Provincial and prefecture coordinated regulation and control method with participation of uncertainty adjustable resources

Abstract

The invention discloses a provincial and regional cooperative regulation and control method with participation of uncertainty adjustable resources, and relates to the technical field of power system scheduling and control, and the method comprises the steps: constructing an uncertainty resource model, and carrying out the quantitative modeling of the uncertainty factors of wind power, photovoltaic and load demands; designing a provincial and regional collaborative optimization algorithm, and constructing a regulation and control strategy based on a double-layer optimization framework; formulating an uncertain resource regulation and control scheme, and combining historical data and real-time prediction to adjust resource configuration; and executing regulation and control result verification and feedback, evaluating a regulation and control effect and adjusting algorithm parameters. Through the systematized provincial and regional coordinated regulation method, remarkable technical improvement is realized in multiple aspects. The model building module adopts a probability distribution method to carry out accurate quantitative modeling on random characteristics of wind power and photovoltaic resources, and compared with a traditional simplified model, the characterization precision of uncertain resource characteristics is improved, and scheduling errors caused by prediction deviation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and control, and in particular to a provincial and local coordinated control method involving uncertain adjustable resources. Background Art

[0002] As a core component of modern energy infrastructure, the power system is undergoing a fundamental transformation from traditional centralized power generation to a distributed, diversified energy structure as the global energy transition deepens. The penetration of renewable energy sources such as wind and solar power in the power system continues to increase. However, the intermittent, random, and volatile nature of these energy sources poses significant challenges to the safe and stable operation of the power grid. To address these challenges, power dispatch and control technology has made significant progress in recent years, including predictive dispatch algorithms, adaptive control strategies, and demand response mechanisms. Of particular note is the gradual establishment of a hierarchical and regional power dispatch system, with provincial and local dispatch agencies assuming different levels of regulatory responsibilities.

[0003] While existing technologies have made progress in control algorithms and forecasting models, significant deficiencies remain in areas such as provincial and local coordination mechanisms, uncertainty modeling, and multi-timescale coordination. First, traditional scheduling methods often focus on localized regions or single power sources, making it difficult to achieve coordinated optimization and dynamic allocation of resources across regions. For example, some regions may have abundant wind energy resources while others may have predominant solar energy resources. However, the lack of interregional coordination mechanisms prevents the full complementarity of these resources. Second, uncertainty modeling for resources like wind and photovoltaic power is overly simplistic, often employing deterministic equivalents or simple random distribution models that fail to accurately characterize their dynamic characteristics and correlation structures. Third, existing system architectures are often centralized, resulting in significant delays between data processing and decision execution, making them difficult to adapt to the rapid fluctuations in renewable energy power. Fourth, control strategies often focus on optimizing a single objective, such as economy or reliability, and lack multi-objective coordinated optimization mechanisms, resulting in low overall system efficiency. Finally, imperfect mechanisms for evaluating and providing feedback on control results make closed-loop optimization difficult, and system adaptability and robustness urgently need to be improved. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a provincial and local coordinated control method involving uncertain adjustable resources, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a provincial and local coordinated control method involving uncertain adjustable resources, including: constructing an uncertain resource model to quantitatively model the uncertainty factors of wind power, photovoltaics and load demand; designing a provincial and local coordinated optimization algorithm to construct a control strategy based on a two-layer optimization framework; formulating an uncertain resource control plan to adjust resource allocation in combination with historical data and real-time predictions; executing control result verification and feedback, evaluating the control effect and adjusting algorithm parameters.

[0007] As a preferred solution of the provincial and local coordinated control method involving uncertain adjustable resources described in the present invention, the step of constructing an uncertain resource model includes: identifying and classifying the resources involved in the control; quantifying the availability of resources using probability theory methods; and verifying the uncertain resource model using Monte Carlo simulation based on probability distribution.

[0008] As an optimal solution for the provincial and local coordinated control method involving uncertain adjustable resources described in the present invention, the two-layer optimization framework includes: the upper layer is the provincial power grid control model, which is responsible for the power balance of the entire network and the allocation of distributed power sources; the lower layer is the local resource allocation model, which is responsible for executing the coordinated control requirements of the upper-level scheduling.

[0009] As an optimal solution for the provincial-local coordinated control method involving uncertain adjustable resources described in the present invention, the provincial-local coordinated optimization algorithm is constructed based on mixed integer linear programming and supports multi-time scale scheduling; the algorithm optimizes the scheduling strategy by minimizing the cost function including fuel cost, wind power cost, photovoltaic cost and power imbalance penalty.

[0010] As an optimal solution for the provincial and local collaborative control method involving uncertain adjustable resources described in the present invention, the uncertain resource control scheme takes into account both safety and economy, and its objective function includes maximizing reliability and minimizing operating costs; the control scheme realizes the processing and decomposition of instructions through a cloud-edge collaborative architecture, in which the cloud computing center handles the generation of scheduling instructions, and the edge computing unit is responsible for the decomposition of instructions.

[0011] As an optimal solution of the provincial and local coordinated control method involving uncertain adjustable resources described in the present invention, the steps of executing control result verification and feedback include: setting evaluation indicators of resource utilization, cost-effectiveness and environmental impact; evaluating the control effect according to the set indicators; and adjusting the resource control strategy based on the evaluation results.

[0012] As a preferred solution of the provincial and local coordinated control method involving uncertain adjustable resources described in the present invention, wherein: in the uncertain resource model:

[0013] Wind power output Pw (t) is characterized as having mean μ w (t) and variance Normal distribution of photovoltaic output P v The relationship between (t) and solar irradiance G(t) is expressed as P v (t) = η v ·G(t)·A, where η v is the photovoltaic system conversion efficiency, A is the photovoltaic array area; the power transmission P between area i and area j is i,j (t) = β i,j ·P t (t), where β i,j is the inter-region transmission efficiency, P t (t) is the actual transmission power.

[0014] To further solve the above technical problems, the present invention provides the following technical solutions: a system for provincial and local coordinated regulation involving uncertain adjustable resources, comprising: a model building module for quantitatively modeling the uncertainty factors of wind power, photovoltaics and load demand; an algorithm design module for constructing a regulation strategy based on a two-layer optimization framework; a plan formulation module for adjusting resource allocation by combining historical data and real-time predictions; and a verification feedback module for evaluating the regulation effect and adjusting algorithm parameters.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the provincial and local coordinated control method involving uncertain adjustable resources as described above.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the provincial and local coordinated control method involving uncertain adjustable resources as described above are implemented.

[0017] Beneficial effects of the present invention: Through a systematic provincial and local coordinated control method, significant technical improvements have been achieved in many aspects. The model construction module uses a probability distribution method to accurately quantify the random characteristics of wind power and photovoltaic resources. Compared with the traditional simplified model, it improves the characterization accuracy of uncertain resource characteristics and reduces scheduling errors caused by prediction bias. The algorithm design module is based on a two-layer optimization framework, which realizes the hierarchical coordination of provincial power grids and local resources, breaks the limitations of traditional single-layer optimization, and effectively solves the problem of inefficient cross-regional resource allocation. The solution formulation module realizes the rapid processing and decomposition of scheduling instructions through the cloud-edge collaborative architecture, significantly reduces the delay between data processing and decision execution, improves the system's response speed to uncertain changes, and balances the multi-objective requirements of economy and safety. The verification feedback module establishes a comprehensive evaluation mechanism for resource utilization, cost-effectiveness and environmental impact, forms a closed-loop optimization of the control strategy, and overcomes the defects of the traditional system's lack of quantitative evaluation and continuous optimization. Overall, the present invention significantly improves the new energy absorption capacity and power system operation efficiency through the collaborative optimization mechanism, reduces scheduling costs, and enhances the resilience of the power grid to cope with uncertain disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a flow chart of the method of the present invention;

[0020] Figure 2 is a system diagram of the present invention;

[0021] Figure 3 This is a diagram of the computer equipment in the present invention. DETAILED DESCRIPTION

[0022] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Example 1, with reference to Figure 1 , which is an embodiment of the present invention, provides a provincial and local coordinated control method with the participation of uncertain adjustable resources.

[0025] There are some problems in the existing related technologies, which are mainly reflected in the following aspects: First, the traditional power dispatching method is mainly based on conventional power generation methods with strong regulation capabilities such as thermal power and hydropower, which is difficult to effectively deal with the uncertainty and volatility of renewable energy such as wind power and photovoltaics. Secondly, the existing dispatching methods are mostly focused on local areas or single power generation source regulation, ignoring the synergy between provinces and regions, resulting in waste of resources and low dispatching efficiency. Furthermore, the uncertainty modeling of resources such as wind power and photovoltaics is too simplified and fails to accurately characterize their dynamic characteristics and correlation structure. In addition, the control strategy often focuses on single-objective optimization and lacks a multi-objective collaborative optimization mechanism. Finally, the evaluation and feedback mechanism of the control results is imperfect, making it difficult to achieve closed-loop optimization.

[0026] The present invention provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate on how to implement the provincial and local coordinated control method involving uncertain adjustable resources.

[0027] Figure 1 The overall flow chart of a provincial and local coordinated control method involving uncertain adjustable resources is shown, including: S1: constructing an uncertain resource model to quantitatively model the uncertainty factors of wind power, photovoltaic power and load demand;

[0028] In an optional embodiment, modeling uncertain resources can employ classic time series analysis methods, such as the Autoregressive Moving Average (ARMA) or Autoregressive Integrated Moving Average (ARIMA) models, to forecast wind and photovoltaic output. These methods, based on the statistical properties of historical data, can capture data periodicity and trends to a certain extent, but have limited ability to handle random fluctuations.

[0029] In an optional embodiment, machine learning methods, such as support vector machines (SVMs), random forests, or neural networks, can be used to establish a mapping relationship between wind power and photovoltaic output and meteorological factors. These methods can handle nonlinear relationships, but generally require a large amount of training data and have poor model interpretability.

[0030] In an alternative embodiment, load demand modeling can employ load clustering and a typical daily curve approach. Based on the similarity of historical load data, loads are divided into several types and a typical load curve is determined for each type. This approach simplifies the problem but struggles to accurately capture the randomness and volatility of loads.

[0031] In this method, the steps of constructing an uncertainty resource model include: identifying and classifying the resources involved in regulation; quantifying the availability of resources using probability theory methods; and verifying the uncertainty resource model using Monte Carlo simulation based on probability distribution.

[0032] Identifying and classifying the resources involved in regulation is the first step in building a model. Based on their physical properties and regulatory response characteristics, resources can be divided into conventional resources (such as thermal power and hydropower) and uncertain resources (such as wind power and photovoltaics). Uncertain resources can be further subdivided based on the sources and characteristics of their uncertainty. For example, the uncertainty of wind power primarily stems from random variations in wind speed, while the uncertainty of photovoltaics primarily stems from variations in solar irradiance and cloud cover.

[0033] The key to building an accurate model is to quantify the availability of resources using probability theory. In this method, the wind power output P wind Characterized as having mean μ wind and variance Normal distribution of photovoltaic output P solar The relationship with solar irradiance G is expressed as P solar =η·G·A, where η is the conversion efficiency of the photovoltaic system, A is the area of the photovoltaic array; the power transfer P between area i and area j i,j =β i,j ·P trans , where β i,j is the inter-region transmission efficiency, P trans is the actual transmission power.

[0034] Validating uncertain resource models using Monte Carlo simulations based on probability distributions is an important method for ensuring model accuracy. By using large numbers of random samples, we can simulate various possible output scenarios for uncertain resources and assess the model's robustness and predictive accuracy. Monte Carlo simulations can generate probability distributions for resource outputs, providing more comprehensive uncertainty information and a reliable basis for subsequent optimization decisions.

[0035] It's important to note that building an uncertain resource model is the foundation of the entire control method. Through scientific modeling techniques, we can accurately grasp the operating characteristics of uncertain resources such as wind power and photovoltaics, providing reliable data support for subsequent optimized scheduling. Compared with traditional deterministic equivalent or simplified stochastic models, this step uses a probability distribution method for precise quantitative modeling, significantly improving the accuracy of characterizing uncertain resource characteristics, reducing scheduling errors caused by forecast bias, and laying the foundation for improving the economic efficiency and reliability of system operation.

[0036] S2: Design a provincial-local collaborative optimization algorithm and build a control strategy based on a two-layer optimization framework;

[0037] In an optional embodiment, the collaborative optimization algorithm can use distributed optimization methods, such as the alternating direction method of multipliers (ADMM) or Lagrangian relaxation, to achieve coordination between different regions. These methods reduce computational complexity by decomposing large-scale problems into multiple small-scale subproblems, but may suffer from slow convergence or local optimality.

[0038] In an alternative embodiment, the optimization framework can be built based on model predictive control (MPC), which continuously updates prediction information and adjusts the control strategy through rolling optimization. This approach can handle system dynamics, but it has a heavy computational burden and poses challenges in real-time performance.

[0039] In an alternative embodiment, reinforcement learning or other artificial intelligence methods can be used to learn the optimal control strategy through the interaction between the agent and the environment. This method is suitable for highly nonlinear and complex systems, but requires a large amount of training data and computing resources.

[0040] In this method, a two-layer optimization framework includes: the upper layer is the provincial power grid control model, which is responsible for the power balance of the entire network and the allocation of distributed power sources; the lower layer is the local resource allocation model, which is responsible for executing the coordinated control requirements of the upper-level dispatch.

[0041] The upper-level provincial grid control model focuses on a broader regional scope, primarily focusing on power balance and system security across the entire network. This model considers macro factors such as cross-regional power transmission capabilities, system backup capacity requirements, and grid security constraints to formulate a network-wide resource allocation strategy to ensure overall system stability.

[0042] The lower-level local resource allocation model focuses on the fine-tuning of local resources. Based on the goals and tasks assigned by the upper-level model, it optimizes the allocation of local resources, taking into account the characteristics and constraints of local resources. This model considers micro-factors such as the operating characteristics of local devices, environmental constraints, and responsiveness, ensuring that upper-level scheduling instructions are effectively executed.

[0043] The provincial-local collaborative optimization algorithm is built based on mixed integer linear programming and supports multi-time scale scheduling; the algorithm optimizes the scheduling strategy by minimizing the cost function that includes fuel cost, wind power cost, photovoltaic cost and power imbalance penalty.

[0044] Mixed-integer linear programming (MILP) is a powerful mathematical optimization method that can handle complex problems involving both discrete and continuous decision variables. In power system scheduling, many decisions are discrete, such as the start and stop status of a generator, while power output, for example, is continuous. The MILP framework effectively handles this mixed nature and provides a globally optimal solution.

[0045] Multi-timescale scheduling is a key strategy for managing resource uncertainty. Different resources and loads exhibit distinct dynamic characteristics at different timescales. For example, wind power fluctuations may be significant at the minute level, while load variations may be more pronounced at the hourly level. By constructing optimization models that encompass different timescales, we can more comprehensively capture the dynamic characteristics of the system and improve the accuracy and flexibility of scheduling.

[0046] Minimizing the cost function is the core objective of optimal scheduling. The cost function in this method comprehensively considers multiple factors, including fuel costs, wind power costs, photovoltaic costs, and power imbalance penalties. Fuel costs reflect the economic efficiency of conventional power generation, while wind power and photovoltaic costs reflect the cost of utilizing renewable energy. The power imbalance penalty ensures supply and demand balance and stable operation of the system. Through this comprehensive cost function, the algorithm strikes a balance between economy and safety, achieving optimal resource allocation.

[0047] It should be noted that the design of a provincial-regional collaborative optimization algorithm is the core of this method. Based on a two-tiered optimization framework, this design achieves hierarchical coordination between the provincial power grid and local resources, breaking the limitations of traditional single-tier optimization and effectively addressing the inefficient cross-regional resource allocation problem. This algorithm not only considers optimization objectives and constraints at different levels but also improves its adaptability and solution efficiency through advanced methods such as mixed-integer linear programming and multi-time-scale scheduling, ensuring the global optimality and practical feasibility of the scheduling strategy.

[0048] S3: Develop an uncertain resource control plan and adjust resource allocation based on historical data and real-time predictions;

[0049] In an alternative embodiment, the development of a control plan can be based on scenario analysis, which constructs multiple possible future scenarios and formulates targeted control strategies. This approach can account for multiple uncertainties, but may face the challenges of subjectivity in scenario selection and high computational complexity.

[0050] In an optional embodiment, the control scheme can use a robust optimization method to ensure the robustness of the control scheme by considering the worst-case system performance. This method can cope with extreme situations, but may lead to overly conservative decisions and affect the economic efficiency of the system.

[0051] In an alternative embodiment, a decentralized control architecture can be used to distribute control tasks to multiple local controllers, reducing the burden on the central controller. This approach improves system response speed, but may face coordination difficulties and lack of global optimality.

[0052] In this method, the objective functions of the uncertain resource control scheme include maximizing reliability and minimizing operating costs; the control scheme realizes the processing and decomposition of instructions through a cloud-edge collaborative architecture, in which the cloud computing center handles the generation of scheduling instructions and the edge computing unit is responsible for the decomposition of instructions.

[0053] The dual objectives of the objective function reflect the core requirements of power system operation: on the one hand, the system needs to maintain high reliability to ensure secure and stable power supply; on the other hand, the system must pursue economic efficiency and reduce operating costs. These two objectives often conflict. For example, improving reliability may require increasing backup capacity, but this increases operating costs. This method seeks an optimal balance between the two through the rational design of the objective function.

[0054] The cloud-edge collaborative architecture is a key technology for achieving rapid response and precise control. Cloud computing centers, with their powerful computing capabilities and global perspective, can handle complex optimization problems and generate globally optimal scheduling instructions. Edge computing units, located close to the controlled objects, can quickly respond to local changes, decompose, and execute scheduling instructions. This collaborative architecture combines the powerful computing power of cloud computing with the rapid response of edge computing, improving overall system performance.

[0055] Combining historical data with real-time forecasts to adjust resource allocation is an effective strategy for coping with uncertainty. Historical data provides statistical patterns in the long-term operating characteristics of resources, while real-time forecasts reflect the latest resource trends. By combining these two, we can more accurately grasp the real-time status and short-term trends of resources, enabling the development of more precise control plans.

[0056] It's important to note that developing a resource control plan for uncertainty is key to achieving effective control. The cloud-edge collaborative architecture enables rapid processing and decomposition of scheduling instructions, significantly reducing the latency between data processing and decision execution, and improving the system's response to uncertain changes. Furthermore, by combining historical data with real-time predictions, the control plan's adaptability and predictability are enhanced, enabling more accurate prediction of resource change trends and proactive adjustments to avoid significant fluctuations in system operation, thereby improving the stability and reliability of system operation.

[0057] S4: Perform verification and feedback of control results, evaluate control effects and adjust algorithm parameters.

[0058] In an optional embodiment, verification and feedback can be achieved through simple error statistical analysis, such as calculating the mean square error (MSE) or mean absolute error (MAE) between the predicted value and the actual value. This method is simple and intuitive, but may not fully reflect the performance of the system.

[0059] In an optional embodiment, an economic benefit analysis method can also be used to evaluate the effectiveness of the control plan by calculating the economic benefits brought by it. This method directly focuses on economic goals, but may ignore other important factors such as safety and reliability.

[0060] In an optional embodiment, a closed-loop control strategy with fixed parameters can be used to adjust the control input according to the deviation between the output and the target. This method is simple to implement, but it is difficult to adapt to the nonlinear and time-varying characteristics of the system.

[0061] In this method, the steps of performing control result verification and feedback include: setting evaluation indicators of resource utilization, cost-effectiveness and environmental impact; evaluating the control effect according to the set indicators; and adjusting the resource control strategy based on the evaluation results.

[0062] Setting evaluation indicators is the first step in verifying the effectiveness of regulation. Resource utilization reflects the efficiency of resource use. Especially for renewable energy sources like wind power and photovoltaics, high utilization rates indicate greater clean energy absorption capacity. Cost-effectiveness measures reflect the economic viability of the system, including total operating costs and unit power generation costs. Environmental impact indicators focus on the environmental impact of system operations, such as carbon emissions and pollutant emissions. These indicators assess system performance from different perspectives, providing a comprehensive assessment perspective.

[0063] Evaluating control effectiveness against established metrics is a core step in verifying system performance. By collecting and analyzing actual operational data, calculating the actual values of various metrics and comparing them against expected targets, we can assess the effectiveness and deficiencies of the control plan. This evaluation process not only focuses on the final results but also analyzes dynamic performance during the process, such as the system's response speed and stability.

[0064] Adjusting resource control strategies based on evaluation results is key to achieving continuous system optimization. By analyzing deficiencies and issues in the evaluation results, we can tailor algorithm parameters, optimize model structures, and refine control strategies. This feedback-based adjustment forms a closed-loop optimization mechanism, enabling the system to continuously learn and improve, adapting to changing external and internal conditions.

[0065] It's important to note that the verification and feedback of control results form a closed-loop optimization mechanism for the system. By establishing a comprehensive evaluation index system, the system can objectively assess all aspects of control effectiveness, including technical performance, economic benefits, and environmental impact. Strategy adjustments based on these evaluation results enable the system to continuously learn and optimize, adapting to the dynamic changes in uncertain resource characteristics and improving its long-term operational performance. This closed-loop optimization mechanism overcomes the shortcomings of traditional systems, which lack quantitative evaluation and continuous optimization, significantly enhancing the system's adaptability and operational stability.

[0066] Furthermore, the steps of constructing the uncertainty resource model include: identifying and classifying the resources involved in regulation; quantifying the availability of resources using probability theory methods; and verifying the uncertainty resource model using Monte Carlo simulation based on probability distribution.

[0067] Furthermore, the two-layer optimization framework includes: the upper layer is the provincial power grid control model, which is responsible for the power balance of the entire network and the allocation of distributed power sources; the lower layer is the local resource allocation model, which is responsible for executing the coordination and control requirements of the upper-level dispatch.

[0068] Furthermore, the provincial and local collaborative optimization algorithm is constructed based on mixed integer linear programming and supports multi-time scale scheduling; the algorithm optimizes the scheduling strategy by minimizing the cost function that includes fuel cost, wind power cost, photovoltaic cost and power imbalance penalty.

[0069] Furthermore, the uncertain resource control scheme takes into account both security and economy, and its objective functions include maximizing reliability and minimizing operating costs; the control scheme realizes the processing and decomposition of instructions through a cloud-edge collaborative architecture, in which the cloud computing center handles the generation of scheduling instructions and the edge computing unit is responsible for the decomposition of instructions.

[0070] Furthermore, the steps of executing control result verification and feedback include: setting evaluation indicators of resource utilization, cost-effectiveness and environmental impact; evaluating the control effect according to the set indicators; and adjusting the resource control strategy based on the evaluation results.

[0071] Furthermore, in the uncertainty resource model:

[0072] Wind power output P w (t) is characterized as having mean μw (t) and variance Normal distribution;

[0073] Photovoltaic output P v The relationship between (t) and solar irradiance G(t) is expressed as P v (t) = η v ·G(t)·A, where η v is the conversion efficiency of the photovoltaic system, A is the photovoltaic array area;

[0074] Power transfer P between area i and area j i,j (t) = β i,j ·P t (t), where β i,j is the inter-region transmission efficiency, P t (t) is the actual transmission power.

[0075] This method achieves significant technical improvements in multiple areas through a systematic provincial-regional coordinated control approach. The model construction module employs a probabilistic distribution approach to accurately quantify the stochastic characteristics of wind and photovoltaic resources. Compared to traditional simplified models, this improves the accuracy of characterizing uncertain resource characteristics and reduces scheduling errors caused by forecast bias. The algorithm design module, based on a two-layer optimization framework, achieves hierarchical coordination between provincial power grids and local resources, breaking the limitations of traditional single-layer optimization and effectively addressing the inefficient cross-regional resource allocation. The plan formulation module utilizes a cloud-edge collaborative architecture to rapidly process and decompose dispatch instructions, significantly reducing the latency between data processing and decision execution, improving the system's responsiveness to uncertain changes, and balancing the multi-objective requirements of economic efficiency and safety. The verification and feedback module establishes a comprehensive assessment mechanism for resource utilization, cost-effectiveness, and environmental impact, forming a closed-loop optimization of the control strategy, overcoming the shortcomings of traditional systems lacking quantitative evaluation and continuous optimization. Overall, this method, through its collaborative optimization mechanism, significantly improves the renewable energy absorption capacity and power system operational efficiency, reduces dispatch costs, and enhances the grid's resilience to uncertain disturbances.

[0076] In summary, existing uncertain resource control technologies face key challenges such as low provincial and local coordination efficiency, insufficient inter-regional resource coordination, and a single control strategy. There is an urgent need to develop more effective coordinated control methods. This proposed provincial and local coordinated control method involving uncertain adjustable resources effectively addresses key issues in existing technologies through a two-layer optimization framework, a dynamic response mechanism, and a cloud-edge collaborative architecture. At the same time, it can improve resource utilization efficiency, enhance system flexibility and adaptability, promote the efficient consumption of renewable energy, and reduce power system operating costs, providing technical support for the construction of a high-proportion renewable energy power system.

[0077] Example 2, reference Figure 2 , as an embodiment of the present invention, provides a provincial and local coordinated control system with the participation of uncertain adjustable resources, including:

[0078] The model building module is used to quantitatively model the uncertainties of wind power, photovoltaic power generation, and load demand. It characterizes the uncertainty characteristics of resources through probability distribution and mathematical models, providing a foundation for subsequent optimization. It can identify and classify uncertain resources, quantify resource availability using probabilistic methods, construct mathematical models for wind power, photovoltaic power generation, and other resources, and verify these models using Monte Carlo simulation.

[0079] The algorithm design module is used to construct control strategies based on a two-layer optimization framework. The upper layer is the provincial power grid control model, and the lower layer is the local resource allocation model. Multi-timescale scheduling is achieved through mixed integer linear programming. It can build a two-layer optimization framework, design a provincial and local resource coordination mechanism, implement an optimization algorithm based on mixed integer linear programming, and construct a cost function optimization scheduling strategy.

[0080] The plan-making module combines historical data with real-time predictions to adjust resource allocation. It uses a cloud-edge collaborative architecture to process and decompose instructions, balancing system security and cost-effectiveness. This module integrates historical data and real-time prediction information to develop optimized resource allocation plans. It leverages the cloud-edge collaborative architecture to process scheduling instructions, balancing system security and cost-effectiveness.

[0081] The verification and feedback module is used to evaluate control effects and adjust algorithm parameters. By setting evaluation metrics, analyzing control results, and providing feedback, a closed-loop optimization system is formed. This module can set evaluation metrics such as resource utilization and cost-effectiveness, analyze the effectiveness of control solutions, generate optimization feedback, and adjust algorithm parameters and control strategies.

[0082] Example 3, reference Figure 3 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0083] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0084] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0085] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] It is important to note that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A provincial and local coordinated control method involving uncertain adjustable resources, characterized in that: include: Construct an uncertainty resource model to quantitatively model the uncertainty factors of wind power, photovoltaic power and load demand; Design a provincial-local collaborative optimization algorithm and build a control strategy based on a two-layer optimization framework; Develop uncertain resource control plans and adjust resource allocation based on historical data and real-time forecasts; Perform control result verification and feedback, evaluate control effects and adjust algorithm parameters.

2. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 1, characterized in that: The steps of constructing the uncertainty resource model include: identifying and classifying the resources involved in regulation; quantifying the availability of resources using probability theory methods; and verifying the uncertainty resource model using Monte Carlo simulation based on probability distribution.

3. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 2, characterized in that: The two-layer optimization framework includes: the upper layer is the provincial power grid control model, which is responsible for the power balance of the entire network and the allocation of distributed power sources; the lower layer is the local resource allocation model, which is responsible for executing the coordination and control requirements of the upper-level dispatch.

4. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 3, characterized in that: The provincial-region collaborative optimization algorithm is built based on mixed integer linear programming and supports multi-time scale scheduling. The algorithm optimizes the scheduling strategy by minimizing a cost function that includes fuel cost, wind power cost, photovoltaic cost, and power imbalance penalty.

5. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 4, characterized in that: The objective functions of the uncertain resource control scheme include maximizing reliability and minimizing operating costs; the control scheme realizes the processing and decomposition of instructions through a cloud-edge collaborative architecture, in which the cloud computing center handles the generation of scheduling instructions and the edge computing unit is responsible for the decomposition of instructions.

6. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 5, characterized in that: The steps of executing control result verification and feedback include: setting evaluation indicators of resource utilization, cost-effectiveness and environmental impact; evaluating the control effect according to the set indicators; and adjusting the resource control strategy based on the evaluation results.

7. The provincial and local coordinated control method involving uncertain adjustable resources according to claim 6, characterized in that: In the uncertainty resource model: Wind power output P w (t) is characterized as having mean μ w (t) and variance Normal distribution; Photovoltaic output P v The relationship between (t) and solar irradiance G(t) is expressed as P v (t) = η v ·G(t)·A, where η v is the conversion efficiency of the photovoltaic system, A is the photovoltaic array area; Power transfer P between area i and area j i,j (t) = β i,j ·P t (t), where β i,j is the inter-region transmission efficiency, P t (t) is the actual transmission power.

8. A system using the provincial and local coordinated control method involving uncertain adjustable resources as described in any one of claims 1 to 7, characterized in that: include: Model building module, used to quantitatively model the uncertainties of wind power, photovoltaic power and load demand; Algorithm design module, used to build control strategies based on a two-layer optimization framework; A plan-making module, used to adjust resource allocation by combining historical data with real-time forecasts; Verification feedback module, used to evaluate the regulation effect and adjust the algorithm parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the provincial and local coordinated control method involving uncertain adjustable resources according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the provincial and local coordinated control method involving uncertain adjustable resources according to any one of claims 1 to 7 are implemented.

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