Power flow out-of-limit elimination method and device based on section sensitivity and source load characteristics, terminal equipment and storage medium

Abstract

The invention discloses a power flow out-of-limit elimination method and device based on section sensitivity and source load characteristics, terminal equipment and a storage medium, and belongs to the technical field of power flow out-of-limit elimination. The method comprises the following steps: acquiring unit output data, bus load data, section sensitivity data and section power flow data of the power dispatching system in a preset full time period; dividing the section into a new energy output section, a load center feed-in section and a network loop section according to section sensitivity data, and respectively constructing a new energy output prediction adjustment optimization model, a bus load prediction adjustment optimization model, a non-new energy output adjustment optimization model and corresponding constraint conditions; and solving to obtain an optimal new energy output predicted value, a bus load predicted value and a non-new energy output value, and performing out-of-limit adjustment. By implementing the method and the device, the problem of low out-of-limit elimination efficiency caused by huge calculated amount when the scheduling system is subjected to overall modeling solution in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tide flow over-limit elimination, and particularly relates to a tide flow over-limit elimination method and device based on section sensitivity and source and load characteristics, a terminal equipment and a storage medium. BACKGROUND

[0002] In the power market pre-clearing, when the current power trading mechanism evolves into the spot market, the modeling dependence of dispatch system operation on security constrained unit commitment (SCUC) continues to increase. This change is not only the inevitable requirement of the core characteristics of the spot market "daily clearing and monthly settlement, real-time clearing", but also the key support to balance efficiency and safety in the process of power system from planned dispatch to market-driven dispatch.

[0003] In the prior art, the dispatch system is usually directly modeled as a whole, and the model essentially belongs to the category of high-dimensional mixed integer nonlinear programming (MINLP). In the dispatch process of this scenario, all sections are modeled globally without distinction, and the over-limit is corrected globally, resulting in exponential growth of the calculation amount. Therefore, the prior art has the problem of large calculation amount and low over-limit elimination efficiency caused by whole modeling. SUMMARY

[0004] The present application provides a tide flow over-limit elimination method and device based on section sensitivity and source and load characteristics, a terminal equipment and a storage medium, which can solve the problem of low over-limit elimination efficiency caused by large calculation amount in the prior art of whole modeling of the dispatch system for tide flow over-limit elimination.

[0005] An embodiment of the present application provides a tide flow over-limit elimination method based on section sensitivity and source and load characteristics, comprising:

[0006] Obtaining new energy unit output data, non-new energy unit output data, bus load data, section sensitivity data and section flow data of the power dispatch system in a preset full time period;

[0007] Among all the above-mentioned section sensitivity data, the section whose nodes all belong to new energy unit nodes is taken as a new energy sending-out section; the section whose nodes all belong to bus load nodes is taken as a load center feeding-in section; and the section whose node types are not less than a preset type threshold is taken as a network loop section;

[0008] According to the new energy unit output data corresponding to the new energy sending section, the section sensitivity data and the section power flow data, a new energy output prediction adjustment optimization model with the minimum new energy output adjustment as the target and a first constraint condition are constructed; according to the bus load data corresponding to the load center feeding section, the section sensitivity data and the section power flow data, a bus load prediction adjustment optimization model with the minimum bus load initial value adjustment as the target and a second constraint condition are constructed; according to the non-new energy unit output data corresponding to the network loop section, the section sensitivity data and the section power flow data, a non-new energy output adjustment optimization model with the minimum non-new energy unit adjustment as the target and a third constraint condition are constructed;

[0009] The new energy output prediction adjustment optimization model, the bus load prediction adjustment optimization model and the non-new energy output adjustment optimization model are solved under the first constraint condition, the second constraint condition and the third constraint condition respectively, and the optimal new energy output prediction value, the optimal bus load prediction value and the optimal non-new energy output value are obtained, and the corresponding unit and bus load are adjusted out of limit.

[0010] Further, the objective function of the new energy output prediction adjustment optimization model is:

[0011]

[0012] In the formula, F new represents the value of the objective function of the new energy output prediction adjustment optimization model, M N represents the penalty factor of the adjustment new energy unit output weight corresponding to the new energy output prediction adjustment optimization model, T represents the time set of the preset whole period, G n represents the new energy unit set, represents the new energy output prediction value of the new energy unit n corresponding to the new energy sending section, the upper adjustment amount at time t, represents the new energy output prediction value of the new energy unit n corresponding to the new energy sending section, the lower adjustment amount at time t, M S represents the penalty factor of the adjustment section out-of-limit relaxation amount weight, K represents all section sets corresponding to the new energy sending section, represents the upward relaxation amount of the section k in the new energy sending section at time t, represents the downward relaxation amount of the section k in the new energy sending section at time t.

[0013] Further, the new energy unit output data includes the adjustment amount of the new energy output prediction value, the new energy unit climbing interval value and the new energy unit output initial value; the first constraint condition includes the new energy unit output climbing constraint, the first power flow section non-out-of-limit constraint and the first unit adjustment amount constraint;

[0014] The construction of the first constraint mentioned above includes:

[0015] Based on the adjustment of the ramping range value of the new energy unit corresponding to the new energy transmission section, the initial value of the new energy unit output, and the predicted value of the new energy output, the ramping constraint of the new energy unit output is constructed.

[0016] Based on the power flow data and sensitivity data of the corresponding new energy transmission sections, the first power flow section non-over-limit constraint is constructed.

[0017] Based on the adjustment amount of the above-mentioned new energy output forecast, the adjustment amount constraint of the first unit is constructed.

[0018] Furthermore, the objective function of the above bus load forecasting and adjustment optimization model is:

[0019]

[0020] In the formula, F L M represents the value of the objective function of the bus load forecasting and adjustment optimization model. L L represents the penalty factor used to adjust the weights of the bus load forecast values ​​in the bus load forecasting and adjustment optimization model. This represents the upward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the downward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the upward relaxation of section k in the load center feed section at time t. This represents the downward relaxation of section k in the load center feed section at time t.

[0021] Furthermore, the aforementioned bus load data includes: the initial value of the bus load and the adjustment amount of the predicted value of the bus load; the aforementioned second constraint includes: the bus load prediction adjustment amount constraint, the second power flow section non-overrun constraint, and the bus load adjustment amount constraint.

[0022] The construction of the second constraint mentioned above includes:

[0023] Based on the adjustment amount of the bus load prediction value corresponding to the load center feed section and the initial value of the bus load, the above-mentioned bus load prediction adjustment amount constraint is constructed.

[0024] Based on the power flow data and sensitivity data of the section corresponding to the load center feed section, the above-mentioned second power flow section non-over-limit constraint is constructed.

[0025] Based on the adjustment amount of the above-mentioned bus load forecast value, the above-mentioned bus load adjustment amount constraint is constructed.

[0026] Furthermore, the objective function of the aforementioned non-new energy power output adjustment and optimization model is:

[0027]

[0028] In the formula, F uint M represents the value of the objective function of the non-new energy power output adjustment and optimization model. G G represents the penalty factor used to adjust the weight of non-renewable energy units in the non-renewable energy output adjustment and optimization model. f This represents a collection of non-new energy generating units. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the upward relaxation of section k corresponding to the network loop section at time t. This represents the downward relaxation of section k corresponding to the network loop section at time t.

[0029] Furthermore, the aforementioned third constraint includes: the output ramp-up constraint of non-new energy units, the constraint of not exceeding the limit at the third power flow section, and the adjustment amount constraint of the second unit.

[0030] The construction of the third constraint mentioned above includes:

[0031] Based on the power output data of the non-new energy units corresponding to the above network loop sections, the power output ramping constraints of the above non-new energy units are constructed.

[0032] Based on the power flow data and sensitivity data of the corresponding sections of the network loop sections, the above-mentioned third power flow section non-over-limit constraint is constructed.

[0033] Based on the adjustment amount of the non-new energy power output value in the non-new energy unit output data corresponding to the above network loop section, the above-mentioned second unit adjustment amount constraint is constructed.

[0034] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments;

[0035] This invention provides a power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics, comprising:

[0036] Data acquisition module, cross-section division module, model building module, and model solving module;

[0037] The aforementioned data acquisition module is used to acquire power dispatch system output data of new energy units, output data of non-new energy units, bus load data, section sensitivity data, and section power flow data under preset all time periods;

[0038] The aforementioned section division module is used to classify sections in which all nodes in the above section sensitivity data belong to new energy unit nodes as new energy transmission sections; sections in which all nodes belong to bus load nodes as load center feed-in sections; and sections in which the types of nodes are not less than a preset type threshold as network loop sections.

[0039] The aforementioned model building module is used to construct a new energy output prediction and adjustment optimization model with the goal of minimizing new energy output adjustment, based on the new energy unit output data, section sensitivity data, and section power flow data corresponding to the aforementioned new energy transmission section; to construct a bus load prediction and adjustment optimization model with the goal of minimizing the initial value adjustment of the bus load, based on the bus load data, section sensitivity data, and section power flow data corresponding to the aforementioned load center feed-in section; and to construct a non-new energy output adjustment optimization model with the goal of minimizing the adjustment of non-new energy units, based on the non-new energy unit output data, section sensitivity data, and section power flow data corresponding to the aforementioned network loop section.

[0040] The aforementioned model solving module is used to solve the aforementioned new energy power output prediction and adjustment optimization model, bus load prediction and adjustment optimization model, and non-new energy power output adjustment optimization model under the aforementioned first constraint condition, second constraint condition, and third constraint condition, respectively, to obtain the optimal new energy power output prediction value, the optimal bus load prediction value, and the optimal non-new energy power output value, and to perform over-limit regulation on the corresponding unit and bus load.

[0041] Based on the above method embodiments, the present invention provides a corresponding terminal device embodiment;

[0042] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics described in any embodiment of the present invention.

[0043] Based on the above method embodiments, the present invention provides a corresponding storage medium embodiment;

[0044] The present invention provides a storage medium including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics described in any embodiment of the present invention.

[0045] The embodiments of the present invention have the following beneficial effects:

[0046] This invention provides a method, apparatus, terminal equipment, and storage medium for power flow over-limit elimination based on cross-sectional sensitivity and source load characteristics. The method includes: acquiring power dispatching system data on output of new energy units, output of non-new energy units, bus load data, cross-sectional sensitivity data, and cross-sectional power flow data under a preset all-time period; subsequently, cross-sections in all the above-mentioned cross-sectional sensitivity data whose nodes belong to new energy unit nodes are designated as new energy transmission cross-sections; cross-sections whose nodes belong to bus load nodes are designated as load center feed-in cross-sections; and the types of nodes are not less than a preset... The cross-sections with threshold values ​​for different types are designated as network loop cross-sections. Based on the aforementioned data, optimization models are constructed for the new energy output prediction and adjustment of the new energy transmission cross-section, along with the first constraint condition; for the bus load prediction and adjustment of the load center input cross-section, along with the second constraint condition; and for the non-new energy output adjustment and optimization model of the network loop cross-section, along with the third constraint condition. These models are solved to obtain the optimal new energy output prediction value, the optimal bus load prediction value, and the optimal non-new energy output value. Limit-breaking adjustments are then made to the corresponding generating units and bus loads. Therefore, in this invention, before model construction, each cross-section is first classified. Within each category, the relevant data corresponding to that category's cross-section is used for model construction, eliminating the need to construct a global model. Furthermore, during the solution process, only each model is solved individually, thus significantly reducing computational load and improving limit-breaking efficiency. Attached Figure Description

[0047] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics, provided in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of a power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics, provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0052] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0053] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0054] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0055] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0056] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0057] See Figure 1 To address the problem that existing technologies involving overall modeling and solving of the scheduling system for power flow violation elimination suffer from low efficiency due to massive computational demands, this invention provides a power flow violation elimination method based on cross-sectional sensitivity and source load characteristics, comprising:

[0058] Step S101: Obtain the power output data of new energy units, the power output data of non-new energy units, the bus load data, the section sensitivity data, and the section power flow data of the power dispatch system under the preset all-time period;

[0059] Specifically, within the preset time period, the energy management system and market clearing system of the power disinfection institution acquire the output data of new energy units, output data of non-new energy units, bus load data, section sensitivity data and section power flow data for each sub-period.

[0060] Specifically, the output data of new energy units includes the upper and lower boundaries of the power generation range of new energy units, the ramp-up range value of new energy units (i.e., the upper and lower boundaries of the ramp-up range of new energy units), the adjustment amount of the predicted value of new energy processing, and the initial value of the output of new energy units, which is the value after the pre-clearance of the units.

[0061] Specifically, the output data of non-new energy units includes the upper and lower boundaries of the power generation range of non-new energy units, the adjustment amount of the output value of non-new energy units, the upper and lower boundaries of the ramping range of non-new energy units, and the initial output value of non-new energy units, which is the value after the pre-clearance of the units.

[0062] Specifically, the bus load data includes the adjustment amount of the bus load forecast value and the initial value of the bus load, which is also used as the bus load forecast value.

[0063] Specifically, the cross-sectional sensitivity data represents the degree of influence of each node on each cross-section after pre-clearing, and the cross-sectional power flow data includes the upper and lower limits of the power flow cross-section, the cross-sectional power flow safety margin, and the initial values ​​of each cross-section before the optimization and elimination of cross-section over-limits after pre-clearing.

[0064] Step S102: Among all the above-mentioned cross-sectional sensitivity data, the cross-sections whose nodes all belong to new energy unit nodes are designated as new energy transmission cross-sections; the cross-sections whose nodes all belong to bus load nodes are designated as load center feed-in cross-sections; and the cross-sections whose node types are not less than the preset type threshold are designated as network loop cross-sections.

[0065] Specifically, the preset threshold for each category is 2. Since the sensitivity data for each cross-section is associated with a node and a cross-section, given the known node types (new energy units, non-new energy units, and bus loads), each cross-section can be directly classified to obtain the aforementioned new energy transmission cross-sections, load center feed-in cross-sections, and network loop cross-sections. After classification, all cross-sections corresponding to the new energy transmission cross-sections have nodes where new energy units are located; all cross-sections corresponding to the load center feed-in cross-sections have nodes where bus loads are located; and all cross-sections corresponding to the network loop cross-sections contain at least two or more node types from the three categories: new energy units, non-new energy units, and bus loads.

[0066] Step S103: Based on the power output data, section sensitivity data, and section power flow data of the new energy transmission section, construct a new energy output prediction and adjustment optimization model with the goal of minimizing new energy output adjustment and a first constraint condition; based on the bus load data, section sensitivity data, and section power flow data of the load center feed-in section, construct a bus load prediction and adjustment optimization model with the goal of minimizing the initial value adjustment of the bus load and a second constraint condition; based on the power output data, section sensitivity data, and section power flow data of the non-new energy units corresponding to the network loop section, construct a non-new energy output adjustment optimization model with the goal of minimizing the adjustment of non-new energy units and a third constraint condition.

[0067] Specifically, for the new energy output forecasting adjustment and optimization model and the first constraint condition, only the data corresponding to the new energy transmission section was used among all the relevant data of all sections. For the bus load forecasting adjustment and optimization model and the second constraint condition, only the data corresponding to the load center feed-in section was used among all the relevant data of all sections. For the non-new energy output adjustment and optimization model and the third constraint condition, only the data corresponding to the network loop section was used among all the relevant data of all sections.

[0068] Preferably, by pre-classifying cross-section types based on source-load characteristics, the decision space is decoupled into targeted optimization sub-models, effectively avoiding the dimensionality curse caused by the coupling of global decision variables, significantly reducing computational complexity and accelerating the process of eliminating over-limits. Simultaneously, based on the cross-section characteristics such as renewable energy transmission and load inflow, associated flexible resources (such as renewable energy predicted output adjustment) are dynamically invoked to achieve targeted matching and optimization between source-grid-load-storage coordination and cross-section safety requirements.

[0069] In a preferred embodiment, the objective function of the above-mentioned new energy output prediction adjustment and optimization model is:

[0070]

[0071] In the formula, F new M represents the value of the objective function of the new energy output prediction, adjustment, and optimization model. N G represents the penalty factor for adjusting the output weight of new energy units in the new energy output forecasting and adjustment optimization model, where T represents the preset time set for the entire period, and G represents the penalty factor for adjusting the output weight of new energy units in the new energy output forecasting and adjustment optimization model. n It represents a collection of new energy generating units. This represents the upward adjustment of the predicted renewable energy output of renewable energy unit n corresponding to the renewable energy transmission section at time t. M represents the downward adjustment of the predicted renewable energy output of renewable energy unit n corresponding to the renewable energy transmission section at time t. S This represents the penalty factor for adjusting the weight of the relaxation amount exceeding the limit at the cross-section, and K represents the set of all cross-sections corresponding to the new energy transmission cross-section. This represents the upward relaxation of section k in the new energy transmission section at time t. This represents the downward relaxation of section k in the new energy transmission section at time t.

[0072] Preferably, the penalty factor M for adjusting the weight of the cross-sectional relaxation amount is... S It should be much larger than M N .

[0073] Preferably, the optimization model for adjusting and optimizing renewable energy output prediction only involves renewable energy transmission sections. Given that the predicted renewable energy output of renewable energy units may not completely eliminate all sections exceeding limits after adjustment, a slack variable for power flow exceeding limits (i.e., ...) is introduced into the objective function. and The optimization process prioritizes adjusting unit output to eliminate over-limits rather than relying on slack to relax constraints, thereby minimizing the adjustment cost of renewable energy output while ensuring the feasibility of the solution.

[0074] In this preferred embodiment, an objective function for a new energy output prediction, adjustment, and optimization model with the goal of minimizing new energy output adjustment is constructed based on the output data of the new energy units corresponding to the new energy transmission section.

[0075] In another preferred embodiment, the above-mentioned new energy unit output data includes: the adjustment amount of the new energy output forecast value, the new energy unit ramping interval value, and the initial value of the new energy unit output; the above-mentioned first constraint includes: the new energy unit output ramping constraint, the first power flow section no-limit constraint, and the first unit adjustment amount constraint.

[0076] The construction of the first constraint mentioned above includes:

[0077] Based on the adjustment of the ramping range value of the new energy unit corresponding to the new energy transmission section, the initial value of the new energy unit output, and the predicted value of the new energy output, the ramping constraint of the new energy unit output is constructed.

[0078] Specifically, adjustments to the predicted output of new energy units need to consider the status of these units after pre-clearance. The output can only be reduced, not increased, from the original predicted output. Therefore, the aforementioned output ramp-up constraint for new energy units is constructed as follows:

[0079]

[0080] In the formula, This represents the predicted renewable energy output of renewable energy unit n at time t, corresponding to the renewable energy transmission section. RU represents the initial output value of the renewable energy unit n corresponding to the renewable energy transmission section at time t. n This represents the upper boundary of the ramp-up range value for the new energy unit n corresponding to the new energy transmission section. RD represents the predicted renewable energy output of renewable energy unit n corresponding to the renewable energy transmission section at time t+1. n This represents the lower boundary of the ramp-up range value of the new energy unit n corresponding to the new energy transmission section.

[0081] Based on the power flow data and sensitivity data of the corresponding new energy transmission sections, the first power flow section non-over-limit constraint is constructed.

[0082] Specifically, at time point t, the adjusted tidal current across the cross-section is calculated based on the cross-sectional sensitivity to obtain the first tidal current cross-section's constraint of not exceeding the limit:

[0083]

[0084] In the formula, α k,t This represents the power flow safety margin of section k in the renewable energy transmission section at time t. This represents the upper limit of the power flow section k in the renewable energy transmission section at time t. δ represents the initial value of section k in the new energy transmission section at time t, before the optimization and elimination of section over-limits after pre-clearing.i,k This represents the section sensitivity of node i to section k in the renewable energy transmission section after the pre-clearing process, n. k G represents the renewable energy units that affect section k in the renewable energy transmission section. nk This represents the set of renewable energy units that affect section k in the renewable energy transmission section. This represents the number of new energy generating units n that affect section k in the new energy transmission section. k The downward adjustment of the predicted output of new energy sources at time t. This represents the number of new energy generating units n that affect section k in the new energy transmission section. k The upward adjustment of the predicted value of new energy output at time t. This represents the lower limit of the power flow section k in the new energy transmission section at time t.

[0085] Based on the adjustment amount of the above-mentioned new energy output forecast, the adjustment amount constraint of the first unit is constructed.

[0086] Specifically, to ensure that the adjustment amount of the predicted new energy output is non-negative, the adjustment constraint for the first unit is constructed as follows:

[0087]

[0088] In this preferred embodiment, the first constraint condition is constructed based on the adjustment amount of the predicted value of new energy power output, the ramp-up interval value of new energy units, the initial value of new energy unit output, the cross-sectional sensitivity data, and the cross-sectional power flow data.

[0089] In another preferred embodiment, the objective function of the above bus load prediction and adjustment optimization model is:

[0090]

[0091] In the formula, F L M represents the value of the objective function of the bus load forecasting and adjustment optimization model. L L represents the penalty factor used to adjust the weights of the bus load forecast values ​​in the bus load forecasting and adjustment optimization model. This represents the upward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the downward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the upward relaxation of section k in the load center feed section at time t. This represents the downward relaxation of section k in the load center feed section at time t.

[0092] Preferably, the penalty factor M for the above-mentioned adjustment section exceeding the relaxation limit weight is... S It should be much larger than M L .

[0093] Preferably, the bus load forecasting and adjustment optimization model only involves the load center feed-in sections. Since adjusting the bus load forecast may not completely eliminate all over-limit sections, a section power flow over-limit relaxation variable is also introduced into the objective function, and a corresponding penalty factor is set to weight the relaxation amount. By adjusting the numerical relationship of the penalty factor, the optimization process is forced to prioritize eliminating over-limits through bus load forecasting adjustments, rather than relying on relaxation to loosen constraints. This minimizes the adjustment cost of bus load forecasting while ensuring the feasibility of the solution.

[0094] In this preferred embodiment, an objective function for the bus load prediction and adjustment optimization model is constructed based on the bus load data corresponding to the load center feed section.

[0095] In another preferred embodiment, the bus load data includes: the initial value of the bus load and the adjustment amount of the predicted value of the bus load; the second constraint includes: the bus load prediction adjustment amount constraint, the second power flow section non-overrun constraint, and the bus load adjustment amount constraint.

[0096] The construction of the second constraint mentioned above includes:

[0097] Based on the adjustment amount of the bus load prediction value corresponding to the load center feed section and the initial value of the bus load, the above-mentioned bus load prediction adjustment amount constraint is constructed.

[0098] Specifically, the adjustment of the bus load forecast value needs to take into account the initial value after pre-clearance to avoid excessive adjustment. Therefore, a custom parameter η is introduced. k,t And the bus load forecast adjustment constraint is constructed as follows:

[0099]

[0100] In the formula, η represents the predicted bus load l at time t, corresponding to the load center feed section. k,t This represents the adjustment amount of the bus load forecast value at section k corresponding to the load center feed section, and the allowable range of variation of the bus load from the initial value at time t. This represents the initial value of the bus load l corresponding to the load center feed section at time t.

[0101] Based on the power flow data and sensitivity data of the section corresponding to the load center feed section, the above-mentioned second power flow section non-over-limit constraint is constructed.

[0102] Specifically, at time point t, based on the adjusted cross-sectional power flow calculation using cross-sectional sensitivity, the second power flow cross-section non-overflow constraint is constructed as follows:

[0103]

[0104] In the formula, α k,t This represents the cross-sectional power flow safety margin of section k in the load center feed section at time t, indicating that... This represents the upper limit of the power flow section k in the load center feed section at time t. δ represents the initial value of section k in the load center feed section at time t, before the optimization elimination of section over-limit after pre-clearing. i,k This represents the section sensitivity of node i to section k in the load center feed section after the pre-clearing is completed. k L represents the bus load that affects section k in the load center feed section. k This represents the set of bus loads that affect section k in the load center feed section. This represents the bus load l that affects section k in the load center feed section. k The downward adjustment of the bus load forecast at time t. This represents the bus load l that affects section k in the load center feed section. k The upward adjustment of the bus load forecast at time t. This represents the lower limit of the power flow section k in the load center feed section at time t.

[0105] Based on the adjustment amount of the above-mentioned bus load forecast value, the above-mentioned bus load adjustment amount constraint is constructed.

[0106] Specifically, to ensure power balance, the sum of the adjustments to all bus load forecasts at time t must be 0, and the adjustments to all bus load forecasts must be non-negative. Therefore, the bus load adjustment constraint is constructed as follows:

[0107]

[0108] In this preferred embodiment, a second constraint condition is constructed based on the bus load data, section sensitivity data, and section power flow data corresponding to the load center feed section.

[0109] In another preferred embodiment, the objective function of the above-mentioned non-new energy power output adjustment and optimization model is:

[0110]

[0111] In the formula, F uintM represents the value of the objective function of the non-new energy power output adjustment and optimization model. G G represents the penalty factor used to adjust the weight of non-renewable energy units in the non-renewable energy output adjustment and optimization model. f This represents a collection of non-new energy generating units. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the upward relaxation of section k corresponding to the network loop section at time t. This represents the downward relaxation of section k corresponding to the network loop section at time t.

[0112] Preferably, the penalty factor M for adjusting the weight of the cross-sectional relaxation amount is... S It should be much larger than M G .

[0113] Preferably, the non-renewable energy power output adjustment optimization model only involves network loop sections in its optimization process. Similarly, considering that there may be sections with incompletely eliminated over-limit constraints after unit output adjustment, a section power flow over-limit relaxation variable is introduced into the objective function, and a penalty factor is set to weight the relaxation amount. By configuring the numerical relationship of the penalty factor, the optimization process is guided to prioritize eliminating over-limit constraints through unit output adjustment, rather than relying on relaxation variables to relax the safety boundary. This minimizes the adjustment cost of non-renewable energy units while ensuring the feasibility of model solution.

[0114] In this preferred embodiment, the objective function of the non-new energy power output adjustment and optimization model is constructed based on the power output data of the non-new energy units corresponding to the network loop section.

[0115] In another preferred embodiment, the third constraint includes: output ramping constraint of non-new energy units, non-exceeding limit constraint of third power flow section, and adjustment amount constraint of second unit;

[0116] The construction of the third constraint mentioned above includes:

[0117] Based on the power output data of the non-new energy units corresponding to the above network loop sections, the power output ramping constraints of the above non-new energy units are constructed.

[0118] Specifically, the output ramp-up constraints for the aforementioned non-new energy generating units are as follows:

[0119]

[0120] In the formula, This represents the non-renewable energy unit g corresponding to the network loop section, and its non-renewable energy output value at time t. This represents the initial output value of the non-new energy unit g corresponding to the network loop section at time t. This represents the upper boundary of the power generation range of the non-new energy unit g corresponding to the network loop section at time t. RU represents the lower boundary of the power generation range of the non-new energy unit g corresponding to the network loop section at time t. g This represents the upper boundary of the ramp-up section for the non-new energy unit g corresponding to the network loop section. RD represents the non-renewable energy output value of the non-renewable energy unit g corresponding to the network loop section at time t+1. g This represents the lower boundary of the ramp-up interval for the non-new energy unit g corresponding to the network loop section.

[0121] Based on the power flow data and sensitivity data of the corresponding sections of the network loop sections, the above-mentioned third power flow section non-over-limit constraint is constructed.

[0122] Specifically, at time point t, based on the adjusted cross-sectional power flow calculation using cross-sectional sensitivity, the constraint for the third power flow cross-section not to exceed the limit is constructed as follows:

[0123]

[0124] In the formula, α k,t This represents the power flow safety margin of section k in the network loop section at time t. This represents the upper limit of the power flow section k in the network loop section at time t. δ represents the initial value of section k in the network loop section at time t before the optimization elimination of section over-limit after pre-clearing. i,k G represents the section sensitivity of node i to section k in the network loop section after the pre-clearing is completed. k G represents the non-new energy generating unit that has an impact on section k in the network loop. k This represents the set of non-new energy generating units that influence section k in the network loop. This represents the non-new energy unit g that affects section k in the network loop. k The downward adjustment of the non-new energy power output value at time t. This represents the non-new energy unit g that affects section k in the network loop. k The upward adjustment of the non-new energy power output value at time t. This represents the lower limit of the power flow section k in the network loop section at time t.

[0125] Based on the adjustment amount of the non-new energy power output value in the non-new energy unit output data corresponding to the above network loop section, the above-mentioned second unit adjustment amount constraint is constructed.

[0126] Specifically, to ensure power balance, the sum of the adjustments of all non-new energy units at time t must be 0, and all adjustments must be non-negative. Therefore, the second unit adjustment constraint is constructed:

[0127]

[0128] Preferably, when constructing the optimization model, actual operating boundaries such as ramp constraints and dead zones are embedded to ensure that the optimization results can be directly mapped to the scheduling command system, thereby reducing the complexity of on-site deployment.

[0129] In this preferred embodiment, the adjustment constraint of the second unit is constructed based on the output data of the non-new energy unit, the cross-sectional power flow data, and the cross-sectional sensitivity data.

[0130] Step S104: Under the first, second, and third constraints mentioned above, solve the above-mentioned new energy power output prediction and adjustment optimization model, bus load prediction and adjustment optimization model, and non-new energy power output adjustment optimization model to obtain the optimal new energy power output prediction value, the optimal bus load prediction value, and the optimal non-new energy power output value, and then perform over-limit regulation on the corresponding units and bus loads.

[0131] Simultaneously, after solving each optimization model, the optimal predicted value of renewable energy output, the optimal predicted value of bus load, and the optimal value of non-renewable energy output are obtained. Based on the optimal predicted value of renewable energy output, the optimal predicted value of bus load, and the optimal value of non-renewable energy output, the corresponding unit and bus load are adjusted to eliminate power flow over-limit.

[0132] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0133] like Figure 2 As shown, an embodiment of the present invention provides a power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics, comprising:

[0134] Data acquisition module, cross-section division module, model building module, and model solving module;

[0135] The aforementioned data acquisition module is used to acquire power dispatch system output data of new energy units, output data of non-new energy units, bus load data, section sensitivity data, and section power flow data under preset all time periods;

[0136] The aforementioned section division module is used to classify sections in which all nodes in the above section sensitivity data belong to new energy unit nodes as new energy transmission sections; sections in which all nodes belong to bus load nodes as load center feed-in sections; and sections in which the types of nodes are not less than a preset type threshold as network loop sections.

[0137] The aforementioned model building module is used to construct a new energy output prediction and adjustment optimization model with the goal of minimizing new energy output adjustment, based on the new energy unit output data, section sensitivity data, and section power flow data corresponding to the aforementioned new energy transmission section; to construct a bus load prediction and adjustment optimization model with the goal of minimizing the initial value adjustment of the bus load, based on the bus load data, section sensitivity data, and section power flow data corresponding to the aforementioned load center feed-in section; and to construct a non-new energy output adjustment optimization model with the goal of minimizing the adjustment of non-new energy units, based on the non-new energy unit output data, section sensitivity data, and section power flow data corresponding to the aforementioned network loop section.

[0138] The aforementioned model solving module is used to solve the aforementioned new energy power output prediction and adjustment optimization model, bus load prediction and adjustment optimization model, and non-new energy power output adjustment optimization model under the aforementioned first constraint condition, second constraint condition, and third constraint condition, respectively, to obtain the optimal new energy power output prediction value, the optimal bus load prediction value, and the optimal non-new energy power output value, and to perform over-limit regulation on the corresponding unit and bus load.

[0139] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort. The above schematic diagram is merely an example of a power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics, and does not constitute a limitation on a power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics. It may include more or fewer components than illustrated, or combine certain components, or use different components.

[0140] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0141] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics as described in any embodiment of the present invention.

[0142] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.

[0143] The aforementioned terminal devices may be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. These devices may include, but are not limited to, processors and memory.

[0144] The processor can 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. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0145] The aforementioned memory can be used to store the aforementioned computer programs and / or modules. The aforementioned processor implements various functions of the aforementioned device by running or executing the computer programs and / or modules stored in the aforementioned memory, and by calling data stored in the memory. The aforementioned memory may mainly include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application program required for a function, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0146] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0147] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics described in any embodiment of the present invention.

[0148] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0149] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics, characterized in that, include: Acquire power dispatch system output data of new energy units, output data of non-new energy units, bus load data, section sensitivity data, and section power flow data under preset all time periods; Among all the section sensitivity data, the section whose nodes all belong to new energy unit nodes is regarded as the new energy transmission section; the section whose nodes all belong to bus load nodes is regarded as the load center feed-in section; and the section whose node types are not less than a preset type threshold is regarded as the network loop section. Based on the power output data of the new energy units corresponding to the new energy transmission section, the section sensitivity data, and the section power flow data, a new energy output prediction and adjustment optimization model with the goal of minimizing new energy output adjustment and a first constraint condition are constructed. Based on the bus load data, section sensitivity data, and section power flow data corresponding to the load center feed section, a bus load prediction and adjustment optimization model with the goal of minimizing the initial value adjustment of the bus load and a second constraint condition are constructed; based on the non-new energy unit output data, section sensitivity data, and section power flow data corresponding to the network loop section, a non-new energy unit output adjustment optimization model with the goal of minimizing the adjustment of non-new energy units and a third constraint condition are constructed. Under the first, second, and third constraints respectively, the new energy output prediction and adjustment optimization model, the bus load prediction and adjustment optimization model, and the non-new energy output adjustment optimization model are solved to obtain the optimal new energy output prediction value, the optimal bus load prediction value, and the optimal non-new energy output value, and the corresponding unit and bus load are adjusted beyond the limit.

2. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 1, characterized in that, The objective function of the new energy output prediction, adjustment and optimization model is: In the formula, F new M represents the value of the objective function of the new energy output prediction, adjustment, and optimization model. N G represents the penalty factor for adjusting the output weight of new energy units in the new energy output forecasting and adjustment optimization model, where T represents the preset time set for the entire period, and G represents the penalty factor for adjusting the output weight of new energy units in the new energy output forecasting and adjustment optimization model. n It represents a collection of new energy generating units. This represents the upward adjustment of the predicted renewable energy output of renewable energy unit n corresponding to the renewable energy transmission section at time t. M represents the downward adjustment of the predicted renewable energy output of renewable energy unit n corresponding to the renewable energy transmission section at time t. S This represents the penalty factor for adjusting the weight of the relaxation amount exceeding the limit at the cross-section, and K represents the set of all cross-sections corresponding to the new energy transmission cross-section. This represents the upward relaxation of section k in the new energy transmission section at time t. This represents the downward relaxation of section k in the new energy transmission section at time t.

3. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 2, characterized in that, The power output data of the new energy units includes: the adjustment amount of the predicted power output of new energy units, the ramp-up range value of new energy units, and the initial power output value of new energy units; the first constraint condition includes: the ramp-up constraint of new energy unit power output, the first power flow section non-exceeding constraint, and the first unit adjustment amount constraint. The construction of the first constraint includes: Based on the adjustment amount of the ramping interval value of the new energy unit corresponding to the new energy transmission section, the initial value of the new energy unit output, and the predicted value of the new energy output, the ramping constraint of the new energy unit output is constructed. Based on the power flow data and sensitivity data of the corresponding new energy transmission section, the first power flow section's non-over-limit constraint is constructed. Based on the adjustment amount of the predicted output value of the new energy source, the adjustment amount constraint of the first unit is constructed.

4. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 3, characterized in that, The objective function of the bus load prediction and adjustment optimization model is: In the formula, F L M represents the value of the objective function of the bus load forecasting and adjustment optimization model. L L represents the penalty factor used to adjust the weights of the bus load forecast values ​​in the bus load forecasting and adjustment optimization model. This represents the upward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the downward adjustment of the predicted bus load l corresponding to the load center feed section at time t. This represents the upward relaxation of section k in the load center feed section at time t. This represents the downward relaxation of section k in the load center feed section at time t.

5. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 4, characterized in that, The bus load data includes: the initial value of the bus load and the adjustment amount of the predicted value of the bus load; the second constraint includes: the bus load prediction adjustment amount constraint, the second power flow section non-overrun constraint, and the bus load adjustment amount constraint. The construction of the second constraint includes: Based on the adjustment amount of the predicted bus load value corresponding to the load center feed section and the initial value of the bus load, the bus load prediction adjustment amount constraint is constructed. Based on the power flow data and sensitivity data of the section corresponding to the load center feed section, the second power flow section non-over-limit constraint is constructed. Based on the adjustment amount of the predicted bus load, the bus load adjustment amount constraint is constructed.

6. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 5, characterized in that, The objective function of the non-new energy power output adjustment and optimization model is: In the formula, F uint M represents the value of the objective function of the non-new energy power output adjustment and optimization model. G G represents the penalty factor used to adjust the weight of non-renewable energy units in the non-renewable energy output adjustment and optimization model. f This represents a collection of non-new energy generating units. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the adjustment amount of the non-new energy unit g corresponding to the network loop section at time t, based on the non-new energy output value. This represents the upward relaxation of section k corresponding to the network loop section at time t. This represents the downward relaxation of section k corresponding to the network loop section at time t.

7. The power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics according to claim 6, characterized in that, The third constraint includes: output ramping constraint of non-new energy units, non-exceeding limit constraint of third power flow section, and adjustment amount constraint of second unit. The construction of the third constraint includes: Based on the output data of the non-new energy units corresponding to the network loop section, the output ramping constraint of the non-new energy units is constructed. Based on the power flow data and sensitivity data of the corresponding network loop section, the non-override constraint of the third power flow section is constructed. The second unit adjustment constraint is constructed based on the adjustment amount of the non-new energy unit output value in the non-new energy unit output data corresponding to the network loop section.

8. A power flow over-limit elimination device based on cross-sectional sensitivity and source load characteristics, characterized in that, include: Data acquisition module, cross-section division module, model building module, and model solving module; The data acquisition module is used to acquire the power dispatch system's output data of new energy units, output data of non-new energy units, bus load data, cross-sectional sensitivity data, and cross-sectional power flow data under preset all-time periods. The section division module is used to classify sections in which all nodes in the section sensitivity data belong to new energy unit nodes as new energy transmission sections; sections in which all nodes belong to bus load nodes as load center feed-in sections; and sections in which the types of nodes are not less than a preset type threshold as network loop sections. The model building module is used to construct a new energy output prediction, adjustment and optimization model with the goal of minimizing new energy output adjustment, and a first constraint condition, based on the new energy unit output data, section sensitivity data and section power flow data corresponding to the new energy transmission section. Based on the bus load data, section sensitivity data, and section power flow data corresponding to the load center feed section, a bus load prediction and adjustment optimization model with the goal of minimizing the initial value adjustment of the bus load and a second constraint condition are constructed; based on the non-new energy unit output data, section sensitivity data, and section power flow data corresponding to the network loop section, a non-new energy unit output adjustment optimization model with the goal of minimizing the adjustment of non-new energy units and a third constraint condition are constructed. The model solving module is used to solve the new energy output prediction and adjustment optimization model, the bus load prediction and adjustment optimization model, and the non-new energy output adjustment optimization model under the first constraint condition, the second constraint condition, and the third constraint condition, respectively, to obtain the optimal new energy output prediction value, the optimal bus load prediction value, and the optimal non-new energy output value, and to perform over-limit adjustment on the corresponding unit and bus load.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform a power flow over-limit elimination method based on cross-sectional sensitivity and source load characteristics as described in any one of claims 1 to 7.

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