An optimized scheduling method, system, device, medium and product

Abstract

The application discloses an optimal scheduling method, system, device, medium and product, and belongs to the technical field of power grid scheduling. The method is as follows: determining an initial feasible region based on the operation constraint condition of each flexible resource in an aggregation; mapping the initial feasible region to an active power plane to obtain a target feasible region based on the corresponding relationship between each resource scheduling scheme and the total output power of the aggregation; determining the adjustable power threshold and the power change rate threshold of the aggregation based on the target feasible region; and inputting the real-time scheduling instruction of the power grid into a scheduling optimization model to obtain the target scheduling scheme of each flexible resource under the constraints of the target feasible region and the power change rate threshold. Therefore, the application can improve the executable rate of the scheduling scheme and the safety of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to an optimized dispatching method, system, equipment, medium, and product. Background Technology

[0002] Flexible resource aggregators integrate various distributed resources such as distributed photovoltaics, energy storage, and electric vehicles to provide unified adjustable power and participate in grid dispatch. In actual operation, the aggregator needs to quickly generate specific dispatch plans for each internal resource based on real-time dispatch instructions issued by the grid, and ensure that the plan is physically feasible and economically reasonable.

[0003] Currently, a common optimization scheduling method involves pre-establishing an overall power regulation model for a flexible resource aggregate, defining the aggregate's power output range and typical operating parameters of its internal resources, and then using a mathematical programming algorithm to solve for the scheduling instructions of each internal resource with the goal of optimal economy. This method typically sets the aggregate's power regulation range to a fixed value or a static interval based on historical data, while the operational constraints of internal resources are directly incorporated into the optimization model in the form of conventional power upper and lower limits, power change rates, etc.

[0004] However, the actual external adjustment capability of an aggregate changes dynamically with the state of its internal resources. Static or fixed-boundary adjustment models cannot accurately reflect this time-varying characteristic, which may cause the scheduling scheme obtained by optimization to exceed the actual physical limits of the aggregate during actual execution, resulting in scheduling instructions being infeasible or execution deviations. Summary of the Invention

[0005] This invention provides an optimized scheduling method, system, device, medium, and product that can solve the problem that existing scheduling optimization models based on static boundaries cannot accurately reflect the time-varying adjustment capabilities of aggregates, leading to scheduling instructions exceeding physical limits and becoming infeasible.

[0006] This invention provides an optimized scheduling method, comprising: Based on the operational constraints of each flexible resource in the flexible resource aggregate, an initial feasible domain including multiple resource scheduling schemes is determined. Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate. Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate; The system acquires real-time dispatch instructions from the power grid, performs physical feasibility checks on these instructions based on adjustable power thresholds and power change rate thresholds, and inputs the real-time dispatch instructions into a preset dispatch optimization model if the checks pass. Under the constraints of the target feasible region and the power change rate threshold, the system solves the dispatch optimization model to obtain dispatch schemes for each flexible resource.

[0007] This invention constructs an initial feasible region based on the operational constraints of each flexible resource and maps it to the active power plane to obtain the target feasible region. This improves the accuracy of characterizing the time-varying regulation capability of the flexible resource aggregate, enabling the power regulation boundary of the aggregate to be dynamically updated with the operating state. Based on the target feasible region, adjustable power thresholds and power change rate thresholds are determined and introduced as constraints in the scheduling optimization model. This improves the matching degree between the scheduling scheme and the actual physical regulation capability of the aggregate, thereby increasing the executability and real-time response reliability of the scheduling scheme, and ultimately enhancing grid security. Furthermore, before inputting real-time scheduling commands into the scheduling optimization model, the physical feasibility of the commands is checked based on the adjustable power threshold and power change rate threshold. This improves the pre-screening accuracy of the scheduling commands, ensuring that all commands entering the optimization solution stage are within the physical regulation capability range of the aggregate, thus improving the security and stability of the real-time execution of the scheduling scheme.

[0008] Furthermore, the output of the scheduling optimization model also includes total revenue; After obtaining the scheduling schemes for each flexible resource, the following is also included: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0009] Shapley value is a solution concept in cooperative game theory, used to fairly distribute the benefits based on each participant's marginal contribution to the total benefit of the alliance.

[0010] Based on the adjustment power, safety enhancement, and carbon emission reduction of each flexible resource, the economic marginal contribution, safety additional contribution, and environmental additional contribution are calculated respectively. The total revenue is allocated using the target Shapley value method, which improves the matching degree between the revenue allocation result and the comprehensive contribution of flexible resources. This allows the three-dimensional value of economy, safety, and environmental protection to be reasonably quantified, thereby improving the fairness of revenue allocation and the enthusiasm of each resource to participate in scheduling.

[0011] Furthermore, based on the marginal economic contribution, the additional contribution to safety, and the additional contribution to environmental protection, the total revenue is allocated to obtain the revenue distribution plan, as follows: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0012] This approach determines the initial allocation scheme based on the marginal economic contribution, and obtains an improvement factor by weighting and summing the additional contributions to safety and environmental protection according to a preset weighted coefficient group. The initial allocation scheme is then modified based on the improvement factor, which enhances the incentive effect of the revenue distribution scheme on non-economic contributions. This allows resources with excellent safety and environmental performance to receive corresponding returns, thereby increasing the willingness of flexible resources to proactively optimize in terms of safety regulation and low-carbon operation.

[0013] Furthermore, after obtaining the profit distribution plan, it also includes: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0014] In this way, in the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme, and the priority of the resource is input into the scheduling optimization model along with the next scheduling instruction. This improves the continuity and coordination efficiency of resource calls in multi-cycle scheduling, and enables historical scheduling performance to positively guide the optimal allocation of subsequent resources.

[0015] Furthermore, based on the target feasible region, the adjustable power threshold and power change rate threshold of the flexible resource aggregate are determined, specifically as follows: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0016] In this way, within the target feasible region, the day-ahead power baseline is solved with the goal of minimizing the total day-ahead operating cost; and with the goal of maximizing the adjustable power range and the power change rate, the adjustable power threshold and the power change rate threshold are solved based on the power baseline, which improves the economy of the threshold setting results and the utilization rate of the aggregate's adjustment capability. While ensuring the optimal day-ahead operating cost, the power adjustment margin of the aggregate is maximized.

[0017] Furthermore, under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved, specifically as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0018] This approach, using the maximization of the total revenue of the flexible resource aggregate as the objective function, solves the problem under the joint constraints of mutual exclusion of power purchase and sale, internal power balance constraints, energy storage battery state of charge constraints, generator power constraints, external dispatch command matching constraints, target feasible region, and power change rate threshold. This improves the optimality of the dispatch scheme under multiple physical constraints and market rules, thereby enhancing the overall economic benefits and operational safety of the flexible resource aggregate.

[0019] Another embodiment of the present invention provides an optimized scheduling system, including: a feasible region construction module, a mapping module, a threshold determination module, and a scheduling solution module; The feasible region construction module is used to determine the initial feasible region, which includes multiple resource scheduling schemes, based on the operational constraints of each flexible resource in the flexible resource aggregate. The mapping module is used to map the initial feasible region to the active power plane based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, so as to obtain the target feasible region of the flexible resource aggregate. The threshold determination module is used to determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate based on the target feasible region. The scheduling solution module is used to obtain real-time scheduling instructions from the power grid. Based on the adjustable power threshold and the power change rate threshold, the real-time scheduling instructions are physically verified. If the verification is successful, the real-time scheduling instructions are input into the preset scheduling optimization model. Under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved to obtain the scheduling schemes for each flexible resource.

[0020] The feasible region construction module of this invention determines the initial feasible region based on the operational constraints of each flexible resource. The mapping module maps the initial feasible region to the active power plane to obtain the target feasible region, improving the efficiency and accuracy of characterizing the time-varying regulation capability of the flexible resource aggregate. This allows the power regulation boundary of the aggregate under multi-resource coupling conditions to be intuitively and accurately reflected on the active power plane. The threshold determination module determines the adjustable power threshold and power change rate threshold based on the target feasible region. The scheduling solution module inputs real-time scheduling instructions into the scheduling optimization model and solves the problem under the constraints of the feasible region and rate threshold. This improves the matching degree between the scheduling scheme and the actual physical regulation capability of the flexible resource aggregate, ensuring that the generated scheduling instructions are always within the power regulation margin and change rate limit of the aggregate, thereby improving the executability of the scheduling scheme and the reliability of real-time response. Overall, the system decouples and coordinates the functions of feasible region construction, power plane mapping, threshold determination, and optimization solution through a modular architecture. This improves the independent iteration capability and overall coordination efficiency of each functional module, facilitating module-level optimization and maintenance for different power grid operation scenarios, thereby improving the system's scalability and engineering applicability.

[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 optimized scheduling method 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 steps as described in the optimized scheduling method of the present invention.

[0023] Another embodiment of the present invention also provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the optimized scheduling method of the present invention. Attached Figure Description

[0024] 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.

[0025] Figure 1 This is a flowchart illustrating an optimized scheduling method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the active power operating feasible domain of a flexible resource aggregate provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a multi-target recognition optimization model structure provided in an embodiment of the present invention; Figure 4 This is a comparison chart of basic parameters of internal flexibility resources of an aggregate provided by an embodiment of the present invention; Figure 5 This is an embodiment of the present invention providing a polymer day-ahead power baseline and dynamically adjustable boundary diagram; Figure 6 This is a graph showing the upward / downward climbing rate of an aggregate, provided in an embodiment of the present invention. Figure 7 This is a typical resource decomposition result diagram of an aggregate at a specific adjustment time, provided by an embodiment of the present invention. Figure 8 This is a comparison chart of the revenue distribution between the traditional Shapley value and the target Shapley value provided by an embodiment of the present invention; Figure 9 This is a diagram illustrating a closed-loop collaborative control mechanism for a flexible resource aggregator provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of an optimized scheduling system provided in an embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] See Figure 1To address the problem that existing scheduling optimization models based on static boundaries cannot accurately reflect the time-varying adjustment capabilities of aggregates, leading to scheduling instructions exceeding physical limits and becoming infeasible, an embodiment of the present invention provides an optimized scheduling method, comprising: Step 101: Based on the operational constraints of each flexible resource in the flexible resource aggregate, determine the initial feasible domain, which includes multiple resource scheduling schemes.

[0034] The flexible resource aggregate refers to a virtual entity that integrates various flexible resources with adjustment capabilities, such as distributed photovoltaics, distributed wind power, distributed energy storage, electric vehicle clusters, and adjustable building air conditioning, through a communication network and a unified control system, and can present its overall adjustment capabilities to the outside world. The initial feasible domain is the set of all possible combinations of the output values ​​of all flexible resources (such as photovoltaics, wind power, energy storage, electric vehicles, etc.), and any combination in the set is a resource scheduling scheme.

[0035] In this step, the flexible resource aggregate participates in electricity market transactions or receives grid dispatch instructions as a whole. The active power output of the aggregate can be adjusted within a certain range. The magnitude of the adjustment capability is jointly affected by the physical constraints of each internal resource (such as upper and lower limits of output, state of charge of energy storage, ramp rate, etc.) and environmental factors (such as sunlight, wind speed), exhibiting significant time-varying characteristics. Theoretically, given the overall output power of the aggregate, it is feasible to decompose it into output schemes of its internal flexible resources. That is, if the total output power of the aggregate is known (e.g., 300kW), theoretically, a set of output allocation schemes for internal resources can be found (e.g., 50kW photovoltaic, 30kW wind power, 35kW energy storage discharge, 15kW electric vehicle, 10kW building air conditioning savings, etc.) such that the sum of the outputs of these resources is exactly equal to 300kW, and the output of each resource is within its own operational constraints. Based on this, when the overall output power of the aggregate is... At that time, its corresponding internal flexible resource scheduling scheme can be represented as a matrix. In this matrix P, each element represents the active power value of a certain flexibility resource at different time periods. Each flexibility resource has corresponding operational constraints, which constitute the feasible region of operation for that flexibility resource. All feasible regions constitute a finite region within the power space, namely the initial feasible region. Since the dimensions of the operational constraints of different flexibility resources differ, the initial feasible region can be represented as a high-dimensional space. As shown below: ; In the formula, the constraint matrix and constraint vector This includes the following three types of constraints with explicit physical meaning: (1) Power upper and lower limit constraints, which stipulate that the output of each flexibility resource at any time cannot exceed the upper and lower limits determined by its physical structure or operating procedures, expressed as: ; in, and Resources The minimum and maximum active power, in kW; specifically, for energy storage systems, This represents the maximum charging power (with the charging and discharging direction defined as positive charging and negative discharging). Furthermore, any flexibility resource has the following under this constraint: , .

[0036] (2) Energy timing coupling constraint for energy storage devices, which describes the dynamic process of the evolution of electrical (or thermal) energy over time in distributed energy storage and building air conditioning energy storage (thermal storage). For any energy storage device, its energy state must satisfy: ; in, for Energy storage capacity at any given time, expressed in kWh; and For charging and discharging power (meeting) ); , For charging and discharging efficiency; , Minimum and maximum power limits; obviously there are .

[0037] (3) Climbing rate constraint, which characterizes the dynamic response limit of a flexible resource when switching from one operating state to another. For any flexible resource, the output change between adjacent time steps must satisfy: ; in, and Resources Maximum upward and downward climbing rates.

[0038] Step 102: Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate.

[0039] In this step, based on the internal flexible resource scheduling scheme set By calculating the power flow, the overall output power of the polymer can be determined. Therefore, there is a definite mapping relationship between the two, expressed as Based on this mapping relationship, the target feasible region of the aggregate can be defined as: the high-dimensional space of the set of internal flexible resource scheduling schemes. The projection onto the power plane of the aggregate. In layman's terms, it's the projection of higher-dimensional space... All possible internal solutions, through mapping Convert these values ​​into corresponding total power values, and the set of these total power values ​​constitutes the target feasible region of the aggregate.

[0040] like Figure 2 The figure shows a schematic diagram of the feasible region of active power operation for a flexible resource aggregate according to an embodiment of the present invention. As shown in the figure, this embodiment mainly focuses on the feasible region of active power; therefore, the above projection process is focused on the active power dimension. Specifically, the high-dimensional space... The initial feasible region is projected onto a one-dimensional active power plane through the mapping relationship between the internal resource scheduling scheme and the total output power of the aggregate. The resulting plane is the active power operating feasible region of the aggregate. The feasible region of the target is represented as: : In other words, the target feasible region is the set of total output power corresponding to all internal scheduling schemes in the high-dimensional initial feasible region. Figure 2 In the high-dimensional space, each point represents a feasible internal resource scheduling scheme. After mapping, it corresponds to a point on the active power plane. The set of all image points constitutes the active power operation feasible region as shown in the figure. The obtained target feasible region will be used as the dynamic physical boundary to provide a constraint basis for subsequent multi-agent collaborative optimization.

[0041] Step 103: Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate.

[0042] It should be noted that the active power operational feasible region defines all possible total output ranges of the aggregate, but it is only a power set and does not yet provide the specific characteristic parameters required for grid dispatch. The process of solving the operational feasible region of the largest aggregate is the process of determining the external characteristic parameters. In this step, the external characteristic parameters refer to the regulation capability parameters presented by the flexibility resource aggregate as a whole to the external power grid, including the adjustable power threshold and the power change rate threshold.

[0043] The adjustable power threshold includes upward adjustable power and downward adjustable power, which represent the maximum power value that the polymer can increase or decrease relative to the power baseline, respectively; the power change rate threshold includes upward ramp rate and downward ramp rate, which represent the maximum power change rate that the polymer can increase or decrease relative to the power baseline per unit time, respectively.

[0044] In this step, a multi-objective identification and optimization model is established and solved to obtain the external characteristic parameters of the flexible resource aggregate. This model uses the target feasible region as the constraint space and, by setting different optimization objectives, solves for the adjustable power threshold and the power change rate threshold. For example, maximizing the adjustable range as the objective function determines the adjustable power threshold, while maximizing the ramp rate as the objective function determines the power change rate threshold. These external characteristic parameters, together with the target feasible region, constitute the dynamic physical boundary of the aggregate's participation in grid dispatch, providing a constraint basis for subsequent steps.

[0045] Step 104: Obtain the real-time dispatch instructions from the power grid. Perform a physical feasibility check on the real-time dispatch instructions based on the adjustable power threshold and the power change rate threshold. If the check passes, input the real-time dispatch instructions into the preset dispatch optimization model. Under the constraints of the target feasible region and the power change rate threshold, solve the dispatch optimization model to obtain the dispatch schemes for each flexible resource.

[0046] Among them, the target feasible region constraint means that the total output power of the aggregate must be within the target feasible region; the power change rate threshold constraint means that the rate of change of the total output power of the aggregate must not exceed the power change rate threshold (i.e., the up / down ramp rate).

[0047] In this step, let the polymer be at time... Upon receiving a real-time dispatch instruction from the power grid, requiring it to [complete the task] within a specified time... Internally, the overall output will be adjusted from the current planned value. The instruction is used as an equality constraint to construct an optimization model. This model can be a mixed-integer linear programming model. Under the objective function of maximizing the total revenue of the flexible resource aggregate, and constrained by the objective feasible region and the power change rate threshold, the active power decision variables of each flexible resource in each time period are solved using the branch and bound method or the simplex method to obtain the scheduling scheme of each flexible resource. The scheduling scheme can be a set of specific internal resource optimal scheduling instructions. .

[0048] Before inputting real-time scheduling instructions into the preset scheduling optimization model, a verification step is included. Specifically, the physical feasibility of the real-time scheduling instructions is verified based on adjustable power thresholds and power change rate thresholds to check whether the instructions meet the constraints of the aforementioned external characteristic parameters. This includes two aspects: the first aspect is the power range constraint, expressed as... The second aspect is the power change rate constraint, expressed as... Only after an instruction passes the above checks and is confirmed to be physically feasible will it proceed to the subsequent optimization and decomposition process. This ensures that scheduling instructions always remain within the safe operating boundaries of the aggregate.

[0049] It should be noted that the above two constraints are the core dynamic boundary constraints that distinguish this invention from existing technologies, used to ensure that the scheduling scheme does not exceed the actual physical regulation capacity of the aggregate. In addition, based on the physical characteristics and operational requirements of the actual power system, this model may also include other conventional constraints, such as power balance constraints, upper and lower limits of output of each resource, energy storage state of charge constraints, ramp rate constraints, etc.

[0050] As an example of an embodiment of the present invention, the output of the scheduling optimization model also includes total revenue; After obtaining the scheduling schemes for each flexible resource, the following is also included: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0051] Shapley value is a solution concept in cooperative game theory, used to fairly distribute the benefits based on each participant's marginal contribution to the total benefit of the alliance.

[0052] In this embodiment, solving the scheduling optimization model yields the scheduling scheme; and the estimated total alliance revenue under this optimal scheduling scheme is also obtained. In the scheduling scheme, the multi-dimensional contribution indicators of each flexible resource in this collaborative optimization scheduling are read, such as the adjustment power capacity provided by each flexible resource. Based on the dispatch scheme, the contribution of each flexible resource to system security (including node voltage stability improvement, line congestion relief, etc.) is determined through power system flow calculation or sensitivity analysis, and the security improvement is obtained. Based on the dispatch scheme, the carbon emission reduction brought by each flexible resource is calculated according to the situation of each flexible resource replacing traditional thermal power units and combined with the carbon emission factor.

[0053] After obtaining the scheduling scheme and total revenue of the flexible resource aggregation, this embodiment uses the Shapley value method to fairly distribute the total revenue in order to incentivize the active participation of each flexible resource in collaboration and ensure the long-term stable operation of the aggregation. Furthermore, while the traditional Shapley value method can reflect the marginal contribution of each flexible resource in solving the revenue distribution problem of the flexible resource aggregation, it has a drawback: insufficient consideration of safety and environmental protection. This application designs an improved Shapley value method based on the traditional method, taking into account factors such as safety and environmental protection, with the aim of making the revenue distribution more fair and reasonable.

[0054] Before calculation, a cooperative game theory model needs to be established. This model describes the payoffs of various flexibility resources and their formation in different alliances, providing a mathematical foundation for subsequent marginal contribution calculations and Shapley value solutions. Specifically, cooperative game theory studies the interactive scenarios in which flexibility resources can form binding agreements and establish alliances. Unlike non-cooperative game theory solutions, the cooperation between different alliances in cooperative game theory primarily emphasizes how to allocate payoffs during the cooperative game process. The key components of cooperative game theory have two elements: first, the set of flexibility resources... Secondly, characteristic functions Let there be n independent participants in the flexible resource aggregate, including distributed photovoltaic owners, distributed wind farm owners, energy storage system investors, electric vehicle cluster managers, and adjustable building air conditioning operators, etc. Let the finite flexible resource set be denoted as . ,in, Numbering of flexibility resources; characteristic function For sets Every possible non-empty subset in Assignment, denoted as Let represent the value generated by all members in the set, so cooperative game theory can be used to... Let's represent this. Marginal contribution, benefit distribution, individual rationality, and collective rationality are all core elements of cooperative game theory, explained as follows: Flexibility resources In the league Marginal contribution Defined as For a cooperative game with transferable payouts The marginal contribution, benefit distribution, and game rationality constraints of flexibility resources in an alliance are defined as follows: Benefit Distribution Let be a payoff vector, representing the scheme with the total benefit of flexible resource allocation, where Representing the The payment allocated to each flexible resource. This applies to a cooperative game where payments are transferable. ,distribute It aligns with individual rationality if and only if the benefit allocated to each individual with flexibility is higher than that of non-cooperation, i.e. For a cooperative game with transferable payouts ,distribute It conforms to overall rationality if and only if the sum of the gains allocated to all flexible resources equals the total gain of the alliance. For a cooperative game with transferable payouts ,say An efficient allocation is one that satisfies both individual rationality and collective rationality. Therefore, this application proposes two assumptions regarding the allocation of cooperative game payoffs: First, each flexible resource is absolutely rational and independent, making judgments with the goal of maximizing its own payoff, thereby making optimal decisions; Second, each flexible resource possesses collective rationality, that is, it recognizes that collaboration can improve overall payoffs.

[0055] Based on cooperative game theory, the economic marginal contribution of each flexible resource is calculated according to its adjustment power capacity in the scheduling scheme. In the cooperative game model, for flexible resource i, its economic marginal contribution is defined as the incremental revenue brought by the flexible resource joining any sub-coalition, that is, the difference in the characteristic function before and after the flexible resource's participation, expressed as: ; In the formula, Indicating flexibility resources In the league The marginal contribution in, i.e. The incremental revenue that joining the alliance brings to the alliance; The characteristic function represents the alliance. Total potential gains; For the characteristic function in the sub-coalition The value that can be taken on.

[0056] The conventional Shapley value allocation method represents an idealized scenario, focusing only on the economic performance of each entity. It assigns equal weights to all flexibility resources within the alliance, failing to reflect the individual differences among these resources. In cooperative alliances comprised of flexibility resources, the differences among independent investors are more pronounced, and traditional methods neglect other crucial factors such as security and environmental impact. Therefore, to ensure the fairness and reasonableness of the allocation results and maintain the stable operation of the cooperative game alliance, this paper introduces security and environmental contribution factors to improve the traditional Shapley value benefit allocation model. This aims to achieve true equilibrium by meeting the expectations of each entity, resulting in the final benefit distribution scheme.

[0057] As an example of an embodiment of the present invention, the total revenue is allocated according to the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection to obtain a revenue allocation scheme, specifically as follows: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0058] In this embodiment, the initial allocation scheme is determined based on the economic marginal contribution, as follows: ; in, For flexibility resources The Shapley value, that is, according to the Shapley rule, The final share of the profits that should be distributed.

[0059] Let the contribution of each entity to the security of the flexible resource aggregate be... The contribution of each entity to environmental protection is The weighting coefficients for safety and environmental friendliness are respectively and ,and =1, and based on this weighting coefficient, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor, expressed as: ; Based on the improvement factor, the initial allocation scheme is modified, and the final profit allocation scheme determined based on the improved Shapley value method is expressed as follows: ; ; In the formula, To improve the revenue distribution scheme for Shapley values; A revenue distribution scheme for the classic Shapley value; The total revenue for all participating entities in the alliance.

[0060] As an example of an embodiment of the present invention, after obtaining the profit distribution plan, the method further includes: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0061] In this embodiment, after the revenue distribution is completed, the aggregate records the revenue distribution results of each flexible resource in the revenue distribution scheme to the historical contribution archive. This archive is used to store the contribution records of each resource entity in each collaborative scheduling, serving as the basis for subsequent priority adjustments and contribution corrections in scheduling cycles.

[0062] At the start of the next scheduling cycle, the priority of each resource is determined based on the revenue allocation results of each flexibility resource in the historical contribution file. Specifically, resource entities with higher historical revenue allocation results (e.g., energy storage systems that have undertaken major regulation tasks and obtained higher revenue in multiple scheduling cycles) are given higher priority in the collaborative optimization of the next scheduling cycle.

[0063] The call priority is input as a parameter into the scheduling optimization model. During optimization, the model prioritizes calling higher-priority flexibility resources (e.g., prioritizing the discharge of energy storage systems or reducing building air conditioning load, provided constraints are met). The next scheduling instruction is issued by the power grid and, together with the call priority, constitutes the input of the optimization model, used to generate the scheduling scheme for the next scheduling cycle.

[0064] As a preferred example of this invention, after each flexible resource executes the scheduling command, it feeds back the actual response status (including actual discharge power, actual charging power, actual load reduction, etc.) to the aggregate's control system via a communication network. The control system uses this actual response status to update the initial operating state in the next round of external characteristic parameter determination, for example, updating the actual state of charge of the energy storage system and the actual remaining power of the electric vehicle.

[0065] The control system records the deviation between the actual response status of each flexible resource and the command value in the scheduling scheme, using this as a correction factor for contribution evaluation in the next round of revenue distribution. For example, if the actual adjustment power of a resource is significantly lower than the command value, its correction factor will reduce its weight in the safety or environmental protection contribution in subsequent revenue distribution, thereby incentivizing each resource to strictly follow the scheduling command.

[0066] Through the above steps, the profit distribution results indirectly but substantially influence the future generation and execution of control commands by the aggregate through an incentive feedback mechanism, forming a complete closed loop of "capability identification → collaborative optimization → command execution → profit distribution → incentive feedback → state update". This closed loop ensures the continuous accuracy of the aggregate's adjustment capabilities, the executability of scheduling commands, and the long-term stability of cooperation within the alliance.

[0067] As an example of an embodiment of the present invention, based on the target feasible region, the adjustable power threshold and power change rate threshold of the flexible resource aggregate are determined as follows: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0068] In this embodiment, as Figure 3 The diagram shows the structure of the multi-target recognition optimization model. As can be seen from the diagram, by adjusting the specific objective function and constraints of the model, the three core external characteristic parameters—power baseline, maximum adjustable power, and ramp rate—can be identified sequentially.

[0069] (1) Power Baseline Identification: The aggregate is optimized with the goal of minimizing the total operating cost during the day-ahead phase, and the resulting total active power curve of the aggregate is the power baseline. Based on this, the objective function in the multi-objective identification optimization model is defined as minimizing the day-ahead operating cost of the aggregate, and the decision variable is the scheduling period. Optimal total power of polymers within The expression is as follows: In the formula, This represents the energy cost of each flexible resource within the aggregate. Energy cost refers to the sum of all expenses incurred by the flexible resource aggregate in acquiring or consuming energy during operation, such as fuel costs, electricity purchase costs, operation and maintenance costs, environmental costs, and start-up / shutdown costs. This optimization model aims to minimize the total operating cost. By solving for a set of optimal internal resource scheduling schemes, it determines the aggregate's power baseline curve during the day-ahead phase. This power baseline serves as a basis for aggregates to participate in day-ahead market filings or formulate their own operating plans. It also acts as a reference benchmark for calculating maximum adjustable power and ramp rate in real-time. PV, W, ESS, EV, and AC correspond to photovoltaics, wind power, energy storage, electric vehicles, and building air conditioning, respectively.

[0070] (2) Maximum Adjustable Power Identification: This external characteristic parameter is used to quantify the power of the aggregate relative to the power baseline at any given time, defining the maximum power that can be increased or decreased, and thus defining the instantaneous adjustment range of the aggregate. Let the upper limit of the aggregate's overall output power be... The lower limit of the overall output power is The range formed by the curves of their changes over time, serving as upper and lower bounds, constitutes the feasible region of the aggregate's overall output power. To ensure the aggregate has the maximum adjustable range, i.e., maximum flexibility, the objective function of the multi-target recognition optimization model is set to maximize the adjustable range, as expressed below: ; In the formula: and These are the upper and lower limits of the aggregate's overall output power during time period t, respectively, and are the decision variables of the model. The optimization objective is to maximize the sum of the aggregate's adjustable power ranges over the entire scheduling period T. The solution obtained... and Together, they define the boundary of the dynamic feasible domain of the aggregate at each moment. The feasible region is a hard constraint that must be strictly followed in the scheduling optimization model. It ensures that no external scheduling instruction or internal optimization scheme will exceed the current physical adjustment capability limit of the aggregate, thereby guaranteeing the safety and feasibility of scheduling.

[0071] (3) Climbing rate (i.e., power change rate): The climbing rate characterizes the maximum rate at which the output power of an aggregate can change per unit time when responding to a scheduling command. The aggregate climbing rate characteristic is divided into uphill climbing rate. and downhill speed This represents the increase / decrease in the overall power output of the aggregate per unit time compared to the power baseline at that moment, i.e., at the scheduling time. The aggregate receives the adjustment command at the scheduling time. The maximum values ​​of the increase and decrease in the overall output power of the aggregate. To solve for the aggregate's ramp rate, the objective function in the multi-objective identification optimization model is set as follows: ; ; ; In the formula, express The overall output power of the aggregate at any given moment and The difference in the aggregate baseline at any given time represents the amount of uphill climb; similarly, Indicates the amount of downhill climb; This represents the time interval between two scheduling moments. From this, we obtain the dynamic climbing ability of the aggregate at each moment t. and .

[0072] The constraint conditions for solving the climbing rate characteristics are expressed as follows: ; In the formula, the first four terms are the constraints in the external characteristic parameter identification and optimization model; the fifth term indicates that the adjustment of each flexible resource within the aggregate should meet its respective ramp rate requirements; the sixth term indicates that at time... The adjustment has been completed, and the overall output power of the polymer is now equal to the commanded power. In addition, the overall adjustment amount of the polymer. It also needs to satisfy its own climbing direction logic (climbing uphill or climbing downhill).

[0073] ; The above formula indicates that the polymer should be regulated under the aforementioned maximum adjustable power characteristic limit.

[0074] This step outputs the power baseline, real-time adjustable upper and lower limits of the aggregate, and the uphill and downhill rates of the aggregate at any scheduling time t. These dynamic characteristic parameters constitute the aggregate's ability to participate in grid regulation and control, and serve as the physical boundary basis for scheduling optimization decisions.

[0075] As an example of an embodiment of the present invention, the scheduling optimization model is solved under the constraints of the target feasible region and the power change rate threshold, specifically as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0076] In this embodiment, a multi-agent collaborative scheduling optimization model integrating constraints on external characteristic parameters is constructed to solve for the optimal internal resource scheduling scheme. In the construction and operation of the flexible resource aggregate, each flexible resource must make decisions with the goal of maximizing its own benefit. Therefore, the objective function is set as a function that maximizes the total benefit of the flexible resource aggregate, i.e., maximizing the sum of the net benefits of each flexible resource. The general objective function expression is as follows: ; In the formula, For flexibility resources Total revenue; For flexibility resources Electricity sales revenue; For flexibility resources Ancillary service revenue; For flexibility resources Government subsidy income; For flexibility resources Investment and installation costs; For flexibility resources The operating and maintenance costs; For flexibility resources The cost of purchasing electricity; The cost of penalizing the abandonment of wind and solar power; For flexibility resources The cost of penalties for power outages; For flexibility resources Fuel costs (such as gas turbines); For flexibility resources Environmental costs; This represents different types of flexible resources, such as wind power and solar power.

[0077] The mathematical models for each specific revenue item are as follows: (1) Electricity sales revenue: Flexibility resources Revenue generated through selling electricity to aggregates or through aggregates to the main grid is represented as: ; ; In the formula, for Real-time flexibility resources The on-grid electricity price; for Real-time flexibility resources Battery consumption for internet access; For flexibility resources Total installed capacity; The total installed capacity of all flexible resources; for The amount of electricity required by the load at any given time; for Total power generation of all flexible resources at any given time; This represents the maximum electrical quantity that the line is allowed to transmit. for Real-time flexibility resources Power generation capacity.

[0078] (2) Ancillary service revenue (considering resources that can provide ancillary services such as peak shaving, such as energy storage, rapidly adjustable distributed generators, interruptible loads, etc.) is expressed as: ; In the formula, for Real-time flexibility resources Ancillary service revenue per unit of electricity; for Real-time flexibility resources Peak-shaving capacity.

[0079] For energy storage flexibility resources, The state of charge / discharge during a given period is related to the power involved in ancillary services, and is expressed as: ; ; ; ; In the formula, For energy storage Discharge amount over a period of time; For energy storage Total power over the time period; For energy storage capacity; For the revenue generated by energy storage participating in ancillary services; For energy storage Minimum power limits for different time periods; The unit electricity conversion factor for energy storage participating in ancillary services; For energy storage The power of participating in ancillary services during a given time period.

[0080] (3) Investment and installation costs, expressed as: ; In the formula, For flexibility resources The unit capacity investment cost; The discount rate; For flexibility resources Economic service life.

[0081] (4) Operation and maintenance costs, expressed as: ; In the formula, For flexibility resources The unit capacity operation and maintenance cost.

[0082] (5) Electricity purchase cost When the internal output of the aggregate is insufficient to meet the internal load or contractual obligations, electricity must be purchased from the main grid. For simplicity, an equivalent net load can be defined. , is represented as: ; ; ; In the formula, for Equivalent net load of aggregate during the time period; The total output power inside the polymer; The maximum allowable power capacity for purchasing electricity from the main grid; for Electricity purchased from the main network by the time-period aggregate; The electricity purchase price; For flexibility resources The total cost of purchasing electricity.

[0083] (6) The penalty for wind and solar power curtailment is expressed as follows: ; In the formula, The penalty costs for abandoning wind and solar power; The penalty price per unit of wind and solar power curtailment; To provide power for wind power forecasting in each time period; The actual wind power output for each time period; Contribute to photovoltaic forecasting for each time period; The actual output of photovoltaic power in each time period.

[0084] (7) Power outage penalty fee, expressed as: ; ; In the formula, For flexibility resources The penalty fees for power outages that must be borne; Let be the amount of power lost at time t; The penalty price is calculated per unit of electricity used for power outages.

[0085] (8) Fuel cost, expressed as: ; In the formula, For flexibility resources The cost of fuel consumed per unit of electricity generated; For flexibility resources Consumption during power generation Fuel consumption per unit of electricity for a particular fuel.

[0086] (9) Environmental costs, expressed as: ; In the formula, For flexibility resources Environmental cost per unit of electricity generated; Number of pollutants; To treat pollutants per unit volume Required costs; For flexibility resources Pollutants per unit of electricity generated The amount of emissions.

[0087] (10) Government subsidies, expressed as: ; In the formula, For flexibility resources The total amount of government subsidies received; For flexibility resources exist Internet power consumption during different time periods; For flexibility resources Electricity price subsidies for power generation.

[0088] The specific constraints are expressed as follows: (1) Mutual exclusion constraint on the purchase and sale of electricity by the aggregate means that the aggregate at any given time... You cannot simultaneously purchase and sell electricity from the power grid, as indicated by: ; In the formula, , These refer to the electricity purchased and sold by the flexible resource aggregator from the upper-level power grid (main grid).

[0089] (2) Power balance constraint refers to the real-time balance of power generation, energy storage discharge and charging, load and power exchange with the grid within the aggregate, which is expressed as: ; In the formula, For the first Generating / discharging resources during time periods The actual operating power; , For the aggregate in time period The power volume of electricity purchased and sold from the main grid; The total load inside the polymer during the time period The power; , Energy storage time period The charging and discharging power.

[0090] (3) The state of charge constraint of the energy storage battery is expressed as: ; In the formula , These are the upper and lower limits of the state of charge (SOC) of the energy storage battery. When the SOC exceeds... Stop charging the energy storage battery when the state of charge is below a certain level. When this happens, the energy storage battery discharge stops.

[0091] (4) Wind turbine generation constraints, expressed as: ; ; In the formula, Forecast power output of wind turbine units; This is the upper limit of the wind turbine's output.

[0092] (5) The power generation constraint of the photovoltaic unit is expressed as: ; ; In the formula, Forecasting power output for photovoltaic units; This is the upper limit of the output of the photovoltaic unit.

[0093] (6) The output constraint of the adjustable distributed generator is expressed as: ; ; In the formula, For the first Adjustable distributed generators during time periods contribution; , For the first Minimum and maximum output limits of the adjustable distributed generator; For the first The maximum ramping power of the generators in adjacent time periods.

[0094] (7) External instruction matching constraints, represented as: ; This formula indicates that the optimization results must strictly meet the requirements of power grid dispatching.

[0095] (8) The feasible region constraint of the aggregate dynamic power is expressed as: ; This formula indicates that the optimized total power curve of the polymer must remain within the dynamic feasible region throughout the entire process.

[0096] (9) The feasible region constraint of the aggregate dynamic power is expressed as: ; This formula represents the rate of change of total power of the aggregate resulting from the coordinated adjustment of internal resources, which must not exceed the real-time ramp-up capability.

[0097] To verify the effectiveness of the scheduling optimization method proposed in this invention, a flexible resource aggregation within a typical distribution network area is selected as the research object for implementation analysis. This embodiment adopts a typical 24-hour daily scheduling scenario with a time resolution of 1 hour. The analysis process is divided into two stages: the day-ahead stage identifies the power baseline with the goal of minimizing the aggregation's operating cost, and further calculates the maximum adjustable power boundaries and ramp rates for each time period, determining the physical boundary based on the dynamic external characteristic parameters identified in the day-ahead stage; the real-time stage, under the given external scheduling instructions, performs collaborative decomposition of various resources within the aggregation based on this physical boundary, and analyzes the contribution and benefit distribution of each resource in this scheduling.

[0098] In this embodiment, the rated parameters of various resources are set as follows: the rated capacity of distributed photovoltaic power is 200kW, the rated capacity of distributed wind power is 250kW, the rated charge / discharge power of the energy storage system is 150kW, the rated energy capacity is 300kWh, the adjustable capacity of the electric vehicle cluster is 100kW, the adjustable capacity of the building air conditioning is 80kW, the state of charge constraint range of the energy storage system is 20% to 90%, and the charge / discharge efficiency is 95%; the electric vehicle cluster has high adjustability potential during evening peak hours and nighttime off-peak hours; the building air conditioning achieves short-term flexible load response by setting temperature offsets. The basic resource parameters are shown in Table 1.

[0099] Table 1. Basic Parameters of Internal Flexibility Resources of Aggregates Typical daily equivalent load, photovoltaic (PV) predicted output, and wind power predicted output all exhibit significant time-varying characteristics: the system load is low in the early morning, and the net external output demand of the aggregate is relatively low at noon due to the high output of PV; while from 18:00 to 20:00 in the evening, as the load rises rapidly and PV power is quickly deactivated, the aggregate faces a stronger upward adjustment demand. Therefore, this period is also a key scenario for subsequent verification of the scheduling optimization capability based on the external characteristic parameters of the aggregate. Figure 4 This paper presents a comparison of the basic parameters of various flexible resources within the energy storage system in terms of rated adjustable capacity, continuous support capability, and response speed. It can be seen that the energy storage system has a significant advantage in rapid response, the electric vehicle cluster is relatively balanced in terms of adjustable capacity and continuous support capability, and building air conditioning has strong short-term call-up potential on the flexible load side. The differences and complementarities in the dynamic characteristics of different resources provide a foundation for subsequent identification of external characteristic parameters and coordinated control.

[0100] Following the original method's approach of identifying external characteristic parameters, the power baseline of the aggregate is first solved at the day-ahead stage with the objective of minimizing total operating cost. The identification results show that the day-ahead power baseline of the aggregate is a reference output curve that varies with time, reflecting the planned operating state of the aggregate under economically optimal conditions without considering additional external commands.

[0101] Corresponding to this embodiment, the obtained day-ahead power baseline P base The values ​​are: [165,160,158,156,160,172,188,210,228,242,255,268,274,270,262,258,266,282,298,304,292,268,228,188]kW.

[0102] Based on the power baseline, the maximum upper and lower adjustable power boundaries of the polymer are further identified. In this embodiment, the upper boundary P... up The lower boundary is: [207,200,196,192,198,214,238,268,294,314,331,346,354,348,334,328,338,362,390,399,380,344,288,236]kW; low The values ​​are: [131,128,128,126,128,136,146,160,172,182,191,200,204,202,198,196,206,224,244,254,244,222,188,152]kW.

[0103] It can be seen that during the midday period, due to the strong output of photovoltaic power and the synergistic adjustment space of energy storage and flexible loads, the adjustable range of the aggregate is relatively wide; while during the evening peak period, although photovoltaic power is basically off, the aggregate still maintains a strong upward adjustment capability due to the combined effects of energy storage discharge, reverse adjustment of electric vehicle clusters, and load reduction of building air conditioning. Figure 5 The daytime power baseline and upper and lower boundaries of the dynamic operational feasible domain of the aggregate are presented under a typical 24-hour scenario. It can be seen that the power baseline reflects the planned operational trajectory of the aggregate under optimal economic conditions, while the upper and lower boundaries jointly characterize its maximum adjustable range in each time period. Both the midday and evening peak periods exhibit relatively wide adjustment ranges, but the sources of their power differ significantly, reflecting the time-varying nature of the aggregate's dynamic external characteristic parameters.

[0104] Based on the maximum adjustable power limits, the upward and downward ramp rates of the polymer are further identified. In this embodiment, the upward ramp rate R... up The following values ​​are given: [28,26,25,24,24,26,32,36,40,42,44,45,44,43,40,38,40,46,52,54,50,44,36,30] kW / h; downhill / climbing speed R down The value is: [24,23,22,21,21,24,28,32,36,38,40,42,42,40,38,36,38,42,46,48,46,40,32,28]kW / h. Figure 6 The data shows the changes in the aggregate's uphill and downhill rates over a typical 24-hour day. It can be seen that the aggregate's climbing ability is not constant, but dynamically changes with resource status and time-of-day characteristics. The uphill ability reaches a relatively high level near the evening peak, indicating that the aggregate can respond more quickly to external upward demand at this time.

[0105] To further verify the effectiveness of the present invention in the real-time control phase, the evening peak period of 19:00 was selected as a typical control scenario in this embodiment. According to the aforementioned external characteristic parameter identification results, the power baseline of the aggregate during this period is 304kW, the upward adjustment boundary is 399kW, the downward adjustment boundary is 254kW, the upward ramp rate is 54kW / h, and the downward ramp rate is 48kW / h.

[0106] Assuming the power grid issues a real-time adjustment command to the aggregate at this moment, requiring its overall output to increase from the current baseline to 389kW, the corresponding adjustment increment is 85kW. Since 389kW satisfies 254≤389≤399, this target value is within the current aggregate's dynamic power feasible region and is feasible from a power boundary perspective. If the dispatch requirement is to be completed within 2 hours, the average power change rate is approximately 42.5kW / h, which is less than the current time period's ramp rate limit of 54kW / h. Therefore, it also meets the requirements from a dynamic response speed perspective.

[0107] Therefore, it can be seen that the adjustment command has passed both the power boundary and ramp rate constraint checks, and can proceed to the subsequent internal resource coordination decomposition stage. This process demonstrates the advantage of this method compared to the static adjustment capability given method, namely, that the physical feasibility of the command can be judged based on dynamic external characteristic parameters before execution.

[0108] In a typical regulation scenario, after the external command undergoes feasibility verification, the internal resources of the aggregate are further decomposed into regulation tasks based on a multi-agent collaborative scheduling optimization model that integrates dynamic external characteristic parameter constraints. Solving for the 85kW upward adjustment demand, the resource decomposition results for the aggregate at 19:00 are as follows: wind power 8kW, photovoltaic 0kW, energy storage system 35kW, electric vehicle cluster 22kW, building air conditioning 20kW, totaling 85kW.

[0109] It can be seen that in the nighttime scenario at 19:00, photovoltaic power has no room for upward adjustment and therefore does not undertake the adjustment task; wind power, due to its certain randomness, only undertakes a small marginal adjustment; energy storage systems become the main response resource due to their rapid discharge capability; electric vehicle clusters provide medium-scale support by reducing charging or reverse discharging; and building air conditioning undertakes the remaining part by reducing short-term load.

[0110] Figure 7 The regulation contribution of various resources within the energy cluster is presented during typical evening peak regulation times. It can be seen that energy storage systems, electric vehicle clusters, and building air conditioning are the main contributors to this regulation task, while photovoltaic power generation did not participate in regulation due to the lack of output space at night. This result indicates that the dynamic regulation capability exhibited externally by the energy cluster can be concretely realized through the internal multi-resource collaborative decomposition.

[0111] After the consortium completes its internal resource scheduling, the revenue of each participating entity in this collaborative adjustment is further distributed according to the revenue distribution mechanism based on the improved Shapley value. Assume the total alliance revenue from this collaborative adjustment is 960 yuan. The basic revenue distribution calculated using the traditional Shapley value is: wind power 90 yuan, photovoltaic 30 yuan, energy storage system 360 yuan, electric vehicle cluster 250 yuan, and building air conditioning 230 yuan.

[0112] After considering the additional contributions of each entity to system safety, stability, and environmental benefits, the final revenue distribution, corrected using the improved Shapley value method, is as follows: wind power 96 yuan, photovoltaic 28 yuan, energy storage system 392 yuan, electric vehicle cluster 246 yuan, and building air conditioning 198 yuan. This demonstrates that the energy storage system, undertaking the primary rapid response task, plays the most prominent role in supporting system safety and stability during peak evening hours; therefore, its revenue share is further increased after introducing a safety contribution factor.

[0113] Figure 8 This paper presents a comparison of the revenue distribution results for various participating entities under the traditional Shapley value method and the improved Shapley value method in typical regulation scenarios. It can be seen that, after considering contributions to safety and environmental protection, energy storage systems with stronger rapid response capabilities receive a higher share of revenue. Furthermore, the revenue distribution for other entities better reflects their comprehensive contributions to coordinated regulation, indicating that the improved Shapley value method has better fairness and incentive rationality.

[0114] After revenue distribution is completed, the aggregate control system executes specific control commands based on the command decomposition results. The energy storage system receives a discharge power setpoint of 35kW and sends it to the energy storage converter via the communication network; the electric vehicle cluster receives a charging power upper limit adjustment command, requiring it to reduce the charging power by 22kW, and sends it to each charging pile terminal; the building air conditioning system receives a temperature setpoint adjustment command, raising the set temperature by 2℃, and executes it through the building energy management system.

[0115] Meanwhile, the control system will record the revenue distribution results in the historical contribution files of each entity, including 392 yuan for energy storage systems, 246 yuan for electric vehicle clusters, 198 yuan for building air conditioning, 96 yuan for wind power, and 28 yuan for photovoltaic power. This file will serve as the basis for adjusting the call priority in the next scheduling cycle's collaborative optimization. Entities with higher historical revenue contributions will be given higher call priority, thereby incentivizing all entities to continue providing high-quality regulation services.

[0116] After executing the instructions, each resource feeds back its actual response status to the aggregate control system, including the actual discharge power of the energy storage system, the actual charging power of the electric vehicle cluster, and the actual load reduction of the building's air conditioning. The control system records the deviations between these actual values ​​and the instruction values, using them as correction factors for contribution evaluation in the next round of revenue distribution. At the same time, it updates the real-time status of each resource, serving as the initial conditions for the next round of dynamic external characteristic parameter identification.

[0117] As can be seen from the entire process of this embodiment, the method proposed in this invention forms a complete closed-loop chain: First, in the day-ahead phase, by running feasible domain modeling and identifying external characteristic parameters, the power baseline, maximum adjustable power, and upward and downward ramp rates of the aggregate are obtained; second, in the real-time phase, when the grid issues a regulation command, the aggregate, based on the aforementioned identification results of external characteristic parameters, first completes the feasibility verification of the power boundary and ramp rate, and then, under the premise that physical constraints allow, performs collaborative decomposition of internal resources; finally, by improving the Shapley value benefit allocation method, the economic benefits formed by this collaborative regulation are allocated according to the comprehensive economic, safety, and environmental contributions of each entity.

[0118] Compared with traditional static methods, this method has advantages in three main aspects: First, the aggregate's external adjustment capability is no longer simplified to a fixed capacity, but is characterized by time-varying external characteristic parameters such as power baseline, dynamic boundary, and ramp rate. Therefore, it can more accurately reflect the aggregate's true adjustment potential under different operating conditions. Second, external instructions have undergone dynamic external characteristic parameter constraint verification before entering the internal resource layer, which can effectively avoid the problem of "feasible at the planning level but unfulfillable at the execution level." Third, the combination of internal collaborative optimization and benefit distribution mechanism not only ensures the physical executability of instruction decomposition, but also takes into account the economic fairness and long-term stability within the alliance.

[0119] As a preferred example of an embodiment of the present invention, based on such Figure 9 The closed-loop collaborative control mechanism for flexible resource aggregates, as shown, provides an optimized scheduling system for flexible resource aggregates, specifically including: The external input layer is used to acquire real-time power grid dispatch instructions, market price signals, load / new energy forecast information, and resource status data; The capability identification layer is used to construct a high-dimensional feasible region of the internal resource scheduling scheme based on load / new energy forecast information and resource status data, project it to obtain the active power operation feasible region, and solve the external characteristic identification dynamic optimization model by setting different objective functions in sequence to identify the power baseline, adjustable power threshold and power change rate threshold. The collaborative optimization layer is used to receive real-time grid dispatch instructions and verify the feasibility of the instructions using adjustable power thresholds and power change rate thresholds. After the verification is passed, an optimized dispatch model is established with the goal of maximizing the total revenue of the alliance. The active power operation feasible region and power change rate threshold are used as core constraints. Combined with market price signals and resource status data, the optimal dispatch scheme of each internal resource and the total revenue of the alliance are obtained. The resource response layer is used to execute the scheduling scheme and feed back the actual response status (actual discharge power, actual charging power, etc.) to the capability identification layer to update the initial state of the next round of dynamic external characteristic identification.

[0120] The incentive feedback layer is used to allocate revenue based on the total revenue of the alliance and the multi-dimensional contribution indicators of each resource (adjustment power capacity, security improvement, carbon emission reduction), using an improved Shapley value method, and recording the allocation results in the historical contribution archive. In the next scheduling cycle, the priority of each resource is determined according to the historical revenue and input into the collaborative optimization layer. At the same time, the actual response deviation is recorded as a correction factor for contribution evaluation in the next round of revenue allocation.

[0121] This forms a complete closed loop: external input → capability identification → collaborative optimization → instruction execution → profit distribution → incentive feedback → state update.

[0122] like Figure 10 As shown, based on the above method embodiments, an embodiment of the present invention provides an optimized scheduling system 1000, including: a feasible domain construction module 1001, a mapping module 1002, a threshold determination module 1003, and a scheduling solution module 1004; The feasible region construction module 1001 is used to determine the initial feasible region, which includes multiple resource scheduling schemes, based on the operational constraints of each flexible resource in the flexible resource aggregate. The mapping module 1002 is used to map the initial feasible region to the active power plane based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, so as to obtain the target feasible region of the flexible resource aggregate. The threshold determination module 1003 is used to determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate based on the target feasible region. The scheduling solution module 1004 is used to obtain real-time scheduling instructions from the power grid. Based on the adjustable power threshold and the power change rate threshold, the real-time scheduling instructions are physically verified. If the verification is successful, the real-time scheduling instructions are input into the preset scheduling optimization model. Under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved to obtain the scheduling schemes for each flexible resource.

[0123] Furthermore, the output of the scheduling optimization model also includes total revenue; The optimized scheduling system 1000 is also used for: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0124] Furthermore, the optimized scheduling system 1000 is also used for: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0125] Furthermore, the optimized scheduling system 1000 is also used for: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0126] Furthermore, the threshold determination module 1003 is also used for: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0127] Furthermore, the scheduling solver module 1004 is also used for: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0128] It is understood that the above system embodiments correspond to the method embodiments of the present invention, and can implement all the implementation methods of any of the above method embodiments of the present invention, which will not be repeated here.

[0129] 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.

[0130] Based on the above embodiments of the optimized scheduling method, 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 optimized scheduling method of any of the above embodiments of the present invention, the steps of which include: Based on the operational constraints of each flexible resource in the flexible resource aggregate, an initial feasible domain including multiple resource scheduling schemes is determined. Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate. Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate; The system acquires real-time dispatch instructions from the power grid, performs physical feasibility checks on these instructions based on adjustable power thresholds and power change rate thresholds, and inputs the real-time dispatch instructions into a preset dispatch optimization model if the checks pass. Under the constraints of the target feasible region and the power change rate threshold, the system solves the dispatch optimization model to obtain dispatch schemes for each flexible resource.

[0131] Furthermore, the output of the scheduling optimization model also includes total revenue; After obtaining the scheduling schemes for each flexible resource, the following is also included: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0132] Furthermore, based on the marginal economic contribution, the additional contribution to safety, and the additional contribution to environmental protection, the total revenue is allocated to obtain the revenue distribution plan, as follows: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0133] Furthermore, after obtaining the profit distribution plan, it also includes: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0134] Furthermore, based on the target feasible region, the adjustable power threshold and power change rate threshold of the flexible resource aggregate are determined, specifically as follows: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0135] Furthermore, under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved, specifically as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 optimized scheduling method described in any of the above-described method embodiments of the present invention, the steps of which include: Based on the operational constraints of each flexible resource in the flexible resource aggregate, an initial feasible domain including multiple resource scheduling schemes is determined. Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate. Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate; The system acquires real-time dispatch instructions from the power grid, performs physical feasibility checks on these instructions based on adjustable power thresholds and power change rate thresholds, and inputs the real-time dispatch instructions into a preset dispatch optimization model if the checks pass. Under the constraints of the target feasible region and the power change rate threshold, the system solves the dispatch optimization model to obtain dispatch schemes for each flexible resource.

[0140] Furthermore, the output of the scheduling optimization model also includes total revenue; After obtaining the scheduling schemes for each flexible resource, the following is also included: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0141] Furthermore, based on the marginal economic contribution, the additional contribution to safety, and the additional contribution to environmental protection, the total revenue is allocated to obtain the revenue distribution plan, as follows: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0142] Furthermore, after obtaining the profit distribution plan, it also includes: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0143] Furthermore, based on the target feasible region, the adjustable power threshold and power change rate threshold of the flexible resource aggregate are determined, specifically as follows: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0144] Furthermore, under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved, specifically as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0145] Based on the above-described method embodiments, this invention further provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the optimized scheduling method described in any of the above-described method embodiments, the steps of which include: Based on the operational constraints of each flexible resource in the flexible resource aggregate, an initial feasible domain including multiple resource scheduling schemes is determined. Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate. Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate; The system acquires real-time dispatch instructions from the power grid, performs physical feasibility checks on these instructions based on adjustable power thresholds and power change rate thresholds, and inputs the real-time dispatch instructions into a preset dispatch optimization model if the checks pass. Under the constraints of the target feasible region and the power change rate threshold, the system solves the dispatch optimization model to obtain dispatch schemes for each flexible resource.

[0146] Furthermore, the output of the scheduling optimization model also includes total revenue; After obtaining the scheduling schemes for each flexible resource, the following is also included: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each flexibility resource is calculated based on the security enhancement amount; The environmental contribution of each flexible resource is calculated based on carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue allocation scheme.

[0147] Furthermore, based on the marginal economic contribution, the additional contribution to safety, and the additional contribution to environmental protection, the total revenue is allocated to obtain the revenue distribution plan, as follows: The initial allocation plan is determined based on the marginal contribution of the economy; Based on the preset weighted coefficient set, the additional contributions to safety and environmental protection are weighted and summed to obtain the improvement factor; The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

[0148] Furthermore, after obtaining the profit distribution plan, it also includes: In the next scheduling cycle, the priority of each flexible resource is determined based on the revenue distribution scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

[0149] Furthermore, based on the target feasible region, the adjustable power threshold and power change rate threshold of the flexible resource aggregate are determined, specifically as follows: Within the target feasible region, the daytime power baseline of the flexible resource aggregate is obtained by minimizing the total daytime operating cost of the flexible resource aggregate. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained by solving based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

[0150] Furthermore, under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved, specifically as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, and external constraints of dispatch command matching, target feasible region and power change rate threshold.

[0151] 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.

[0152] 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. An optimized scheduling method, characterized in that, include: Based on the operational constraints of each flexible resource in the flexible resource aggregate, an initial feasible domain including multiple resource scheduling schemes is determined. Based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregate, the initial feasible region is mapped to the active power plane to obtain the target feasible region of the flexible resource aggregate. Based on the target feasible region, determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate; The real-time dispatch command of the power grid is obtained. The physical feasibility of the real-time dispatch command is checked based on the adjustable power threshold and the power change rate threshold. If the check passes, the real-time dispatch command is input into a preset dispatch optimization model. Under the constraints of the target feasible region and the power change rate threshold, the dispatch optimization model is solved to obtain the target dispatch scheme for each of the flexible resources.

2. The optimized scheduling method as described in claim 1, characterized in that, The output of the scheduling optimization model also includes total revenue; After obtaining the target scheduling scheme for each of the aforementioned flexibility resources, the method further includes: Obtain the security enhancement and carbon emission reduction corresponding to each flexibility resource; Based on the adjustment power of each flexible resource in the target scheduling scheme, the economic marginal contribution of each flexible resource is calculated. The additional security contribution of each of the aforementioned flexibility resources is calculated based on the security enhancement amount. The environmental contribution of each of the aforementioned flexible resources is calculated based on the carbon emission reductions. Using the target Shapley value method, the total revenue is allocated based on the economic marginal contribution, the safety additional contribution, and the environmental additional contribution to obtain a revenue allocation scheme.

3. The optimized scheduling method as described in claim 2, characterized in that, The total revenue is allocated based on the economic marginal contribution, the additional contribution to safety, and the additional contribution to environmental protection, resulting in a revenue distribution plan, as follows: The initial allocation scheme is determined based on the aforementioned economic marginal contribution; Based on a preset set of weighting coefficients, the additional contribution to safety and the additional contribution to environmental protection are weighted and summed to obtain an improvement factor. The initial allocation scheme is modified based on the improvement factor to obtain the profit distribution scheme.

4. The optimized scheduling method as described in claim 2, characterized in that, After obtaining the profit distribution plan, the following is also included: In the next scheduling cycle, the priority of each of the aforementioned flexibility resources is determined based on the revenue allocation scheme; The call priority and the next scheduling instruction are input into the scheduling optimization model to obtain the next scheduling scheme.

5. The optimized scheduling method as described in claim 1, characterized in that, The determination of the adjustable power threshold and power change rate threshold of the flexible resource aggregate based on the target feasible region specifically involves: Within the target feasible region, the day-ahead power baseline of the flexible resource aggregator is obtained by minimizing the total day-ahead operating cost of the flexible resource aggregator. With the goal of maximizing the adjustable power range of the flexible resource aggregate, the adjustable power threshold is obtained based on the power baseline; With the goal of maximizing the power change rate of the polymer, the power change rate threshold is obtained by solving based on the power baseline.

6. The optimized scheduling method as described in claim 1, characterized in that, The process of solving the scheduling optimization model under the constraints of the target feasible region and the power change rate threshold is as follows: The objective function is to maximize the total revenue of the flexible resource aggregate. The objective function is solved under the constraints of mutual exclusion of electricity purchase and sale behavior in the flexible resource aggregate, power balance constraints of each flexible resource, state of charge constraints of energy storage batteries, power constraints of generator sets, external scheduling command matching constraints, the target feasible region and the power change rate threshold.

7. An optimized scheduling system, characterized in that, include: The module includes a feasible region construction module, a mapping module, a threshold determination module, and a scheduling solution module. The feasible domain construction module is used to determine an initial feasible domain, including multiple resource scheduling schemes, based on the operational constraints of each flexible resource in the flexible resource aggregate. The mapping module is used to map the initial feasible region to the active power plane based on the correspondence between each resource scheduling scheme and the total output power of the flexible resource aggregator, so as to obtain the target feasible region of the flexible resource aggregator. The threshold determination module is used to determine the adjustable power threshold and power change rate threshold of the flexible resource aggregate based on the target feasible region. The scheduling solution module is used to obtain real-time scheduling instructions from the power grid, perform physical feasibility checks on the real-time scheduling instructions based on the adjustable power threshold and the power change rate threshold, and input the real-time scheduling instructions into a preset scheduling optimization model if the check passes. Under the constraints of the target feasible region and the power change rate threshold, the scheduling optimization model is solved to obtain the target scheduling scheme for each of the flexibility resources.

8. 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 optimized scheduling method as described in any one of claims 1-6.

9. 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 optimized scheduling method as described in any one of claims 1-6.

10. A computer program product, characterized in that, include: Computer instructions, when executed by a processor, implement the optimized scheduling method as described in any one of claims 1 to 6.

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