Resource scheduling method, system, device and medium based on dynamic coupling weight
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA SOUTHERN POWER GRID COMPANY
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-23
AI Technical Summary
Existing resource scheduling methods based on dynamic coupling weights fail to effectively consider the needs of ancillary services such as emergency response, frequency regulation, and reserve during step-by-step scheduling, resulting in poor scheduling performance and increased power system operating costs.
By acquiring the characteristic parameters of market participants and the power grid operation parameters, calculating the first and second weighting factors, establishing a dynamic coupling weight matrix, constructing a unified resource scheduling system, optimizing the solution of resource scheduling schemes, and ensuring the deep integration of market decision-making and power grid physical operation.
It significantly improves the coordination, economy, and security of power market resource dispatch, realizes dynamic conversion of resource capacity across markets and equivalent quantification of power grid physical constraints, and ensures fair competition and optimal resource allocation among markets.
Smart Images
Figure CN122267905A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system technology, and in particular to a resource scheduling method, system, device and medium based on dynamic coupling weights. Background Technology
[0002] In power system operation, resource dispatch is a core component to ensure the safe and stable operation of the system and the reliable supply of electricity. Dispatching agencies need to rationally allocate the output or reserved capacity of each market participant in different electricity markets based on the technical characteristics of each market participant, the operating status of the power grid, and the operational needs of the system, in order to achieve optimal resource allocation.
[0003] Currently, the commonly used resource scheduling method based on dynamic coupling weights adopts a step-by-step scheduling mode. That is, firstly, based on the bidding information of each market participant in the energy market and the system load demand, the power generation output of each market participant is determined through optimization calculation. Then, based on the bidding information of each market participant in the ancillary service market and the system regulation demand, a second optimization calculation is performed on the basis of the determined power generation output to determine the reserved capacity of each market participant in frequency regulation service and reserve service respectively.
[0004] However, in distributed dispatching, the demand for ancillary services such as emergency response, frequency regulation, and backup is not taken into account when determining the power generation, resulting in poor dispatching performance and increased power system operating costs. Summary of the Invention
[0005] This invention provides a resource scheduling method, system, device, and medium based on dynamic coupling weights, which can solve the problem of poor scheduling performance caused by step-by-step scheduling.
[0006] This invention provides a resource scheduling method based on dynamic coupling weights, comprising: For each market participant, obtain the characteristic parameters and demand parameters corresponding to each electricity market, and obtain the grid operation parameters and power flow parameters of the transmission sections; Based on the characteristic parameters, the operating parameters, and the power flow parameters, a first weighting factor is obtained to characterize the conversion relationship of the same resource capacity in each of the electricity markets. Based on the operating parameters and the power flow parameters, a second weighting factor is obtained to characterize the equivalent impact relationship of the same resource allocation on the physical constraints of the power grid. Constraints are determined based on the first weighting factor and the second weighting factor corresponding to each market participant. Under these constraints, optimization is performed with the objective of minimizing the cost of each market participant in each electricity market to obtain a resource scheduling scheme. Each market participant is then controlled to execute the resource scheduling scheme.
[0007] This invention establishes a unified resource scheduling system for multiple electricity markets through a complete process of multi-source data acquisition, dynamic weight factor calculation, coupling constraint construction, and collaborative optimization solution. It realizes the dynamic conversion of the value of the same resource across different markets and the equivalent quantification of its contribution to the physical constraints of the power grid. This significantly improves the coordination, economy, and security of power market resource scheduling, and ensures the deep integration and optimal matching of market decisions with the physical operating characteristics of the power grid.
[0008] Furthermore, the calculation based on the characteristic parameters, the operating parameters, and the power flow parameters to obtain a first weighting factor characterizing the conversion relationship of the same resource capacity in each of the electricity markets is specifically as follows: For each of the aforementioned electricity markets, obtain the corresponding preset adjustment parameters and preset conversion factors; The evaluation value corresponding to the electricity market is calculated based on the characteristic parameters, and the emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow. The first weighting factor corresponding to the electricity market is obtained by calculating the preset adjustment parameters, preset conversion coefficients, the evaluation value, and the emergency coefficient.
[0009] By configuring static parameters, assessing subject characteristics, quantifying system status, and performing multi-factor fusion calculations, a refined generation mechanism for the first weighting factor was established. This enabled dynamic and comprehensive consideration of market characteristics, subject capabilities, and system requirements in cross-market resource capacity conversion, significantly improving the accuracy, timeliness, and adaptability of resource capacity conversion relationships. It also provided a reliable quantitative basis for fair competition and optimal resource allocation in the market.
[0010] Furthermore, the second weighting factor, calculated based on the operating parameters and the power flow parameters, to characterize the equivalent impact of the same resource allocation on the physical constraints of the power grid, is specifically as follows: For each of the power transmission sections, obtain the corresponding preset power transmission distribution factor; The congestion coefficient corresponding to each transmission section is calculated based on the power flow parameters. The power transmission distribution factor, the congestion coefficient, and the evaluation value are calculated to obtain the second weighting factor corresponding to the electricity market.
[0011] By presetting static physical factors, quantifying dynamic congestion, and integrating subject effectiveness, a scientific computational mechanism for the second weighting factor was established. This enabled a dynamic and comprehensive representation of the impact of resource allocation on the physical constraints of the power grid, considering geographical location, situation, and service quality. This significantly improved the accuracy, effectiveness, and efficiency of resource security contribution assessment, providing a reliable physical quantitative basis for market decision optimization under power grid security constraints.
[0012] Furthermore, the electricity market includes a response market, a frequency regulation market, and a reserve market; the characteristic parameters include a first response time in the response market, a frequency regulation rate in the frequency regulation market, and a second response time in the reserve market. The calculation of the assessment value corresponding to the electricity market based on the characteristic parameters is specifically as follows: Obtain historical adjustment coefficients, historical call success rates, and frequency modulation rate weighting factors; Based on the first response time and the preset first response parameters, the first evaluation value of the market participant in the response market is calculated; The second evaluation value of the market participant in the frequency modulation market is obtained by calculating based on the frequency modulation rate weighting factor, the historical adjustment coefficient, the frequency modulation rate, and the preset frequency modulation rate threshold. The third evaluation value of the market participant in the standby market is calculated based on the historical call success rate, the second response time, and the preset second response parameters.
[0013] By segmenting market types, extracting differentiated indicators, integrating historical performance, and conducting multi-dimensional evaluation calculations, a refined evaluation system for the three types of markets—response, frequency regulation, and standby—was established. This system enables precise quantification and scientific evaluation of the service capabilities of entities under different market characteristics, significantly improving the relevance, comprehensiveness, and accuracy of market entity evaluations. It also provides fine-grained data support for the optimal allocation of resources across markets.
[0014] Further, the calculation of the market participant's first evaluation value in the response market based on the first response time and preset first response parameters specifically involves: Obtain the preset response threshold; When the first response time is greater than the response threshold, the first evaluation value is determined to be zero. When the first response time is less than or equal to the response threshold, the first response time and the first response parameter are calculated according to the exponential decay function to obtain the first evaluation value.
[0015] By setting response thresholds, employing segmented evaluation strategies, and applying exponential decay functions, a scientific computational mechanism for response market evaluation values was established. This mechanism organically combines the screening of response capability qualifications with incentives for excellent performance, significantly improving the discriminative power and incentive effect of response speed evaluation. It ensures that rapid response resources receive evaluation recognition commensurate with their value, thereby encouraging market participants to invest in improving response speed.
[0016] Furthermore, the operating parameters include load change rate, frequency deviation value, and reserve capacity; The emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow, specifically as follows: Obtain the preset power grid capacity threshold; Determine the maximum value among all the power flow parameters, and set the maximum value as the first urgency coefficient of the response market; The load change rate and the frequency deviation value are normalized, and the normalized load change rate and the normalized frequency deviation value are weighted and summed to obtain the second emergency coefficient corresponding to the frequency regulation market. The ratio between the reserve capacity and the grid capacity threshold is calculated, and the third emergency coefficient corresponding to the reserve market is determined based on the ratio.
[0017] By acquiring multi-dimensional operating parameters, normalizing them, weighting and fusion them, and calculating ratios, a differentiated emergency coefficient generation mechanism for the three types of markets—response, frequency regulation, and reserve—was established. This mechanism enables a precise and dynamic mapping of the power grid's operational status to the market's urgency level, significantly improving the accuracy, relevance, and market-specificity of market urgency assessment. It also provides a reliable system status input for dynamic weight calculation and market priority adjustment.
[0018] Furthermore, the constraint conditions include a first constraint condition and a second constraint condition; The determination of constraints based on the first weighting factor and the second weighting factor corresponding to each market participant specifically includes: For each market participant, the resource dispatch value corresponding to each electricity market is weighted and summed with the first weighting factor to obtain the capacity value corresponding to the market participant. The first constraint condition is to constrain the capacity value to be less than or equal to the preset market capacity threshold corresponding to the market participant. For each transmission section, the resource scheduling value of each market participant in each electricity market is weighted and summed with the second weighting factor to obtain the power flow value corresponding to the transmission section. The power flow value is constrained to be less than or equal to the preset transmission threshold corresponding to the transmission section as the second constraint condition.
[0019] By constructing a two-layer constraint system, cross-market weighted aggregation, and dual boundary control, a complete constraint framework integrating market capacity constraints and power grid security constraints was established. This achieved deep coupling between market participant decision-making and system physical operation, significantly improving the collaborative guarantee capability of individual feasibility and system security in resource scheduling optimization, and providing a solid constraint foundation and reliable solution boundary for multi-market joint clearing.
[0020] Another embodiment of the present invention provides a resource scheduling system based on dynamic coupling weights, including: an acquisition module, a weight module, and a scheduling module; The acquisition module is used to acquire characteristic parameters and demand parameters corresponding to each electricity market for each market participant, and to acquire the operating parameters of the power grid and the power flow parameters of the transmission section. The weighting module is used to calculate, based on the characteristic parameters, the operating parameters, and the power flow parameters, a first weighting factor to characterize the conversion relationship of the same resource capacity in each of the electricity markets, and to calculate, based on the operating parameters and the power flow parameters, a second weighting factor to characterize the equivalent impact relationship of the same resource call on the physical constraints of the power grid. The scheduling module is used to determine constraints based on the first weighting factor and the second weighting factor corresponding to each market participant, and to perform optimization under the constraints with the objective of minimizing the cost of each market participant in each electricity market, thereby obtaining a resource scheduling scheme and controlling each market participant to execute the resource scheduling scheme.
[0021] 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, wherein when the processor executes the computer program, it implements the steps of the resource scheduling method based on dynamic coupling weights of the present invention.
[0022] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the resource scheduling method based on dynamic coupling weights of the present invention. Attached Figure Description
[0023] 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.
[0024] Figure 1 This is a flowchart illustrating a resource scheduling method based on dynamic coupling weights provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a resource scheduling system based on dynamic coupling weights provided in an embodiment of the present invention. Detailed Implementation
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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).
[0031] 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.
[0032] See Figure 1 To address the problem of poor scheduling performance caused by step-by-step scheduling in existing technologies, an embodiment of the present invention provides a resource scheduling method based on dynamic coupling weights, comprising: Step 101: For each market participant, obtain the characteristic parameters and demand parameters corresponding to each electricity market, and obtain the power grid operation parameters and power flow parameters of the transmission section.
[0033] Among them, the transmission section is a collection of a series or parallel transmission lines in the power grid, which is usually used to connect two regional power grids.
[0034] In this step, for each market participant, characteristic parameters and demand parameters corresponding to each electricity market are obtained, along with the grid operation parameters and power flow parameters of transmission sections. Specifically, the characteristic parameters include energy market declaration data and ancillary service market declaration data. Energy market declaration data can be the bidding information of each market participant i for the target time period T (such as 13:00-13:15 in the next trading cycle), including price-power pairs, i.e., the electrical energy that the participant is willing to buy or sell at that price, which can be represented as the supply curve or demand curve of participant i. The ancillary service market includes the frequency regulation market, the reserve market, and the emergency response service market. For the frequency regulation market, the declaration data includes the upward capacity declared by participant i. Reduce capacity Corresponding capacity pricing And the frequency modulation rate that characterizes its frequency modulation performance. For the standby market, the reported data includes the standby capacity reported by participant i. Response time and capacity pricing The frequency regulation market and standby can be considered general ancillary service markets, while the emergency response service market is an emergency ancillary service market. Therefore, participants are distinguished by the denoting "j". For the emergency response service market, the reported data includes available capacity. (Participant j declares the maximum service power available during time period T), first response time (From receiving the call instruction to completing the entire process) (Time elapsed for capacity output) and duration (One call can maintain) The maximum duration of capacity), physical node identifier Loc(j) (the grid bus number to which the declared resource is connected, which is the basis for spatially coupling market behavior with the physical state of the grid), and quotation information (capacity price). and energy compensation price That is, the price of maintaining a state of readiness and the price of actually executing a response.
[0035] The running parameters are the current time. The power grid data includes network topology and voltage amplitude at each node. and phase angle The meritorious currents of each branch road The current flow parameter, also known as the current flow occupancy rate, is expressed as... ,in, The total active power flow at section k can be obtained by accumulating the absolute values of the power flow of the corresponding branch at that section. The static safe transmission limit of section k is given by the system operation procedure or online safety analysis. It is a value between 0 and 1 that dynamically quantifies the level of congestion or stress at that network location.
[0036] It should be noted that the transmission section referred to in this embodiment refers to a critical transmission section, which is a transmission section where if the power flow exceeds the safety limit, it will directly threaten the stable operation of the entire power grid and even trigger a chain of failures.
[0037] Furthermore, all implementation characteristics and operational parameters for the target time period T are time-stamped and clearly defined. Starting from the current state, and aiming to optimize the grid operation during time period T, the electricity market clearing and dispatching are carried out. Specifically, the dispatching scheme for time period T should conform to both the bidding characteristics of market participants and the objective laws of the physical operation of the power grid.
[0038] Step 102: Based on the characteristic parameters, the operating parameters, and the power flow parameters, calculate to obtain a first weighting factor that characterizes the conversion relationship of the same resource capacity in each of the electricity markets, and based on the operating parameters and the power flow parameters, calculate to obtain a second weighting factor that characterizes the equivalent impact relationship of the same resource allocation on the physical constraints of the power grid.
[0039] In this step, firstly, the first weighting factor (i.e., the substitution weighting factor) is calculated. This factor addresses how to allocate the capacity of the same physical resource (such as a generator unit) among competing markets (such as the energy market and the frequency regulation market). It is not a fixed value but is dynamically calculated based on the resource's characteristic parameters (such as response performance) and the corresponding market's demand parameters (such as urgency). This factor quantifies the equivalent physical capacity occupied when a unit of resource capacity is allocated to a non-energy market, relative to the energy market allocated to it. The higher the factor value, the higher the capacity value of the resource in that market under the current system state, and the more priority it should be given. Secondly, the second weighting factor (i.e., the complementarity weighting factor) is calculated. This factor is used to evaluate the actual contribution of calling upon a market resource (such as providing backup or emergency services) to alleviating specific grid physical constraints (such as overload of critical sections). It is based on the static geographical location influence of the resource (such as the power transmission distribution factor) and dynamically adjusted in conjunction with its characteristic parameters (such as response speed) and grid power flow parameters (such as the degree of section congestion). This factor quantifies the real, equivalent impact of resource-provided services on solving specific security problems, transforming static physical potential into dynamic, effective contributions. Finally, based on the calculated first and second weighting factors, a dynamic coupling weight matrix is constructed. The rows of this matrix represent various physical or rule constraints (such as resource capacity limits and cross-sectional transmission limits), the columns represent decision variables for each market (such as energy output and reserve capacity), and the matrix elements are the corresponding first and second weighting factors, thus structuring the coupling relationships.
[0040] Step 103: Determine the constraints based on the first weighting factor and the second weighting factor corresponding to each market participant. Under the constraints, perform optimization to minimize the cost of each market participant in each electricity market to obtain a resource scheduling scheme, and control each market participant to execute the resource scheduling scheme.
[0041] In this step, an objective function is constructed to minimize the total procurement cost, which encompasses the expected payments across all markets. The expression for the objective function is as follows: ; in, For all resources participating in the energy market, Contribute to Resource i's successful bid in the energy market. ( Let be the price cost function of resource i in the energy market. This is a collection of all FM and standby markets. For the set of resources declared in market m, The winning bid capacity of resource i in market m. For resource i, the capacity quotation submitted by m in the market. This is a collection of resources from all participants in the emergency response services market. For resource j, the winning bid capacity in market D, This refers to the capacity quotation submitted by resource j in market D.
[0042] As an example of an embodiment of the present invention, the constraint conditions include a first constraint condition and a second constraint condition; The determination of constraints based on the first weighting factor and the second weighting factor corresponding to each market participant specifically includes: For each market participant, the resource dispatch value corresponding to each electricity market is weighted and summed with the first weighting factor to obtain the capacity value corresponding to the market participant. The first constraint condition is to constrain the capacity value to be less than or equal to the preset market capacity threshold corresponding to the market participant. For each transmission section, the resource scheduling value of each market participant in each electricity market is weighted and summed with the second weighting factor to obtain the power flow value corresponding to the transmission section. The power flow value is constrained to be less than or equal to the preset transmission threshold corresponding to the transmission section as the second constraint condition.
[0043] In this embodiment, the constraints also include: energy balance constraints, expressed as = ,in, The total load forecast is used to ensure a balance between power generation and consumption; the ancillary service demand constraint is that for each market m, there is... ≥ ,in, Let m be the total market demand capacity of the system (in MW); and let m be the emergency response service demand constraint. ≥ ,in, The system represents the total demand capacity of market D; resource application upper and lower limits are constrained, meaning that for each decision variable, there are upper and lower limits for the application capacity.
[0044] The first constraint is a resource capacity coupling constraint, meaning that for each resource p, its total physical capacity consumption must satisfy the following expression: ; in, Contribute to resource P's successful bid in the energy market. It is the first weighting factor of resource p in market m. Resource P in the emergency response service market The first weighting factor, The set of frequency regulation and reserve markets in which resource p participates. This represents the maximum output of resource p. When resource p does not participate in a service market, the corresponding term is 0. It should be noted that resource p here represents the set of i and j. The first term of the formula represents the winning bid output of resource i or j in the energy market, and the second and third terms correspond to the auxiliary market and the emergency / prevention market, respectively.
[0045] It should be noted that this solution introduces dynamic... It accurately quantifies the physical capacity equivalent to the reserved 1MW ancillary service capacity, and this occupancy changes dynamically with the urgency of the system's demand for the service and the performance score of the resource itself.
[0046] The second constraint is a security coupling constraint, meaning that for each critical transmission section k, its power flow must be limited by the secure transmission limit, and this power flow is jointly determined by the clearing results of all markets, expressed as: ; in, It is the second weighting factor of resource i in market m at transmission section k. It is the second weighting factor of resource j at transmission section k. It is the maximum safe transmission power of section k.
[0047] It should be noted that this scheme also incorporates the capacity of ancillary and preventative services as decision variables affecting power flow into the constraints, and uses dynamic weights. Precise quantification of the effective impact of resources on cross-sections amplifies the contribution of rapid and accurate resource response to safety during periods of severe congestion.
[0048] The above constraints and objective function constitute a large-scale linear programming problem, which is solved by calling an optimization solver. The optimization calculation yields all decision variables ( , , The optimal value of ) is the clearing amount of each market in the resource scheduling scheme. The clearing of each market participant is controlled according to the resource scheduling scheme. The dual variable corresponding to each constraint can also be obtained to characterize the degree of improvement of the overall objective function value by the marginal relaxation of the constraint.
[0049] Furthermore, the clearing price of each market can be determined based on the dual variables of the relevant constraints. Specifically, the energy market price is determined based on the dual variables of the energy balance constraints, denoted as... In this model, because security constraints are coupled with multiple market variables, energy prices inherently include the cost of the system's safety margin. The clearing price in the ancillary services market, taking the capacity price in the frequency regulation market as an example, is determined by the dual variable of the frequency regulation demand constraint. Specifically, due to the existence of resource capacity coupling constraints, This reflects not only the scarcity of frequency regulation capacity but also the opportunity cost incurred by occupying energy capacity. The clearing price of the emergency response service market is determined by the dual variable of its demand constraint. The given price incorporates its value contribution to alleviating cybersecurity congestion. The output also includes the dual variables for each resource capacity coupling constraint. and the dual variables of each network security coupling constraint. This represents the total system cost savings resulting from the marginal increase in the physical capacity of resource i. In other words, if the maximum physical capacity of resource i increases by 1MW, the total procurement cost of the system can be reduced by the amount that meets all market demands and grid constraints. This represents the total system cost savings resulting from the marginal increase in the transmission limit of section k.
[0050] Based on the resource identifier and suggested call amount in the resource scheduling scheme, a power setting value instruction is generated and sent to the corresponding resource execution response. The revenue of each market is calculated by taking the actual implementation of the scheme in each market as an example.
[0051] First, following the existing settlement principles of the electricity market, we calculate the basic revenue for each market participant in each market. The revenue for energy market participant i is expressed as: ; in, To assign power to participant i in the energy market. The clearing price for the energy market. For participant i, the basic settlement revenue in the energy market; Similarly, the payoff for participant i in the ancillary services market m is expressed as: ; in, It is the basic settlement revenue of participant i in the ancillary services market m. It is the clearing price of the ancillary services market. Let i be the winning bid volume in the ancillary services market m. The payoff for emergency response service market participant j is expressed as follows: ; in, For participant j, the basic settlement revenue in the emergency response services market, The clearing price for the emergency response services market. For participant j, the winning bid capacity in the emergency response services market By analyzing the internal dual information of the joint clearing model, the coupling value of each market participant's behavior on other markets and the system as a whole is calculated. This coupling value originates from the dynamic coupling constraints introduced in this scheme. Each such constraint corresponds to a dual variable (shadow price), which quantifies the total cost saved for society as a whole by marginally relaxing the constraint—that is, the scarcity value of the resource represented by the constraint. Resource capacity coupling constraints correspond to the scarcity value of capacity, and their shadow prices... This represents the total system cost saved by increasing the total physical capacity of resources by one unit; network security coupling constraints correspond to the value of transmission security, its shadow price. This represents the total system cost savings achieved by increasing the transmission limit of section k by one unit. Then, the contribution share of each market participant's behavior to the aforementioned scarcity value is calculated, i.e., the uniform settlement factor. It represents the compensation that the participant is entitled to for its coupling contribution beyond its underlying market gains.
[0052] For a resource p that provides multiple services, the coupling value calculation is represented as: ; This formula calculates the excess physical capacity of resource p when providing services to market m. This excess capacity cannot be used in the energy market, but its value (determined by...) (Measurement) should be compensated. By combining dynamic weights with shadow prices, this opportunity cost compensation, which is undetectable in traditional settlements, is precisely quantified.
[0053] For all service providers that affect network security, the calculation is expressed as follows: ; in, Let p be the set of all transmission sections affected by resource p. The above formula calculates the additional effective security contribution provided by the resource beyond its static geographical location. This additional contribution saves the system significant transmission congestion costs (caused by...). (Measurement). The above formula captures and quantifies the additional security value beyond fixed physical connections brought about by the dynamic performance of resources and system state, and compensates accordingly.
[0054] Finally, the basic revenue of each market participant is added to the coupling contribution compensation to obtain the total amount paid to each market participant. Theoretically, the U value of the coupling contribution compensation may be negative, indicating that the resource performance is substandard, the value of reserved capacity is low, or the security value is insufficient, but in this embodiment, the lower limit of U is set to 0.
[0055] As an example of an embodiment of the present invention, the calculation based on the characteristic parameters, the operating parameters, and the power flow parameters to obtain a first weighting factor for characterizing the conversion relationship of the same resource capacity in each of the electricity markets is specifically as follows: For each of the aforementioned electricity markets, obtain the corresponding preset adjustment parameters and preset conversion factors; The evaluation value corresponding to the electricity market is calculated based on the characteristic parameters, and the emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow. The first weighting factor corresponding to the electricity market is obtained by calculating the preset adjustment parameters, preset conversion coefficients, the evaluation value, and the emergency coefficient.
[0056] In this embodiment, the first weighting factor, i.e., the alternative weighting factor This factor quantifies the intensity of competition and substitution when the capacity of the same physical resource is allocated across different markets (such as the energy market and various non-energy markets). Using the unit capacity occupancy in the energy market as a benchmark (i.e., 1MW of physical capacity used for power generation occupies 1MW of physical capacity), this factor calculates the equivalent capacity occupancy amplification factor corresponding to the allocation of a unit of capacity to other service markets. Essentially, the first weighting factor prioritizes the service capacity of high-performance resources by introducing differentiated costs of capacity occupancy, preventing them from being squeezed out by other transactions such as the energy market, thereby ensuring that the system's capacity requirements for critical services are met. It reflects the equivalent energy market capacity that must be forsaken when a resource allocates 1 unit of physical capacity to a specific service market under the current system state; it is a dimensionless conversion factor. Combined with the objective function of minimizing total cost, the model weighs whether to allocate capacity to the service market with the higher bid or for other markets during the optimization process. Since the equivalent occupancy cost of the service market may be higher, the model will only allocate the necessary capacity when its demand constraints (hard constraints) must be met. This mechanism, combined with hard constraints, ensures that sufficient system capacity is reserved for critical services, preventing the crowding out of critical service capacity due to short-term interests in the energy market or other markets.
[0057] First weighting factor The formula for calculation is: ; in, It is a static benchmark conversion factor for resource p (the set of resources i and j) and non-energy market services m, and is generally taken as 1.0; It is an adjustment parameter greater than 0, used to control the emergency factor. Sensitivity, for example, when the urgency factor High, and resource p performs excellently in the service market m ( When it is high, its Reaching 1.3 means that in the joint clearing model, reserving 1MW of capacity for market services for this unit will be considered as occupying 1.3MW of its physical capacity space.
[0058] It should be noted that in this embodiment, the substitution weighting factor is only calculated for resources i and j in the non-energy market. The energy market is the benchmark, and the substitution weighting factor of resources for the energy market is 1. The non-energy market includes the frequency regulation market, the reserve market, and the emergency market.
[0059] As an example of an embodiment of the present invention, the second weighting factor calculated based on the operating parameters and the power flow parameters to characterize the equivalent impact of the same resource allocation on the physical constraints of the power grid is specifically as follows: For each of the power transmission sections, obtain the corresponding preset power transmission distribution factor; The congestion coefficient corresponding to each transmission section is calculated based on the power flow parameters. The power transmission distribution factor, the congestion coefficient, and the evaluation value are calculated to obtain the second weighting factor corresponding to the electricity market.
[0060] In this embodiment, the second weighting factor, namely the complementarity weighting factor... This is used to quantify the actual contribution of a market service to alleviating overload safety constraints on a specific power grid section. Traditional safety constraints only consider the impact of generator output on power flow and use a fixed power transmission distribution factor (PTDF). As a coefficient, this coefficient only reflects the static physical impact of the resource's geographical location, that is, the degree of fixed impact of the resource's power variation on the power flow of the target transmission section. Based on this, this scheme dynamically corrects the above static impact coefficient by introducing the resource's performance score and the section's congestion status, thus obtaining the second weighting factor, calculated as follows: ; in, It is PTDF, which represents the inherent power flow influence potential of the node where resource i is located on the critical section k; This is used to correct static physical potential to effective potential. For example, when the resource's response time (5 minutes) is greater than the warning time for cross-section overrun (2 minutes), its actual effectiveness approaches zero, and the corresponding weighting factor also approaches zero, expressed as Score≈0. Therefore, its effective weight approaches 0. It is the congestion coefficient of transmission section k, and the calculation formula is expressed as follows: .
[0061] It should be noted that the calculated... It may be significantly higher or lower than the static PTDF, reflecting the real, equivalent impact of calling the specific service provided by the resource in the current system state on solving a specific security problem.
[0062] Furthermore, the calculated weighting factors are organized into a block matrix W according to a predetermined structure. The rows of the matrix correspond to different physical or rule constraints, primarily including capacity coupling constraints for each resource and safety coupling constraints for each critical section. The columns of the matrix correspond to different market decision variables, primarily including the output of each resource in the energy market. Reserved capacity for various resources in various ancillary service markets ; and the reserved capacity of each resource in the emergency response service market. The matrix elements are the dynamic weighting factors that fill the corresponding positions: at the intersection of the capacity coupling constraint row and the non-energy market variable column, the corresponding weighting factors are filled in. Fill in 1 at the intersection of the capacity coupling constraint row and the energy market variable column. Fill in the corresponding value at the intersection of the security coupling constraint row and all market variable columns that can affect power flow (including energy, ancillary services, and preventative services). For energy market variables, the coefficient is the static PTDF.
[0063] Example: Assuming that unit G1 participates in the energy, frequency regulation market, and preventative services, considering the capacity constraints of G1 and the safety constraints of section L1, the corresponding part of matrix W is shown in Table 1 below: Table 1 Weight Matrix In practical applications, rows can be increased or decreased based on the number of resources and cross-sections, and columns can be increased or decreased based on the number of service markets.
[0064] As an example of an embodiment of the present invention, the electricity market includes a response market, a frequency regulation market, and a reserve market; the characteristic parameters include a first response time in the response market, a frequency regulation rate in the frequency regulation market, and a second response time in the reserve market; The calculation of the assessment value corresponding to the electricity market based on the characteristic parameters is specifically as follows: Obtain historical adjustment coefficients, historical call success rates, and frequency modulation rate weighting factors; Based on the first response time and the preset first response parameters, the first evaluation value of the market participant in the response market is calculated; The second evaluation value of the market participant in the frequency modulation market is obtained by calculating based on the frequency modulation rate weighting factor, the historical adjustment coefficient, the frequency modulation rate, and the preset frequency modulation rate threshold. The third evaluation value of the market participant in the standby market is calculated based on the historical call success rate, the second response time, and the preset second response parameters.
[0065] As an example of an embodiment of the present invention, the calculation of the first evaluation value of the market participant in the response market based on the first response time and a preset first response parameter specifically includes: Obtain the preset response threshold; When the first response time is greater than the response threshold, the first evaluation value is determined to be zero. When the first response time is less than or equal to the response threshold, the first response time and the first response parameter are calculated according to the exponential decay function to obtain the first evaluation value.
[0066] In this embodiment, the adaptability of the same resource varies when providing services in different non-energy service markets, allowing for performance scoring for different markets. It should be noted that the physical attributes of the energy output of each resource in the energy market are identical at the time of delivery; its performance primarily refers to its schedulability, reflected by its declared capacity limits and ramp-up rate. Therefore, the energy market score is not calculated; the first evaluation value for the emergency response service market is used instead. The calculation formula is expressed as: ; in, It is the first response time. It is the maximum permissible response time stipulated by the emergency market, i.e., the response threshold. This is the benchmark reference time constant, i.e., the first response parameter. The first response time is a core indicator for evaluating emergency response services. This formula, through an exponential decay function, non-linearly amplifies the value of differences in response speed, incentivizing market players to invest in truly rapid response technologies.
[0067] Second assessment value of the FM service market The calculation formula is expressed as: ; in, The actual frequency modulation rate of resource i. The optimal adjustment rate benchmark, i.e., the frequency modulation rate threshold, can be taken as the upper quartile of the adjustment rates of all frequency modulation resources in the same period or a fixed value. This is the historical adjustment accuracy coefficient for resource i, i.e., the historical adjustment coefficient, ranging from [0,1], where 1 indicates completely accurate following of the instruction. This is the frequency modulation rate weighting factor. The total mileage of the frequency modulation signal refers to the percentage change indicated by all frequency modulation commands issued by the dispatch center during the assessment period. For example, if the command changes from 0 (no adjustment requirement) → 1 (full-scale adjustment command) → -1 (full-scale reduction adjustment command) → 0 → 1, the total mileage is |1-0| + |-1-1| + |0-(-1)| + |1-0| = 5, which is 500%. The actual power change provided corresponds to this. This formula combines rate and accuracy, avoiding unreasonable scoring for resources that are fast but inaccurate or accurate but not fast, and more realistically reflects their comprehensive contribution to the dynamic recovery of the system frequency.
[0068] The third assessment value of the standby service market is calculated using the following formula: ; in, The second response time for resource i is specifically the time from the issuance of the call command to the achievement of rated output. This serves as the backup service reference time constant, i.e., the second response parameter. This represents the historical success rate of resource i over a certain period. For new resources, a default value of 0.95 can be used. This formula multiplies the speed factor (exponential term) by the reliability factor (ρ), emphasizing that both speed and reliability are essential for high-quality backup resources. For new auxiliary services such as inertial / rapid ramping, their performance scores are defined by specific performance parameters based on the service's technical characteristics. These specific performance parameters are then normalized by dividing them by the corresponding set value, or by the maximum value of the specific performance parameters of all resources in the new auxiliary service, to obtain the corresponding performance score.
[0069] As an example of an embodiment of the present invention, the operating parameters include load change rate, frequency deviation value and reserve capacity; The emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow, specifically as follows: Obtain the preset power grid capacity threshold; Determine the maximum value among all the power flow parameters, and set the maximum value as the first urgency coefficient of the response market; The load change rate and the frequency deviation value are normalized, and the normalized load change rate and the normalized frequency deviation value are weighted and summed to obtain the second emergency coefficient corresponding to the frequency regulation market. The ratio between the reserve capacity and the grid capacity threshold is calculated, and the third emergency coefficient corresponding to the reserve market is determined based on the ratio.
[0070] In this embodiment, the urgency of the energy market is reflected by the clearing price, eliminating the need for an additional urgency coefficient. Therefore, the urgency coefficient in this embodiment is based on the non-energy market. For the second urgency coefficient of the frequency regulation service market... In each clearing cycle, the net load change rate and frequency deviation obtained from the energy management system (EMS) are normalized and then weighted and summed to obtain the result. This is a value greater than 0; the larger the value, the more urgent the instantaneous demand for frequency regulation in the system. The net load change rate is the short-term change rate of the system's net load (i.e., total load minus renewable output), and the frequency deviation is the normalized result of dividing the system frequency deviation by the allowable frequency deviation limit. When power generation exceeds the load, the system frequency rises; when it falls below, the frequency drops. The net load change rate directly determines the rate of change of the system frequency; the greater the fluctuation, the greater the rate of frequency change, and the greater the need for frequency regulation services. This data belongs to grid status data and is not data reported by participants.
[0071] The third emergency factor in the standby service market It is negatively correlated with the system's reserve capacity margin, specifically calculated as the ratio difference between the system's currently available total spinning reserve capacity and the system's specified grid capacity threshold. The formula is: ; That is, when available reserves are sufficient (available reserves ≥ grid capacity threshold), Urgency_res = 0, indicating no urgency. When reserves are insufficient, this value increases linearly between 0 and 1, and the more severe the shortage, the closer it gets to 1.
[0072] The first emergency factor for the emergency response services market Considering that emergency response services are primarily used to mitigate foreseeable network congestion or stability risks, their urgency is positively correlated with the probability of exceeding the most severe potential security constraints, calculated as follows: ,in, This is a set of key transmission sections of the power grid.
[0073] Monitor all predefined critical transmission sections, select the one whose current or predicted occupancy rate is closest to the limit, and use its occupancy rate directly as the threshold. This can directly and effectively quantify the overall urgency of the system in alleviating network congestion.
[0074] like Figure 2 As shown, based on the above method embodiments, an embodiment of the present invention provides a resource scheduling system 200 based on dynamic coupling weights, including: an acquisition module 201, a weight module 202 and a scheduling module 203; The acquisition module 201 is used to acquire characteristic parameters and demand parameters corresponding to each electricity market for each market participant, and to acquire the operating parameters of the power grid and the power flow parameters of the transmission section. The weighting module 202 is used to calculate, based on the characteristic parameters, the operating parameters and the power flow parameters, a first weighting factor to characterize the conversion relationship of the same resource capacity in each of the electricity markets, and to calculate, based on the operating parameters and the power flow parameters, a second weighting factor to characterize the equivalent impact relationship of the same resource call on the physical constraints of the power grid. The scheduling module 203 is used to determine constraints based on the first weighting factor and the second weighting factor corresponding to each market participant, and to perform optimization under the constraints with the objective of minimizing the cost of each market participant in each electricity market, thereby obtaining a resource scheduling scheme and controlling each market participant to execute the resource scheduling scheme.
[0075] It is understood that the above system item embodiments correspond to the method item embodiments of the present invention, and can implement the resource scheduling method based on dynamic coupling weight provided by any of the above method item embodiments of the present invention.
[0076] It should be noted that the system embodiments described above are merely illustrative, and 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 system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0077] For ease of description and brevity, the system embodiments of the present invention include all the implementation methods described in the above embodiments of the resource scheduling method based on dynamic coupling weights, and will not be repeated here.
[0078] Based on the above embodiments of the resource scheduling method based on dynamic coupling weights, another embodiment of the present invention provides a terminal device, which includes 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 resource scheduling method based on dynamic coupling weights of any embodiment of the present invention.
[0079] 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 terminal device.
[0080] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0081] 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. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0082] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the resource scheduling method based on dynamic coupling weights as described in any of the above-described method embodiments of the present invention.
[0083] Based on the above-described method embodiments, this invention also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of any of the above-described method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0084] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention 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, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0085] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles 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 resource scheduling method based on dynamic coupling weights, characterized in that, include: For each market participant, obtain the characteristic parameters and demand parameters corresponding to each electricity market, and obtain the grid operation parameters and power flow parameters of the transmission sections; Based on the characteristic parameters, the operating parameters, and the power flow parameters, a first weighting factor is obtained to characterize the conversion relationship of the same resource capacity in each of the electricity markets. Based on the operating parameters and the power flow parameters, a second weighting factor is obtained to characterize the equivalent impact relationship of the same resource allocation on the physical constraints of the power grid. Constraints are determined based on the first weighting factor and the second weighting factor corresponding to each market participant. Under these constraints, optimization is performed with the objective of minimizing the cost of each market participant in each electricity market to obtain a resource scheduling scheme. Each market participant is then controlled to execute the resource scheduling scheme.
2. The resource scheduling method based on dynamic coupling weights as described in claim 1, characterized in that, The calculation based on the characteristic parameters, operating parameters, and power flow parameters yields a first weighting factor characterizing the conversion relationship of the same resource capacity in each of the electricity markets, specifically: For each of the aforementioned electricity markets, obtain the corresponding preset adjustment parameters and preset conversion factors; The evaluation value corresponding to the electricity market is calculated based on the characteristic parameters, and the emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow. The first weighting factor corresponding to the electricity market is obtained by calculating the preset adjustment parameters, preset conversion coefficients, the evaluation value, and the emergency coefficient.
3. The resource scheduling method based on dynamic coupling weights as described in claim 2, characterized in that, The calculation based on the operating parameters and the power flow parameters yields a second weighting factor that characterizes the equivalent impact of the same resource allocation on the physical constraints of the power grid. Specifically: For each of the power transmission sections, obtain the corresponding preset power transmission distribution factor; The congestion coefficient corresponding to each transmission section is calculated based on the power flow parameters. The power transmission distribution factor, the congestion coefficient, and the evaluation value are calculated to obtain the second weighting factor corresponding to the electricity market.
4. The resource scheduling method based on dynamic coupling weights as described in claim 2, characterized in that, in, The electricity market includes a response market, a frequency regulation market, and a reserve market; the characteristic parameters include a first response time in the response market, a frequency regulation rate in the frequency regulation market, and a second response time in the reserve market. The calculation of the assessment value corresponding to the electricity market based on the characteristic parameters is specifically as follows: Obtain historical adjustment coefficients, historical call success rates, and frequency modulation rate weighting factors; Based on the first response time and the preset first response parameters, the first evaluation value of the market participant in the response market is calculated; The second evaluation value of the market participant in the frequency modulation market is obtained by calculating based on the frequency modulation rate weighting factor, the historical adjustment coefficient, the frequency modulation rate, and the preset frequency modulation rate threshold. The third evaluation value of the market participant in the standby market is calculated based on the historical call success rate, the second response time, and the preset second response parameters.
5. The resource scheduling method based on dynamic coupling weights as described in claim 4, characterized in that, The calculation of the market participant's first evaluation value in the response market based on the first response time and preset first response parameters is specifically as follows: Obtain the preset response threshold; When the first response time is greater than the response threshold, the first evaluation value is determined to be zero. When the first response time is less than or equal to the response threshold, the first response time and the first response parameter are calculated according to the exponential decay function to obtain the first evaluation value.
6. The resource scheduling method based on dynamic coupling weights as described in claim 4, characterized in that, in, The operating parameters include load change rate, frequency deviation value, and reserve capacity; The emergency coefficient corresponding to the electricity market is calculated based on the operating parameters and the power flow, specifically as follows: Obtain the preset power grid capacity threshold; Determine the maximum value among all the power flow parameters, and set the maximum value as the first urgency coefficient of the response market; The load change rate and the frequency deviation value are normalized, and the normalized load change rate and the normalized frequency deviation value are weighted and summed to obtain the second emergency coefficient corresponding to the frequency regulation market. The ratio between the reserve capacity and the grid capacity threshold is calculated, and the third emergency coefficient corresponding to the reserve market is determined based on the ratio.
7. The resource scheduling method based on dynamic coupling weights as described in claim 1, characterized in that, in, The constraints include a first constraint and a second constraint. The determination of constraints based on the first weighting factor and the second weighting factor corresponding to each market participant specifically includes: For each market participant, the resource dispatch value corresponding to each electricity market is weighted and summed with the first weighting factor to obtain the capacity value corresponding to the market participant. The first constraint condition is to constrain the capacity value to be less than or equal to the preset market capacity threshold corresponding to the market participant. For each transmission section, the resource scheduling value of each market participant in each electricity market is weighted and summed with the second weighting factor to obtain the power flow value corresponding to the transmission section. The power flow value is constrained to be less than or equal to the preset transmission threshold corresponding to the transmission section as the second constraint condition.
8. A resource scheduling system based on dynamic coupling weights, characterized in that, include: The module consists of an acquisition module, a weighting module, and a scheduling module. The acquisition module is used to acquire characteristic parameters and demand parameters corresponding to each electricity market for each market participant, and to acquire the operating parameters of the power grid and the power flow parameters of the transmission section. The weighting module is used to calculate, based on the characteristic parameters, the operating parameters, and the power flow parameters, a first weighting factor to characterize the conversion relationship of the same resource capacity in each of the electricity markets, and to calculate, based on the operating parameters and the power flow parameters, a second weighting factor to characterize the equivalent impact relationship of the same resource call on the physical constraints of the power grid. The scheduling module is used to determine constraints based on the first weighting factor and the second weighting factor corresponding to each market participant, and to perform optimization under the constraints with the objective of minimizing the cost of each market participant in each electricity market, thereby obtaining a resource scheduling scheme and controlling each market participant to execute the resource scheduling scheme.
9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the resource scheduling method based on dynamic coupling weights as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the resource scheduling method based on dynamic coupling weights as described in any one of claims 1-7.