High-energy-consumption load resource adjusting method based on virtual power plant

Abstract

The invention relates to the technical field of power load management, and discloses a high-energy-consumption load resource adjustment method based on a virtual power plant, which comprises the following steps: step 1, describing the operation behavior of each load resource in the virtual power plant by constructing a high-energy-consumption load resource dynamic scheduling model of the virtual power plant, the relation between state variables and control input in the load adjustment process is established through corresponding dynamic equations, and the high-energy-consumption load resource dynamic scheduling model enables the real-time state and control parameters of load adjustment to be described when the operation process of each load resource is modeled. By introducing an optimal control theory and a scheduling optimization algorithm, combining with a penalty term of power output change, the adjustment process of a high-energy-consumption load is optimized, frequent fluctuation is avoided, and by punishing power change, load fluctuation is reduced, load adjustment is achieved, and the effect of improving the stability of a power grid is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power load management, and in particular to a high energy consumption load resource regulation method based on a virtual power plant. Background Art

[0002] The transformation of energy structure and the large-scale access of renewable energy have led to severe load fluctuations in the power grid. As a system integrating multiple distributed energy sources, energy storage equipment and load resources, the virtual power plant aims to balance the load of the power grid through intelligent scheduling optimization and improve the regulation capacity and stability of the power grid. In particular, high-energy-consuming load resources, as an important part of the virtual power plant, pose great challenges to scheduling due to their high volatility and impact on the power grid.

[0003] Most current load regulation methods rely on scheduling strategies based on experience or simple rules. These methods often suffer from low scheduling accuracy, high scheduling delays, and large load fluctuations when dealing with large-scale, high-energy-consuming load regulation. In addition, traditional load resource scheduling methods lack precise control over equipment start and stop. Frequent start and stop operations affect the stability of the power system, increase equipment maintenance costs, and increase energy efficiency losses.

[0004] Existing studies have attempted to dispatch load resources through optimization algorithms and control theories, but most methods still cannot meet the requirements of real-time performance, accuracy, and system stability when faced with complex power grid environments and large-scale load resources. Therefore, how to improve the regulation efficiency of high-energy-consuming load resources and the stability of the power grid through more advanced control theories, optimization algorithms, and real-time dispatching technologies has become an important technical issue that needs to be solved in the current field of virtual power plants.

[0005] The present invention introduces optimal control theory and scheduling optimization algorithm, combines mixed integer programming algorithm and parallel computing technology, and proposes a high-energy-consuming load resource regulation method based on virtual power plant to solve the above-mentioned problems. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a high-energy-consuming load resource regulation method based on a virtual power plant to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A high energy consumption load resource adjustment method based on a virtual power plant, comprising:

[0008] Step 1: By constructing a dynamic scheduling model for high-energy-consuming load resources in a virtual power plant, describe the operating behaviors of each load resource in the virtual power plant, and establish the relationship between state variables and control inputs during the load regulation process through corresponding dynamic equations. The dynamic scheduling model for high-energy-consuming load resources enables the modeling of the operating processes of each load resource to describe the real-time state and control parameters of load regulation.

[0009] Step 2: Define the scheduling optimization objective and set the constraint conditions according to the load resource scheduling data obtained from the dynamic scheduling model for high-energy-consuming load resources.

[0010] Step 3: Based on the scheduling optimization objective and set constraint conditions, use a control algorithm to optimize the scheduling of load resources. The control algorithm adjusts the power output of load resources to minimize the scheduling cost of the virtual power plant. The control algorithm considers the switch states of load devices and optimizes the start-stop operations of load resources through mixed-integer programming.

[0011] Step 4: Analyze the defined scheduling model using variational methods and optimal control theory to obtain the optimal scheduling strategy. By solving the optimization equation, obtain the power output and switch states of each load resource at different time steps to provide an operating plan for the virtual power plant scheduling.

[0012] Step 5: Conduct a stability analysis on the obtained scheduling scheme, and analyze the stability of the scheduling system using Lyapunov stability theory.

[0013] Step 6: Combine parallel computing technology to accelerate the optimization process. Through a real-time data acquisition system, dynamically adjust the power output of each high-energy-consuming load resource in the virtual power plant according to the load fluctuation of the power grid to achieve efficient load regulation.

[0014] Preferably, in Step 2, the scheduling optimization objective of the load resource is expressed as:

[0015]

[0016] where N is the total number of high-energy-consuming load resources in the virtual power plant,

[0017] T is the number of time periods considered during the scheduling process,

[0018] C i (P i (t)) represents the scheduling cost of the i-th load resource at time t,

[0019] P i (t) represents the power output of the i-th load resource at time y,

[0020] λ i is the penalty factor, μi is the cost of the start-stop operation of the i-th load resource,

[0021] |P i (t) - P i (t - 1)| represents the absolute value of the change in the power output of the i-th load resource between time t and t - 1,

[0022] z i (t) is the switch state of the i-th load resource at time t.

[0023] Preferably, the constraint conditions for the scheduling optimization include:

[0024] Grid power balance constraint:

[0025] where P i (t) represents the power output of the i-th load resource at time t, N is the total number of high-energy-consuming load resources in the virtual power plant, and P grid (t) is the power demand of the power grid at time t, represents for time point t;

[0026] Load regulation capacity constraint:

[0027] where P min.i and P max.i are the minimum and maximum power outputs of the i-th load resource, and P i (t) represents the power output of the i-th load resource at time t, for all load resources i and time t.

[0028] Preferably, in step 3, the control algorithm uses the optimal control theory for scheduling optimization, and the optimal control equation is expressed as:

[0029]

[0030] where L is the Lagrangian function and B is the bias term,

[0031] P i (t) represents the power output of the i-th load resource at time t,

[0032] z i (t) represents the switch state of the i-th load resource at time t,

[0033] If z i (t) = 1, then the load resource is turned on,

[0034] If z i (t) = 0, then the load resource is turned off.

[0035] Preferably, the scheduling algorithm uses mixed integer programming to optimize the start-stop operation of load resources, and the constraint conditions of the start-stop operation are expressed as:

[0036]

[0037] where z i (t) represents the on-off state of the i-th load resource at time t.

[0038] If z i (t) = 1, the load resource is turned on,

[0039] If z i (t) = 0, the load resource is turned off.

[0040] Preferably, the high-energy-consuming load resource regulation method includes using the variational method and the optimal control theory to analyze the defined scheduling model to obtain the optimal scheduling strategy, and the optimal scheduling strategy is expressed by solving the optimization equation:

[0041] P i (t) = f i (P previous (t), z i (t)),

[0042] where P previous (t) represents the power output of the previous time step, z i (t) represents the on-off state of the i-th load resource at time t, and f i (·) is the optimal scheduling strategy obtained according to the dynamic model of load regulation.

[0043] Preferably, in step 5, the scheduling scheme is analyzed by the Lyapunov stability theory to ensure that the system will not become unstable during the load regulation process, and the Lyapunov function is defined as:

[0044]

[0045] where P ref.i is the target power output, P i (t) represents the power output of the i-th load resource at time t, V(P 1 (t), …, P N (t)) represents the Lyapunov function, and N is the total number of high-energy-consuming load resources in the virtual power plant.

[0046] Preferably, the high-energy-consuming load resource regulation method solves the optimal scheduling by combining the gradient descent method and dynamic programming to obtain the optimal scheduling strategies of each load resource, and the optimal scheduling strategies are expressed as:

[0047]

[0048] Among them, is the gradient of the Lagrangian function, and δP i (t) represents the adjustment made to the power output of the load resources at each time step. For all load resources i and time t.

[0049] Preferably, the high-energy-consuming load resource regulation method improves the calculation efficiency through parallel computing technology, and performs parallel optimization on the scheduling of each load resource. The calculation process of the parallel optimization is carried out through the following formula:

[0050]

[0051] Among them, is the total optimization goal, is the parallel calculation performed on the i-th load resource at time t. N is the total number of high-energy-consuming load resources in the virtual power plant, and P i (t) represents the power output of the i-th load resource at time t. represents for time point t.

[0052] Preferably, the high-energy-consuming load resource regulation method includes a real-time data acquisition and feedback control system. The real-time data acquisition and feedback control system monitors the power grid load fluctuation in real time and dynamically adjusts the power output of each high-energy-consuming load resource in the virtual power plant. The dynamic adjustment process is controlled by the following formula:

[0053] P i (t) = Adjust(P current (t), P target (t)),

[0054] Among them, P current (t) is the power output of the load resource i at the current moment, P target (t) is the target power output, Adjust(·) is the control function for adjusting the load output according to real-time data, and P i (t) represents the power output of the i-th load resource at time t.

[0055] The present invention provides a high-energy-consuming load resource regulation method based on a virtual power plant. It has the following beneficial effects:

[0056] 1. By introducing the optimal control theory and scheduling optimization algorithm, and combining with the penalty term of power output change, the present invention optimizes the regulation process of high-energy-consuming loads, avoids frequent fluctuations, and reduces load fluctuations through the penalty on power change, thereby achieving load regulation and obtaining the effect of improving the stability of the power grid.

[0057] 2. By adopting the mixed-integer programming algorithm, the present invention optimizes the start-stop operations of load resources, ensures the coordination between the switch states of load devices and the regulation strategy, reduces unnecessary start-stop operations, and improves the operating efficiency of the virtual power plant by reasonably arranging the start-stop of load resources, thereby obtaining the effects of reducing frequent start-stop of equipment and extending the service life of equipment.

[0058] 3. By combining parallel computing technology, the present invention improves the computational efficiency of the scheduling optimization process, ensures that the virtual power plant can adjust load resources in real time when the power grid load fluctuates, and improves the response speed of the system by accelerating the scheduling process, thereby obtaining the effect of being able to quickly respond to power grid load changes and maintain power grid balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] The present invention will be described in detail below with reference to the accompanying drawings:

[0062] Embodiment:

[0063] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for regulating high-energy-consuming load resources based on a virtual power plant, including:

[0064] Step 1. By constructing a dynamic scheduling model of high-energy-consuming load resources in a virtual power plant, the operation behaviors of each load resource in the virtual power plant are described, and the relationship between state variables and control inputs during the load regulation process is established through corresponding dynamic equations. The dynamic scheduling model of high-energy-consuming load resources enables the real-time state and control parameters of load regulation to be described when modeling the operation process of each load resource.

[0065] Step 2. Define the scheduling optimization objective and set the constraint conditions according to the load resource scheduling data obtained from the dynamic scheduling model of high-energy-consuming load resources.

[0066] Step 3: Set constraints based on the scheduling optimization goal, and use a control algorithm to optimize the scheduling of load resources. The control algorithm adjusts the power output of load resources to minimize the scheduling cost of the virtual power plant. The control algorithm considers the switch states of load devices and optimizes the start-stop operations of load resources through mixed-integer programming.

[0067] Step 4: Analyze the defined scheduling model using variational methods and optimal control theory to obtain the optimal scheduling strategy. By solving the optimization equations, obtain the power output and switch states of each load resource at different time steps, providing an operation plan for the virtual power plant scheduling.

[0068] Step 5: Conduct a stability analysis of the obtained scheduling scheme, and analyze the stability of the scheduling system using Lyapunov stability theory.

[0069] Step 6: Combine parallel computing technology to accelerate the optimization process. Through a real-time data acquisition system, dynamically adjust the power output of each high-energy-consuming load resource in the virtual power plant according to the load fluctuation of the power grid to achieve efficient load regulation.

[0070] Benefits of Step 1: By constructing a dynamic scheduling model, this step enables the virtual power plant to describe the operating behaviors and regulation characteristics of each high-energy-consuming load resource. Use dynamic equations to establish the relationship between state variables and control inputs during the load regulation process to ensure real-time scheduling and precise control in a complex power grid environment, improving the reliability and accuracy of scheduling. This step provides a modeling basis for subsequent scheduling optimization and can flexibly respond to various load regulation requirements.

[0071] Benefits of Step 2: By defining the scheduling optimization goal and setting constraints based on the scheduling data obtained from the model, ensure that the scheduling goal matches the actual demand, avoiding over-regulation and situations where the power grid demand cannot be met. This step ensures that the load resource regulation can be carried out within a reasonable power range and at the same time meets the power balance requirements of the power grid, providing guarantee for the stable operation of the power grid.

[0072] Benefits of Step 3: The introduction of the control algorithm minimizes the scheduling cost of the virtual power plant by adjusting the power output of load resources, while meeting the load demand and stability requirements of the power grid. The mixed-integer programming algorithm is used to optimize the start-stop operations of load resources, avoiding frequent start-stop of equipment, reducing operating costs, and improving the usage efficiency of equipment. The implementation of this step can maximize economic benefits while reducing unnecessary energy consumption and equipment losses.

[0073] Benefits of Step 4: The variational method and optimal control theory can conduct in-depth analysis of the scheduling model to find the optimal scheduling strategy that stabilizes the power grid and meets the load demand. By solving the optimization equation, the optimal power output and switching state of each load resource at different time points are obtained, ensuring the optimality of the scheduling result. It provides a basis for scheduling decisions for the virtual power plant and optimizes the resource allocation of the system.

[0074] Benefits of Step 5: Through the stability analysis of the scheduling scheme, the Lyapunov stability theory is used to ensure that the system can operate stably during load regulation, avoiding excessive fluctuations and unstable power grid frequency during the load regulation process. This step provides theoretical support for the system to ensure the safety and stability of the regulation process of high-energy-consuming load resources and guarantee the long-term stable operation of the power grid.

[0075] Benefits of Step 6: Combining parallel computing technology, this step significantly improves the computational efficiency of the scheduling optimization process, enabling the virtual power plant to respond quickly to power grid load fluctuations. The real-time data acquisition system ensures the dynamic monitoring of the power grid load fluctuation situation and precisely regulates each high-energy-consuming load resource in the virtual power plant. It improves the system response speed and can ensure the load balance and stability of the power grid.

[0076] In Step 2, the scheduling optimization objective of the load resource is expressed as:

[0077]

[0078] where N is the total number of high-energy-consuming load resources in the virtual power plant,

[0079] T is the number of time periods considered in the scheduling process,

[0080] C i (P i (t)) represents the scheduling cost of the i-th load resource at time t,

[0081] P i (t) represents the power output of the i-th load resource at time y,

[0082] λ i is the penalty factor, and μ i is the cost of starting and stopping the i-th load resource,

[0083] |P i (t)-P i (t - 1)| represents the absolute value of the change in the power output of the i-th load resource between time t and t - 1,

[0084] z i (t) is the switching state of the i-th load resource at time t.

[0085] The constraint conditions for dispatch optimization include:

[0086] Grid power balance constraint:

[0087] Among them, P i (t) represents the power output of the i-th load resource at time t, N is the total number of high-energy-consuming load resources in the virtual power plant, and P grid (t) is the power demand of the power grid at time t, represents for time point t;

[0088] Load regulation capacity constraint:

[0089] Among them, P min.i and P max.i are the minimum and maximum power outputs of the i-th load resource, and P i (t) represents the power output of the i-th load resource at time t, for all load resources i and time t.

[0090] By defining the dispatch optimization objective function, this step systematizes the dispatch costs and constraint conditions of all high-energy-consuming load resources in the virtual power plant, providing a mathematical basis for the subsequent dispatch algorithm. The objective function combines factors such as dispatch costs, load fluctuation degree, and start-stop operation costs, comprehensively considering economic benefits and system stability to ensure that the dispatch strategy can balance costs and grid demands.

[0091] By introducing a penalty factor, especially the absolute value penalty for load output changes, this step can effectively reduce the frequent fluctuations during the regulation process of load resources, avoiding the grid instability problems caused by frequent fluctuations in traditional dispatch methods. Ensure the frequency stability of the power grid and improve the regulation accuracy of load resources.

[0092] In this step, by introducing the start-stop operation costs of load resources, the start and stop of load equipment are reasonably arranged to reduce unnecessary start-stop operations. A reasonable start-stop arrangement improves the operating efficiency of the virtual power plant, reduces equipment losses caused by frequent start and stop, and extends the service life of the equipment, improving the economy of the virtual power plant.

[0093] While defining the dispatch objective, this step ensures the constraint conditions during the dispatch optimization process, such as grid power balance and load regulation capacity constraints. The grid power balance constraint ensures that the power output of load resources is consistent with the power grid load demand, avoiding problems of overloading and underloading; the load regulation capacity constraint ensures that the power output of each load resource is always within the adjustable range, avoiding adjustments that exceed the physical limits of the equipment, and ensuring the safety and feasibility of dispatch operations.

[0094] In step 3, the control algorithm uses the optimal control theory for scheduling optimization, and the optimal control equation is expressed as:

[0095]

[0096] where L is the Lagrangian function and B is the bias term,

[0097] P i (t) represents the power output of the i-th load resource at time t,

[0098] z i (t) represents the switching state of the i-th load resource at time t,

[0099] If z i (t) = 1, the load resource is turned on,

[0100] If z i (t) = 0, the load resource is turned off.

[0101] The scheduling algorithm uses mixed-integer programming to optimize the start-stop operations of load resources, and the constraint conditions for the start-stop operations are expressed as:

[0102]

[0103] where z i (t) represents the switching state of the i-th load resource at time t.

[0104] If z i (t) = 1, the load resource is turned on,

[0105] If z i (t) = 0, the load resource is turned off.

[0106] By adopting the optimal control theory and combining with the Lagrangian function to optimize the scheduling process, the power output of each load resource in the virtual power plant can be accurately adjusted. The optimal control equation can ensure the balance of the grid load while maximizing the economic benefits of the power system by optimizing the power output of each load resource. Compared with the traditional scheduling method, the optimal control algorithm can achieve accurate load regulation, improve the grid stability and reduce energy waste.

[0107] In this step, the start-stop operations of load resources are optimized by introducing the mixed-integer programming algorithm. The reasonable arrangement of the start-stop operations can reduce the frequent start-stop of load equipment, reduce the energy loss and wear during equipment startup, and extend the service life of the equipment. By optimizing the switching state, the operating cost of the virtual power plant can be reduced, and at the same time, the overall operating efficiency of the system can be improved, avoiding the resource waste caused by frequent start-stop in the traditional method.

[0108] Through the optimization of the switch state by the control algorithm, the reasonable adjustment of each load resource in different time periods can be achieved, avoiding the over-adjustment and non-adjustability of the load resources. Especially during peak load periods, reasonably adjusting the switch state of the load equipment can balance the load demand and the grid capacity, and improve the adjustment ability of the grid.

[0109] The regulation method for high-energy-consuming load resources includes using the variational method and the optimal control theory to analyze the defined scheduling model to obtain the optimal scheduling strategy. The optimal scheduling strategy is expressed by solving the optimization equation as follows:

[0110] P i (t) = f i (P previous (t), z i (t)),

[0111] where P previous (t) represents the power output of the previous time step, and z i (t) represents the switch state of the i-th load resource at time t, and f i (·) is the optimal scheduling strategy obtained according to the dynamic model of load regulation.

[0112] By applying the variational method and the optimal control theory, the optimal scheduling strategy for high-energy-consuming load resources in the virtual power plant can be accurately derived mathematically. This method ensures the grid load balance by minimizing the scheduling error and energy loss, while reducing the fluctuations and instabilities in the scheduling process. The optimal scheduling strategy can accurately calculate the power output and switch state of each load resource at different time steps, providing an efficient load regulation scheme for the grid.

[0113] By introducing the power output P previous (t) of the previous time step and the switch state z i (t) of the current time step, dynamic adjustment and real-time optimization are realized. The optimal scheduling strategy considers the state of the current load resources and synthesizes the past power output, making the load regulation process have good continuity and adaptability, and being able to respond to the grid load fluctuations in real time. The dynamic modeling and optimization enable the virtual power plant to efficiently handle complex and variable load demands.

[0114] By solving the optimal scheduling equation, it can be ensured that the power output and switch state of each load resource in the scheduling process meet the requirements of the grid, while avoiding excessive fluctuations of the load resources and frequent start-stop of the equipment. The regulation ability of the system is improved, the stable operation of the grid is ensured, and the problem of grid frequency instability caused by excessive fluctuations is reduced. In addition, the optimal scheduling strategy helps to improve the operation efficiency of the virtual power plant and reduce the scheduling cost.

[0115] In step 5, the scheduling scheme is analyzed through the Lyapunov stability theory to ensure that the system will not become unstable during the load regulation process. The Lyapunov function is defined as:

[0116]

[0117] where P ref.i is the target power output, P i (t) represents the power output of the i-th load resource at time t, and V(P 1 (t), …, P N (t)) represents the Lyapunov function, and N is the total number of high-energy-consuming load resources in the virtual power plant.

[0118] The present invention analyzes the scheduling scheme through the Lyapunov stability theory to ensure that the virtual power plant will not become unstable during load regulation. By defining the Lyapunov function, the stability of the power grid system can be analyzed and monitored in real time, avoiding the instability of the power grid due to excessive fluctuations and unreasonable regulation strategies. This method provides stability guarantee and provides digital support for the scheduling scheme during the load regulation process, thereby enhancing the reliability of the virtual power plant and the security of the power grid. Through this step, the system can maintain efficient and stable operation in the face of uncertainty and load fluctuations.

[0119] The high-energy-consuming load resource regulation method solves the optimal scheduling by combining the gradient descent method and dynamic programming to obtain the optimal scheduling strategy for each load resource. The optimal scheduling strategy is expressed as:

[0120]

[0121] where is the gradient of the Lagrangian function, and δP i (t) represents the adjustment of the power output of the load resource at each time step, for all load resources i and time t.

[0122] The present invention effectively solves the optimal scheduling problem of high-energy-consuming load resource regulation in the virtual power plant by combining the gradient descent method and dynamic programming. The gradient descent method adjusts the power output by optimizing the gradient of the objective function, and dynamic programming ensures the global optimal solution. The combination of the two can improve the efficiency of the scheduling process, enhance the scheduling accuracy and stability of the system, and at the same time, can quickly respond to the power grid load fluctuations to ensure the efficient and stable operation of the virtual power plant. It has significant advantages in the face of complex power grid load fluctuations and diverse demands, providing optimization support for the operation of the virtual power plant.

[0123] The high-energy-consuming load resource regulation method improves the calculation efficiency through parallel computing technology, performs parallel optimization on the scheduling of each load resource, and the calculation process of the parallel optimization is carried out through the following formula:

[0124]

[0125] Among them, is the total optimization goal, is the parallel calculation for the i-th load resource at time t, N is the total number of high-energy-consuming load resources in the virtual power plant, and P i (t) represents the power output of the i-th load resource at time t, represents for time point t.

[0126] By adopting parallel computing technology, the present invention effectively solves the problem of low calculation efficiency of the traditional load resource scheduling method when facing large-scale power grids and high-energy-consuming load regulation. The parallel optimization speeds up the scheduling process, shortens the scheduling time, and improves the resource allocation efficiency. Especially when dealing with power grid load fluctuations, it can achieve real-time response. By optimizing the scheduling of multiple load resources simultaneously, the system can quickly adapt to the changes in the power grid load, ensuring the load balance and stability of the power grid. This method provides an efficient and flexible scheduling scheme for the virtual power plant, significantly improving the operation efficiency and response speed of the system.

[0127] The high-energy-consuming load resource regulation method includes a real-time data acquisition and feedback control system. The real-time data acquisition and feedback control system monitors the power grid load fluctuations in real time and dynamically adjusts the power output of each high-energy-consuming load resource in the virtual power plant according to the real-time load data. The dynamic adjustment process is controlled through the following formula:

[0128] P i (t) = Adjust(P current (t), P target (t)),

[0129] Among them, P current (t) is the power output of the load resource i at the current moment, P target (t) is the target power output, Adjust(·) is a control function for adjusting the load output according to real-time data, and P i (t) represents the power output of the i-th load resource at time t.

[0130] By introducing a real-time data acquisition and feedback control system, the present invention can significantly improve the real-time response ability and regulation accuracy of the virtual power plant during the load regulation process. It monitors the power grid load fluctuations in real time and dynamically adjusts the power output of the load resources according to the real-time data to ensure the stable operation of the power grid and effectively avoid the problem of load imbalance. The feedback control mechanism enables flexible and precise load regulation, improves the operation efficiency and system stability of the virtual power plant, and provides support for the smooth operation of the power system.

[0131] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for regulating high energy consumption load resources based on a virtual power plant, characterized in that: include: Step 1: By constructing a dynamic scheduling model of high-energy-consuming load resources of a virtual power plant, the operation behavior of each load resource in the virtual power plant is described, and the relationship between the state variable and the control input in the load regulation process is established through the corresponding dynamic equation. The dynamic scheduling model of high-energy-consuming load resources enables the real-time state and control parameters of load regulation to be described when modeling the operation process of each load resource; Step 2: Based on the load resource scheduling data obtained by the high-energy-consuming load resource dynamic scheduling model, define the scheduling optimization target and set the constraint conditions; Step 3: Set constraints based on the dispatch optimization goal, and use a control algorithm to optimize the dispatch of load resources. The control algorithm adjusts the power output of load resources to minimize the dispatch cost of the virtual power plant. The control algorithm considers the on / off state of the load equipment and optimizes the start / stop operation of the load resources through mixed integer programming. Step 4: Use the variational method and optimal control theory to analyze the defined dispatch model and obtain the optimal dispatch strategy. By solving the optimization equation, the power output and switch state of each load resource at different time steps are obtained to provide an operation plan for the virtual power plant dispatch. Step 5: Perform stability analysis on the obtained scheduling scheme, and use Lyapunov stability theory to analyze the stability of the scheduling system; Step 6: Combine parallel computing technology to accelerate the optimization process. Through the real-time data acquisition system, dynamically adjust the power output of each high-energy-consuming load resource in the virtual power plant according to the load fluctuation of the power grid to achieve efficient load regulation.

2. According to claim 1, a high energy consumption load resource adjustment method based on a virtual power plant is characterized in that: In step 2, the scheduling optimization target of the load resources is expressed as: Where N is the total number of high energy consumption load resources in the virtual power plant, T is the number of time periods considered in the scheduling process, C i (P i (t)) represents the scheduling cost of the i-th load resource at time t, P i (t) represents the power output of the i-th load resource at time y, λ i is the penalty factor, μ i is the cost of starting and stopping the i-th load resource, |P i (t)-P i (t-1)| represents the absolute value of the power output change of the i-th load resource between time t and t-1, z i (t) is the switching state of the i-th load resource at time t.

3. According to claim 2, a high energy consumption load resource adjustment method based on a virtual power plant is characterized in that: The constraints of the scheduling optimization include: Grid power balance constraints: Among them, P i (t) represents the power output of the i-th load resource at time t, N is the total number of high-energy-consuming load resources in the virtual power plant, and P grid (t) is the power demand of the power grid at time t, It is used for time point t; Load regulation capability constraint: P min.i ≤P i (t)≤P max.i , Among them, P min.i and P max.i is the minimum and maximum power output of the ith load resource, P i (t) represents the power output of the i-th load resource at time t, For all load resources i and time t.

4. According to claim 1, a method for regulating high energy consumption load resources based on a virtual power plant is characterized in that: In step 3, the control algorithm uses the optimal control theory to perform scheduling optimization, and the optimal control equation is expressed as: Among them, L is the Lagrangian function, B is the bias term, P i (t) represents the power output of the i-th load resource at time t, z i (t) represents the switch state of the i-th load resource at time t, If i (t) = 1, the load resource is turned on, If i (t) = 0, the load resource is closed.

5. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 4, characterized in that: The scheduling algorithm uses mixed integer programming to optimize the start and stop operations of load resources. The constraints of the start and stop operations are expressed as: Among them, z i (t) represents the switch status of the i-th load resource at time t. If i (t) = 1, the load resource is turned on, If i (t) = 0, the load resource is closed.

6. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 1, characterized in that: The high energy consumption load resource regulation method includes analyzing the defined scheduling model using the variational method and the optimal control theory to obtain the optimal scheduling strategy, which is expressed as follows by solving the optimization equation: P i (t)=f i (P previous (t),z i (t)), Among them, P previous (t) represents the power output at the previous time step, z i (t) represents the switch state of the i-th load resource at time t, f i (·) is the optimal dispatching strategy obtained based on the dynamic model of load regulation.

7. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 1, characterized in that: In step 5, the scheduling scheme is analyzed by Lyapunov stability theory to ensure that the system will not become unstable during the load regulation process. The Lyapunov function is defined as: Among them, P ref.i is the target power output, P i (t) represents the power output of the i-th load resource at time t out, V(P1(t),…,P N (t)) represents the Lyapunov function, and N is the total number of high-energy-consuming load resources in the virtual power plant.

8. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 1, characterized in that: The high-energy-consuming load resource adjustment method solves the optimal scheduling by combining the gradient descent method with dynamic programming to obtain the optimal scheduling strategy for each load resource. The optimal scheduling strategy is expressed as: in, is the gradient of the Lagrangian function, δP i (t) represents the adjustment of the power output of the load resource at each time step, For all load resources i and time t.

9. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 1, characterized in that: The high-energy-consuming load resource adjustment method improves the computing efficiency through parallel computing technology, and performs parallel optimization on the scheduling of each load resource. The parallel optimization calculation process is performed through the following formula: in, For the overall optimization goal, is the parallel calculation of the i-th load resource at time t, N is the total number of high-energy-consuming load resources in the virtual power plant, P i (t) represents the power output of the i-th load resource at time t, Denotes the time point t.

10. The method for regulating high energy consumption load resources based on a virtual power plant according to claim 1, characterized in that: The high-energy-consuming load resource adjustment method includes a real-time data acquisition and feedback control system, which monitors the power grid load fluctuation in real time and dynamically adjusts the power output of each high-energy-consuming load resource in the virtual power plant according to the real-time load data. The dynamic adjustment process is controlled by the following formula: P i (t=Adjust(P current (t),P target (t)), Among them, P current (t) is the power output of load resource i at the current moment, P target (t) is the target power output, Adjust(·) is the control function for adjusting the load output according to real-time data, P i (t) represents the power output of the i-th load resource at time t.

Images