+3 renewable energy units, therefore the uncertainty set in each time period will have 256 vertices. The extreme points of each time period are summed element-wise, and the extreme points are sorted in ascending order according to the summation results, thus obtaining the top vertex scene, the bottom vertex scene, and the representative vertex scene, as shown below. Figure 3 As shown.

[0172] Step 2: Unit Combination Result Analysis

[0173] The results of robust optimization of unit combination considering the uncertainty of renewable energy were compared with the results of deterministic unit combination under a representative peak scenario.

[0174] Table 1* Operating parameters of thermal power units

[0175]

[0176]

[0177] Table 2* Operating parameters of renewable energy units

[0178]

[0179] Please refer to Figure 2 , where is the typical output curve of wind power and photovoltaic units in the system, and is the installed capacity of renewable energy units in the system. The proportion of renewable energy in the system can be calculated. For such a high proportion of new energy power generation system, its uncertainty and unpredictability are quite large. Therefore, whether the scheduling decision is feasible in all scenarios and whether the unpredictability constraint is met is a major challenge for optimizing scheduling.

[0180] Please refer to Figure 4 and Figure 5 In a deterministic scenario representing the peak, units G1-G3, with their lower generation costs, need to be started first to meet load requirements. Thermal power unit G2 primarily serves as a marginal unit to handle load fluctuations, with its output varying with net load. Units G5 and G6 are briefly started to meet peak load demands. In a deterministic scenario, only about three units of G1-G4 need to be started, which is generally sufficient to meet grid load fluctuations.

[0181] The situation is different in the results of the robust unit combination optimization for uncertainty. Due to the severe fluctuations in net load under extreme conditions, new units need to be added to enhance ramp-up capabilities. Therefore, all thermal power units are almost always operational. Furthermore, as shown in the figure, under extreme scenarios, the outputs of thermal power units G5 and G6 vary with the overall grid net load. After all thermal power units are started, the system is able to handle the most extreme cases. The improved robust unit combination results can meet the feasibility and unpredictability requirements of all scenarios.

[0182] This invention utilizes an IEEE 30 example for verification calculations and compares the decision results of traditional robust optimization of uncertain sets with those calculated by the algorithm of this invention, verifying the successful achievement of the research objectives. The results show that the decision results obtained by this model satisfy the requirements of unpredictability and feasibility across all scenarios. Starting multiple thermal power units improves the system's ramp-up capability, and dispatching commands achieve a "wind-driven" effect.

[0183] In summary, this invention presents an adaptive unit combination optimization method and system based on extreme scenarios. By transforming an infinite set of robust uncertainties into a finite set of vertex scenarios and adding cross-time-period climbing constraint verification to the extreme climbing scenario set, it solves the defects of conventional uncertain set robust optimization and stochastic programming in not satisfying unpredictability and full scenario feasibility. The scheduling instructions obtained by the proposed model can be used for unit combination optimization decision-making in a high-proportion renewable energy power market, and have been verified through numerical examples.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0185] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0187] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or 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 through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0188] 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.

[0189] 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.

[0190] If the integrated module / 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0191] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0194] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. An adaptive unit combination optimization method based on extreme scenario driving, characterized in that, Includes the following steps: S1. Construct the uncertainty set in robust optimization; S2. Based on the uncertainty set constructed in step S1, determine the overall model of the adaptive robust optimization unit combination. The objective function of the overall model of the adaptive robust optimization unit combination is: in, This is the sum of the start-up and shutdown costs of the thermal power unit; The sum of the operating costs of the thermal power units. To address the uncertainties of renewable energy units; These are decision variables in the unit combination model; It represents the combined output of renewable energy units throughout all hours. Let be the decision variables for economic scheduling; the constraints are as follows: Unit start-up and shutdown costs: Operating costs are: Minimum start-stop time constraints: Relationship of variables in unit combination decision: in, For the unit During the period Start-stop state variables, For the unit During the period State transition variables, For the unit During the period of efforts, For the unit During the period Operating costs For the unit Minimum start-up and shutdown time, The total number of time periods. A collection of thermal power units; S3. The overall model of adaptive robust optimization unit combination obtained in step S2 is transformed into a stochastic programming model with vertices and extreme scenarios. Full-scenario feasibility constraints and unexpected constraints are added to the stochastic programming model to realize adaptive unit combination optimization based on extreme scenarios. The stochastic programming model with vertices and extreme scenarios is as follows: in, This is the sum of the start-up and shutdown costs of the thermal power unit. The sum of the operating costs of the thermal power units. This represents the convex combination representation of the extreme points of uncertain output of renewable energy units. The Cartesian product represents the uncertain output of renewable energy units throughout the entire time period. To solve the feasible region of the operating cost problem of thermal power units, These are the decision variables in the unit combination model. As a decision variable for economic scheduling, The number of generator sets. These are the coefficients for convex combinations.

2. The adaptive unit combination optimization method based on extreme scenario driving according to claim 1, characterized in that, In step S1, The output uncertainty set of the time period renewable generator unit is Represented as: in, The coefficients are convex combination coefficients, and ; For the first There are several extreme points.

3. The adaptive unit combination optimization method based on extreme scenario driving according to claim 1, characterized in that, In step S3, adding full-scenario feasibility constraints and unexpected constraints to the stochastic programming model specifically involves: S301. Classify the constraints; S302. Divide the scenario into basic scenario, vertex scenario, and extreme scenario; determine the constraints obtained in step S301 and the corresponding scenarios, and then apply the decision variables of the basic scenario, vertex scenario, and extreme scenario to the two-stage economic dispatch decision variables of the corresponding constraints to determine the day-ahead economic dispatch model. S303. Add full-scenario feasibility constraints and unforeseen constraints to the day-ahead economic scheduling model obtained in step S302. The scheduling decision should be able to cope with all possible outcomes of uncertain factors while satisfying the temporal logic.

4. The adaptive unit combination optimization method based on extreme scenario driving according to claim 3, characterized in that, In step S301, constraints unrelated to the uncertainty set include: Minimum start-up and shutdown time constraints, standby constraints, thermal power unit output constraints, start-up and shutdown state relationship constraints; Single-cycle constraints related to uncertainty sets: power balance constraints, line transmission capacity constraints, which need to be satisfied. The scenario and the number of constraints are *Number of original constraints; Intertemporal constraints related to uncertainty sets: The ramp rate constraint for thermal power plants needs to meet the following requirements. The scenario and the number of constraints are *Number of original constraints It is a collection of regeneration units.

5. The adaptive unit combination optimization method based on extreme scenario driving according to claim 3, characterized in that, In step S302, the day-ahead economic dispatch model is as follows: in, and These are the index sets for the aforementioned vertices and extreme scenarios, respectively. For the first Taiwanese unit in Output at peak times and in extreme scenarios For the unit number, A collection of thermal power units. The extreme points are numbered in the rearranged order of extreme points. This is a time period number.

6. The adaptive unit combination optimization method based on extreme scenario driving according to claim 3, characterized in that, In step S303, the full-scenario feasibility constraints and unexpected constraints are as follows: in, This represents the minimum output of the thermal power unit during the extreme scenario of ramping uphill. For the first Taiwanese unit in Output at peak times and in extreme scenarios This refers to the maximum output of the thermal power unit during the extreme scenario of ramping uphill. Numbering of thermal power units Number the extreme points. For vertex set, This is the period of maximum output.

7. An adaptive unit combination optimization system driven by extreme scenarios, characterized in that, include: The data module constructs the uncertainty set in robust optimization; The construction module determines the overall model of adaptive robust optimization unit combination based on the uncertainty set constructed by the data module. The objective function of the overall model of adaptive robust optimization unit combination is: in, This is the sum of the start-up and shutdown costs of the thermal power unit; The sum of the operating costs of the thermal power units. To address the uncertainties of renewable energy units; These are decision variables in the unit combination model; It represents the combined output of renewable energy units throughout all hours. Let be the decision variables for economic scheduling; the constraints are as follows: Unit start-up and shutdown costs: Operating costs are: Minimum start-stop time constraints: Relationship of variables in unit combination decision: in, For the unit During the period Start-stop state variables, For the unit During the period State transition variables, For the unit During the period of efforts, For the unit During the period Operating costs For the unit Minimum start-up and shutdown time, The total number of time periods. A collection of thermal power units; The optimization module transforms the overall model of adaptive robust unit combination optimization obtained from the construction module into a stochastic programming model with vertices and extreme scenarios. It adds full-scenario feasibility constraints and unpredictable constraints to the stochastic programming model, achieving adaptive unit combination optimization driven by extreme scenarios. The stochastic programming model with vertices and extreme scenarios is specifically as follows: in, This is the sum of the start-up and shutdown costs of the thermal power unit. The sum of the operating costs of the thermal power units. This represents the convex combination representation of the extreme points of uncertain output of renewable energy units. The Cartesian product represents the uncertain output of renewable energy units throughout the entire time period. To solve the feasible region of the operating cost problem of thermal power units, These are the decision variables in the unit combination model. As a decision variable for economic scheduling, The number of generator sets. These are the coefficients for convex combinations.

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