A method for dynamic management of energy consumption

Abstract

The present application relates to the field of intelligent control and optimization, and discloses a kind of energy consumption dynamic management method, comprising the following steps: S1, the mathematical model of energy system is constructed, and the state space and control space of energy system are defined;S2, and the stability of energy system state is analyzed by Lyapunov function;S3, the optimal control law of system is obtained by solving HJB equation;S4, design Kalman type state estimator;S5, model parameters are converted between micro and macro levels, for the consistency of energy and constraint conditions between scales;S6, the HJB equation is discretized and solved using numerical method;S7, the performance of the system is verified by simulation and experiment.The effect of automatic avoidance of high electricity price operation interval, budget overrun warning and adaptive cost optimization is achieved by constructing stochastic dynamics energy consumption model, HJB optimal control solution and cross-scale energy consumption mapping, dynamic prediction and optimal scheduling control of energy consumption behavior under different loads, different electricity prices and different budget upper limits.

Description

Technical Field

[0001] This invention relates to the field of intelligent control and optimization technology, specifically to a method for dynamic energy consumption management. Background Technology

[0002] As a core hub of air transport, airports have large-scale, complex, and dynamically fluctuating energy consumption systems. Dynamic energy consumption management is a key technology to ensure their efficient operation. Airport energy consumption covers the entire operation chain, including building energy consumption such as terminal air conditioning and lighting, operational support energy consumption such as runway navigation lights and baggage sorting systems, and ground equipment energy consumption such as aircraft towing vehicles.

[0003] Airports urgently need dynamic energy management methods due to multiple core needs. From a cost perspective, energy expenses are a major airport expenditure; dynamic management, through refined optimization, can directly reduce costs and increase efficiency. From a safety perspective, critical equipment such as navigation lights relies on a stable power supply; dynamic management can monitor energy consumption anomalies in real time, provide early warnings of equipment malfunctions and potential power supply risks, and ensure aviation safety. From a green development perspective, airports face emission reduction assessments under dual-carbon targets; dynamic management reduces waste and lowers carbon emissions through methods such as peak-shifting energy use.

[0004] Currently, energy consumption management at most domestic airports remains relatively rudimentary. In related technologies, the model fails to fully consider the coupling effect between load forecasting errors and time-varying electricity price fluctuations, resulting in significant deviations in energy cost prediction under high load conditions. This, in turn, affects the optimization effect of scheduling strategies and causes the budget overrun warning mechanism to fail to be triggered in a timely manner. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a dynamic energy consumption management method that solves the problem that the model fails to fully consider the coupling effect between load forecasting errors and time-varying electricity price fluctuations, resulting in significant deviations in energy cost prediction under high load conditions, which in turn affects the optimization effect of scheduling strategies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A dynamic energy consumption management method includes the following steps: S1. Construct a mathematical model of the energy system, define the state space and control space of the energy system, and describe the energy consumption process of the system based on stochastic differential equations; S2. Perform reachability set analysis to calculate the range of possible states of the energy system under specified initial conditions, and analyze the stability of the energy system state using Lyapunov functions; S3. By solving the HJB equation, the optimal control law of the system is obtained. The selection of the control input is based on the optimality condition of the value function. S4. Design a Kalman-type state estimator to estimate the state of the energy system in real time and use an error feedback mechanism to correct the system control input. S5. Define a scale transformation operator to transform model parameters between micro and macro levels for energy and constraint consistency across scales. S6. The HJB equation is discretized and solved using numerical methods, and the control strategy of the energy system is optimized using iterative algorithms. S7. Verify the performance of the energy system through simulation and experimentation to ensure it can operate under different load and constraint conditions and meet optimization objectives.

[0007] Preferably, S1 further includes: The system acquires energy consumption data, state variable data, and external disturbance data during operation through a parameter acquisition component. Based on the data, construct a state vector and determine the rate of state change; The energy consumption data is used to establish a state-space model based on the energy consumption evolution law, and the state change rate is used to form the dynamic constraint set in the model.

[0008] Preferably, S2 includes: A state reachability threshold is defined, and a continuous region satisfying the threshold is defined as the reachability domain; The reachable domain boundary is analyzed based on the parameters of the state gradient change to determine whether the reachable domain has undergone topological changes. A reachable domain update operation is triggered when a topology change trend occurs in the reachable domain. The state reach threshold is set based on the initial state vector, the energy consumption change rate, and the upper limit of the control input.

[0009] Preferably, S3 further includes: The state deviation amount is set according to the state deviation distribution; Set the control input cost based on changes in control input; The state cost is set based on the changes in system state; The target quantity for controlling input updates is obtained by weighted summation of the cost quantities; The target variable is used to update the optimal control law.

[0010] Preferably, S4 further includes: Define the set of state estimation error constraints; The gain of the state estimator is adjusted based on the change in the noise interference term to ensure that the estimation error satisfies the error constraint set. The state estimation error is calculated by the difference between the state prediction and the state correction.

[0011] Preferably, S5 includes: By setting a set of parameters for the mapping relationship between the microscale and the macroscale; Construct a scale mapping function based on the parameter set; The scale mapping function is updated based on the change in energy consumption to ensure that the parameters of models at different scales remain consistent. Scale mapping is performed by transforming microscopic energy parameters into macroscopic energy consumption parameters.

[0012] Preferably, S6 includes: Set the discretization step size and discretize the state space in the HJB equations; The value function is updated based on the changes in control input at discrete state points. Determine whether the iteration has converged based on the change in the discrete-valued function; Continue executing the next iteration if the convergence constraint is not met; The discretization process employs a discretization strategy.

[0013] Preferably, S7 further includes: Construct an evaluation metric that includes changes in energy consumption, changes in control input, and state deviations; The system's response characteristics under different operating conditions are analyzed based on the evaluation metrics. Among them, the evaluation metric is used to verify the operating status of the system under different load conditions, different time periods, and different external disturbances.

[0014] Preferably, S3, S4, and S5 are associated through a set of associated parameters, which includes: State deviation is used to reflect the difference between the actual state and the target state of the system; Changes in control input are used to measure the impact of changes in control input on the system state. Scale transformation is used to describe the energy conversion and constraint matching between microscopic and macroscopic models; By jointly processing the associated parameters, corresponding joint control inputs are set for optimal control under different states and load conditions.

[0015] An energy consumption dynamic management system, the system comprising: The modeling module is used to construct mathematical models of energy systems, define the state space and control space of energy systems, and describe the dynamic laws of energy consumption processes. The data acquisition module is used to acquire energy consumption data, state variable data, and external disturbance data in real time during system operation. The reachability set analysis module is used to perform reachability set analysis to determine the reachable regions of the system state and whether the system meets the stability requirements. The optimal control module is used to obtain the optimal control law of the system by solving the HJB equations, and the control input is selected according to the optimality condition. The state estimation module is used to estimate the system state in real time and correct the control input through Kalman filtering or other error feedback mechanisms to ensure system stability. The scale mapping module is used to convert between microscopic and macroscopic models to ensure consistency in the system's energy consumption and constraints. The numerical simulation module is used to simulate and verify the system, and to simulate the system's performance and stability under different loads and control inputs. The controller module is used to adjust the control strategies of each module based on the collected energy consumption, state variables and external disturbance data, thereby optimizing the system's operating state.

[0016] This invention provides a method for dynamic energy consumption management. It has the following beneficial effects: 1. This invention, through the construction of a stochastic dynamic energy consumption model, reachability set stability analysis, HJB optimal control solution, and cross-scale energy consumption mapping, achieves dynamic prediction and optimal scheduling control of energy consumption behavior under different loads, different electricity prices, and different budget limits, thereby reducing total energy consumption costs, automatically avoiding high electricity price operating ranges, providing budget overrun warnings, and adaptive cost optimization.

[0017] 2. This invention, through reachability set analysis and Lyapunov stability analysis, ensures that the system can maintain stable operation under different initial conditions and control inputs, thus obtaining more reliable system stability assurance. This analysis process can evaluate the reachability domain of the system and predict whether the system can operate stably within the reachable region based on actual control inputs and disturbances, ensuring the stability of the system in complex environments.

[0018] 3. This invention, through the design of a Kalman-type state estimator and an error feedback mechanism, estimates and corrects the system state in real time, achieving more precise dynamic control, obtaining accurate control input and system stability. Through continuous state estimation and dynamic gain adjustment, the system ensures that the state estimation error is always within the allowable range, thereby guaranteeing the system's stability and response speed.

[0019] 4. This invention achieves consistency between microscopic and macroscopic models through scale mapping technology, optimizes the system's energy efficiency conversion and constraint matching, and obtains a more efficient cross-scale energy management scheme. By establishing an effective mapping relationship between microscopic and macroscopic scales, the system can achieve more precise energy consumption management, thereby improving overall energy efficiency and reducing energy loss. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system architecture diagram of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0022] Please see the appendix Figure 1 This invention provides a method for dynamic energy consumption management, comprising the following steps: S1. Construct a mathematical model of the energy system, define the state space and control space of the energy system, and describe the energy consumption process of the system based on stochastic differential equations; S1 also includes: The system acquires energy consumption data, state variable data, and external disturbance data during operation through a parameter acquisition component. Construct a state vector based on the data and determine the rate of change of the state; Among them, energy consumption data is used to establish a state-space model based on the energy consumption evolution law, and the state change rate is used to form the dynamic constraint set in the model; This model not only describes the dynamic changes in energy consumption of a system, but also supports energy cost forecasting, budget allocation, and energy dispatch analysis, serving as a decision support tool for energy management in enterprises and industrial parks.

[0023] Specifically, energy consumption data, state variable data, and external disturbance data during system operation are acquired through parameter acquisition components. These components include electricity meters, flow meters, and temperature sensors, enabling real-time acquisition of various state information within the system. The sampling frequency is [missing information]. That is, sampling per second Sampling interval During data acquisition, all sensor measurements are synchronized via clock to ensure consistent sampling time and minimize error to within one sampling period. ; A state vector is constructed based on the collected data, and the rate of change of state is calculated. Indicates the system at time... The state vector is constructed using the following method for all important variables: ; in, For a moment Instantaneous power, unit: kW; Cumulative energy consumption, unit: kWh; Equipment load rate, range: 0–1; Ambient temperature, unit: ℃; This represents the energy storage state quantity (0-1 ratio). For load forecasting error or other internal disturbance estimates; Rate of change of state The rate of change of the state vector with time is represented by the finite difference method, which approximates the state vector: ; in, To reduce noise interference, the state vector can be sampled first at the sampling interval. Smoothing processes can be performed, including low-pass filtering or exponentially weighted smoothing. ; in, The smoothing factor is then used to calculate its rate of change using the smoothed state vector. ; Based on energy consumption data, a state-space model based on the energy consumption evolution law is constructed, and a set of dynamic constraints is formed. It is assumed that the energy consumption process of the system can be represented by the following stochastic differential equation: ; in, For system status, To control the input, External disturbances This is a Wiener process, but for ease of computation, it is converted into a discrete-time model: ; in, Here is the state transition matrix. For the control matrix, The disturbance effect matrix, This is process noise, assumed to be averaged Gaussian noise: .

[0024] Dynamic constraint sets are used to limit the rate of change of the system state. Assume the constraint set is... It is represented as: in, This is the lower limit of the rate of change. The upper and lower limits are determined empirically and statistically, and the calculation formulas are as follows: , ; in, The historical average rate of change The standard deviation of the rate of change For safety, the above steps can be used to obtain the set of dynamic constraints for the system state. This is further applied to subsequent reachability set analysis and optimal control solutions; Based on the above, by collecting equipment operation data, external environmental parameters, and energy supply information, the energy consumption variation over time is modeled as a dynamic system. The model depicts behaviors such as load changes, equipment start-up and shutdown, and power consumption trends, reflecting the changing characteristics of energy use under different equipment, time periods, and operating conditions. By incorporating historical electricity price data, real-time electricity market information, and load forecasting results, the model calculates the energy cost range for future periods, predicts short-term and medium-to-long-term energy expenditures, automatically identifies high-cost operating segments, low-cost scheduling opportunities, and potential cost anomalies, providing a basis for budget formulation. As electricity prices, equipment status, seasonal demand, and budget conditions change, the system updates the energy consumption model and forecasting parameters in real time, ensuring the long-term effectiveness of the generated energy management strategies. The model is continuously revised based on actual operational feedback, improving forecast accuracy and strategy rationality, ensuring that energy management decisions always meet the company's operational needs and economic goals.

[0025] S2. Perform reachability set analysis to calculate the range of possible states of the energy system under specified initial conditions, and analyze the stability of the energy system state using Lyapunov functions; S2 includes: Define a state reachability threshold and define a continuous region that satisfies the threshold as the reachability domain; The reachable domain boundary is analyzed based on the parameters of the state gradient change to determine whether the reachable domain has undergone topological changes. A reachable domain update operation is triggered when a topology change trend occurs in the reachable domain. The state reach threshold is set based on the initial state vector, the rate of change of energy consumption, and the upper limit of control input. Meanwhile, when conducting stability analysis, external factors such as energy price fluctuations, budget constraints, and energy supply and demand fluctuations are considered to generate a space of feasible management strategies that meet management objectives and support the scheduling and optimal allocation of energy resources.

[0026] Specifically, the state reachability threshold is set based on the system's initial state vector, energy consumption rate of change, and upper limit of control input. It can be based on the system's physical limitations, maximum control input The range of changes in the system state is expressed by the following formula: ; in, Let be the initial state vector. The maximum rate of state change. To control the upper limit of input, These are weighting coefficients that are adjusted according to actual needs.

[0027] This threshold defines a region; if a state vector satisfies this threshold, the state is considered reachable. This region is called the reachable domain. The reachable domain describes the set of all states that the system can stably reach given control inputs and disturbances. Next, the reachable domain boundary is analyzed based on the parameters of the state gradient changes to determine whether a topological change has occurred. The boundary of the reachable domain refers to the boundary of the system state with the greatest possible change under the influence of control inputs and disturbances. To analyze the changes in these boundaries, the gradient changes of the state vector at different time points are first calculated. It can be represented as: ; in, The sampling interval is... For a moment The state vector; When the boundary of the reachable domain changes, it means that the topology of the system may have changed. By analyzing the trend of the state gradient change, the signal of topological change can be detected. The following methods can be used to analyze gradient changes: ; If the gradient change If a preset threshold is exceeded, it is assumed that the system's reachable domain boundary may have undergone a topological change. Finally, when a topological change trend is observed in the reachable domain, a reachable domain update operation is triggered. When the system detects a significant topological change trend at the reachable domain boundary, the reachable domain needs to be updated. This means that in the new state, the system may need to recalculate the reachable domain boundary and correct the control strategy. In this case, the updated reachable domain will take into account the current state information, energy consumption change rate, and control input constraints. The system collects and updates external management factors in real time, including electricity market prices, energy supply and demand forecasts, and budget execution status. External factor information is obtained from the data acquisition module or external data interfaces, covering data such as peak and off-peak electricity price changes, supply and demand fluctuation trends, budget balances, and expenditure rates. This dynamically reflects the external operating environment. During state reachability analysis, the system not only defines state boundaries based on energy consumption change patterns but also limits the system's operating range based on upper limits for electricity price changes, budget expenditure constraints, and supply and demand deviation thresholds, ensuring that the reachable area aligns with management objectives. When situations such as rapid electricity price increases, near-exhaustion of the budget, or tight energy supply occur, the system determines that the reachable domain may shrink or undergo topological changes, and provides early risk warnings and adjusts operational strategies accordingly. Finally, real-time iterative updates to the strategy space ensure the continuous effectiveness of the system's management capabilities. Based on real-time updates of electricity prices, supply and demand, and budgets, the system continuously expands or shrinks the strategy space, ensuring that the generated energy management strategies always meet the requirements of enterprise operational objectives and market environment changes.

[0028] S3. By solving the HJB equation, the optimal control law of the system is obtained. The selection of the control input is based on the optimality condition of the value function. S3 also includes: The state deviation amount is set according to the state deviation distribution; Set the control input cost based on changes in control input; The state cost is set based on the changes in system state; The target quantity used to control input updates is obtained by weighted summation of the cost quantities; The objective variable is used to update the optimal control law, which takes into account load scheduling optimization, energy cost minimization, budget constraints, electricity market rules, and management objectives, in order to help generate a multi-objective comprehensive optimization energy management strategy.

[0029] Specifically, The state deviation amount is set according to the state deviation distribution. The state deviation amount refers to the difference between the current state of the system and the target state. It can be calculated using the following formula: The following formula is used for calculation: ; in, ; ; State deviation measures the error of the current state relative to a predetermined target state. A weighted approach is typically used to determine the degree of influence of the deviation, in order to better control system stability and response speed. Control input cost is defined based on changes in control input; control input cost refers to the impact of changes in control input on system performance. To quantify changes in control input, we define control input cost. for: ; in, The control input at time k; This is the control input at time k-1; These are weighting coefficients used to calculate the cost of changes in control input. This cost penalizes excessive changes in control input to prevent unnecessary fluctuations or excessive adjustments in the system during control. The state cost is set based on system state changes; it refers to the impact of system state changes on the target performance. (State change cost) This can be expressed by the following formula: ; in, The rate of change of state, i.e., the state of the system over time. The rate of change over time These are weighting coefficients used to control the cost of state changes. The state cost reflects the price of a state change; a larger state change leads to a higher cost. Therefore, by adjusting... It can control the system's sensitivity to changes in state; The specific content of the HJB equation is as follows:

[0030] The terminal conditions are: ; in: ; ; ; ; ; The target quantity used to update the control input is obtained by weighted summation of the cost quantities. It is the core indicator for control input updates. It is a weighted sum of state deviation, control input cost, and state cost, as shown in the following formula: ; in, ; ; ; ; By using a weighted summation method, the impact of various factors on the target quantity can be balanced, and the optimal control law can be updated based on the target quantity. This method allows for flexible adjustment of the control strategy according to actual needs to achieve system optimization. During real-time operation, the control law dynamically changes according to electricity prices, load demand, budget margins, and system state deviations. When electricity prices are high, the control law automatically reduces energy consumption or schedules loads to low-price periods; when the budget is close to its limit, the control law automatically reduces controllable energy consumption or adjusts the operating mode; when load demand increases, the control law increases its output to ensure stable power supply. Through this mechanism, the control law can coordinate energy consumption control and cost optimization under multiple objective constraints. Load scheduling constraints include minimum load, maximum load, operating cycle constraints, and restrictions on transferable loads. Load scheduling adjustments are completed within the framework of the optimal control law, enabling the optimization strategy to automatically change the control input based on information such as peak and off-peak electricity periods, demand response plans, and energy consumption budget distribution, thereby achieving optimal scheduling and cost savings.

[0031] S4. Design a Kalman-type state estimator to estimate the system state in real time and use an error feedback mechanism to correct the system control input. S4 also includes: Define the set of state estimation error constraints; The gain of the state estimator is adjusted based on the change in the noise interference term to ensure that the estimation error satisfies the error constraint set. The state estimation error is calculated by the difference between the state prediction and the state correction.

[0032] Specifically, firstly, a set of state estimation error constraints is defined. To ensure that the state output by the estimator meets the accuracy requirements, an allowable range for the state estimation error needs to be set. Let the state estimation error be: ; in, State estimates, provided by the estimator; The actual or corrected state of the system is used to construct a set of error constraints based on the system stability and control accuracy requirements. ; ; in, The lower and upper limits of the error components can be set based on historical error statistics, noise levels, and control indicators. The error constraint set is used to determine whether the current estimation error falls within the allowable range. Secondly, the gain of the state estimator is adjusted according to changes in the noise interference term to ensure the error satisfies the constraint set. The noise interference term of the system includes: Process noise: ; Noise measurement: The state prediction and correction are as follows: ; ; in, State prediction quantity State correction amount Measured value The estimated gain includes the Kalman gain estimation error, which is calculated from the difference between the predicted and corrected values. ; To ensure the error meets the constraints, the gain needs to be adjusted according to the noise changes. The Kalman gain expression is: ; in: The gain varies with measurement noise, allowing for dynamic adjustment based on noise changes. When noise increases, the dependence on the measurement is automatically reduced; when noise decreases, the dependence on the measurement is automatically increased, thus keeping the state estimation error within the constraint set. Inside; When the error tends to exceed the constraint set, a gain adjustment mechanism is used to ensure that the estimation error meets the constraint requirements. If the estimation error If the value tends to exceed the set threshold, adaptive gain adjustment will be triggered. ; in, The adjustment coefficients, calculated based on the degree of error exceeding the limit, are as follows: ; The adjustment mechanism ensures that when the error increases, the correction of the measurement is strengthened, and when the error is too small or the noise increases, the dependence on the measurement is reduced, thus always guiding the estimation error to remain within the constraint range. Through the above steps, this embodiment realizes dynamic estimation gain adjustment based on noise changes, making the state estimation error controllable and satisfying the error constraint set, thus providing a reliable state estimation basis for subsequent stable control of the system.

[0033] S5. Define a scale transformation operator to transform model parameters between micro and macro levels for energy and constraint consistency across scales. S5 includes: By setting a set of parameters for the mapping relationship between the microscale and the macroscale; Construct a scale mapping function based on the parameter set; The scale mapping function is updated based on the change in energy consumption to ensure that the parameters of models at different scales remain consistent. Scale mapping is performed by transforming microscopic energy parameters into macroscopic energy consumption parameters.

[0034] Specifically, firstly, a set of parameters is defined to represent the mapping relationship between the microscopic and macroscopic scales. To achieve this mapping between microscopic scale parameters (including particle energy, local power density, and transient energy exchange) and macroscopic scale parameters (including total system energy consumption and average power), a parameter set needs to be defined, through: ; The parameter set typically includes: microscopic energy conversion coefficient (including single-particle energy), energy per unit volume, scale ratio (including the ratio of microscopic volume to total system volume), time scale mapping coefficient (including the proportional relationship between microscopic time step and macroscopic sampling period), and energy consumption coupling coefficient (including the weight of microscopic energy changes on macroscopic energy consumption). The definitions are as follows: ; in: ; ; ; These parameters determine the transformation relationship between the microscopic and macroscopic levels. A scaling function is constructed based on this parameter set. The scaling function is used to convert microscopic energy parameters... Converted into macroscopic energy consumption parameters The basic form of the construction is as follows: ; In the case of a typical linear mapping, it can be written as: ; in: ; ; ; ; ; If the effects of nonlinear coupling are considered, it can be expanded to: ; in Determined by the statistical characteristics or physical model of the system operation, the scale mapping function is finally updated based on the change in energy consumption to keep the parameters of different scale models consistent. When the macroscopic energy consumption changes during the operation of the system, the mapping function needs to be adaptively updated to maintain the consistency between microscopic parameters and macroscopic energy consumption.

[0035] S6. The HJB equation is discretized and solved using numerical methods, and the control strategy of the system is optimized using iterative algorithms. S6 includes: Set the discretization step size and discretize the state space in the HJB equations; The value function is updated based on the changes in control input at discrete state points. Determine whether the iteration has converged based on the change in the discrete-valued function; Continue executing the next iteration if the convergence constraint is not met; The discretization process employs a discretization strategy.

[0036] Specifically, a discretization step size is set, and the state space in the HJB equations is discretized. To solve the HJB equations on a computer, the state space must be discretized. The discretization step size is set to Δs, the purpose of which is to transform the continuous state space into a discrete set of states {x_i} by choosing an appropriate step size. The choice of the discretization step size directly affects the computational accuracy and solution efficiency. Specifically, the discretization process of the state space can be expressed as: ; in, Represents the discretized state points; This represents the minimum value in the state space. For a long walk; The total number of discrete points is represented by the discretization process, which divides the originally continuous state space into several discrete state points, thus providing a basis for subsequent iterative updates. In the discretized state space, each state point... Each has a corresponding value function This represents the system performance at that state point. To solve for the optimal control strategy, it is necessary to consider the control input. The value function is updated by calculating the changes in the control input and then updating the discrete value function at the current state point. Let the control input at the current moment be... The discrete values ​​of the value function are updated using the following formula: ; in, For state The target quantity is determined by considering factors such as state deviation and control input costs. The system's state transition equation describes the transition from state... arrive The evolutionary process. The discretization step size represents the change in state. This is achieved by continuously updating the value function. This process can gradually approximate the solution of the optimal control strategy in the discrete state space. Then, the convergence of the iteration is determined based on the change in the discrete-valued function. During the iteration process, the change in the value function is calculated at each iteration. To determine whether the convergence condition has been met, the iteration termination condition is set as follows: ; in, For the first The value function of the next iteration The convergence threshold represents the allowable error range for the change in the value function. Finally, if the convergence constraint is not met, the next iteration continues if the change in the value function... Exceeding the convergence threshold Then continue with the next iteration until the convergence condition is met.

[0037] S7. Verify the system's performance through simulation and experiments to ensure it can operate under different load and constraint conditions and meet optimization objectives.

[0038] S7 also includes: Construct an evaluation metric that includes changes in energy consumption, changes in control input, and state deviations; The response characteristics of the system under different operating conditions are analyzed based on the evaluation metrics; Among them, the evaluation metric is used to verify the operating status of the system under different load conditions, different time periods, and different external disturbances.

[0039] Specifically, firstly, constructing an evaluation metric that includes changes in energy consumption, changes in control input, and state deviation is an important tool for quantitative analysis of system state. It integrates multiple influencing factors such as changes in energy consumption, changes in control input, and state deviation. The evaluation metric is defined as follows: It can be represented in the following form: ; in, This represents the change in macroeconomic energy consumption. To control the amount of change in the input, This represents the change in system state deviation, i.e., the difference between the current state and the target state. These are weighting coefficients for various factors, used to balance the contributions of energy consumption changes, control input changes, and state deviations; Based on the evaluation metrics, the response characteristics of the system under different operating conditions are analyzed, and the constructed evaluation metrics are used as a basis. The system can perform response analysis under different operating conditions. By monitoring multiple operating cycles, the evaluation metrics can help analyze the system's response characteristics under different loads, time periods, and external disturbances, thereby providing decision support for subsequent control optimization and energy management. Under different load conditions, the system's energy consumption, control input, and state deviation will change. Changes in load may lead to increased power demand, thus affecting overall energy consumption. Based on this, the evaluation metrics... It is necessary to consider the impact of load changes on energy consumption, control input, and system status; Changes in time periods affect system energy efficiency because energy consumption is often closely related to peak and off-peak hours. During peak hours, the system may need to handle a heavier load, leading to a significant increase in energy consumption; while during off-peak hours, the system load is relatively light, and energy consumption is lower. (Based on evaluation metrics) It can analyze the system's response characteristics at different time intervals, assess the impact of energy consumption changes on system performance, and evaluate the system's operating status under different load conditions, different time intervals, and different external disturbances. To verify the system's operating status under various conditions, the evaluation quantity... This will be used to compare and analyze system performance under different operating conditions. By periodically calculating evaluation metrics and comparing them with predetermined target values, it is possible to determine whether the system meets the set performance indicators. Under different load conditions, the changing trends of evaluation metrics can help determine whether the system can automatically adjust its operating parameters when the load changes, maintaining optimal energy use. At different time intervals, evaluation metrics can help verify whether the system has the ability to adjust its operating strategy according to changes in time periods, thereby achieving dynamic optimization of energy consumption. Under different external disturbances, evaluation metrics can reflect the system's emergency response capability and recovery efficiency after disturbances, ensuring the continuous and stable operation of the system in complex environments. Ultimately, evaluation metrics provide strong data support for the performance verification of the system, ensuring the reliability and efficiency of the system under different scenarios.

[0040] S3, S4, and S5 are linked through a set of associated parameters, which includes: State deviation is used to reflect the difference between the actual state and the target state of the system; Changes in control input are used to measure the impact of changes in control input on the system state. Scale transformation is used to describe the energy conversion and constraint matching between microscopic and macroscopic models; By jointly processing the associated parameters, corresponding joint control inputs are set for optimal control under different states and load conditions.

[0041] Specifically, to achieve efficient dynamic management, these three steps must be organically combined through a set of associated parameters to derive a joint control input. This allows for optimal control under different states and load conditions. First, the set of associated parameters is defined, which includes: State deviation Used to reflect the actual state of the system With the target state The difference between them. Calculated as: Control input change Used to measure control input The impact of the change on the system state is calculated as follows: Scale transformation quantity This is used to describe the energy transfer and constraint matching between microscopic and macroscopic models, and is usually implemented through a scaling function, including... in This represents energy consumption on a macroscopic scale. This represents energy consumption at the microscopic scale. It is a set of scaling transformation parameters; Secondly, by using a set of associated parameters to set the joint control input, based on the system's operating state, changes in control input, and energy conversion requirements, the optimal control input can be set through joint analysis of the set of associated parameters. The combined control input can be adjusted according to different system states, load conditions, and external disturbances. The expression for the combined control input is defined as follows: ; in, This reflects the system's deviation, and the control inputs need to respond in order to push the system state toward the target state. Measure changes in control inputs to ensure system input stability and avoid excessive fluctuations in adjustments. This reflects the energy conversion requirements between macro and micro scales, in order to ensure energy efficiency consistency across scales; Then, optimal control is performed based on different states and load conditions. Control adjustments are made under different states. When the system's operating state deviates from the target state... A larger value indicates that the system needs adjustment. At this time, the joint control input... Significant changes are needed to bring the system to a normal state as quickly as possible. Towards the target state ; Control adjustments under different load conditions: Changes in load conditions directly affect the system's energy consumption requirements, and control inputs... The system will adjust appropriately based on load changes. Under high load conditions, the system may need to increase input to maintain stability and efficiency. Under low load conditions, the system can reduce input to conserve energy. In this scenario, the change in control input within the associated parameter set... This demonstrates the impact of load changes on system control inputs. By jointly processing the aforementioned parameters, an optimal control strategy can be dynamically generated. This strategy can adjust the control inputs according to the actual operating conditions of the system under different states, load conditions, and time periods, ensuring that the system maintains optimal performance in a changing external environment.

[0042] Please see the appendix Figure 2 An energy consumption dynamic management system, the system comprising: The modeling module is used to construct mathematical models of energy systems, define the state space and control space of energy systems, and describe the dynamic laws of energy consumption processes. The data acquisition module is used to acquire energy consumption data, state variable data, and external disturbance data in real time during system operation. The reachability set analysis module is used to perform reachability set analysis to determine the reachable regions of the system state and whether the system meets the stability requirements. The optimal control module is used to obtain the optimal control law of the system by solving the HJB equations, and the control input is selected according to the optimality condition. The state estimation module is used to estimate the system state in real time and correct the control input through Kalman filtering or other error feedback mechanisms to ensure system stability. The scale mapping module is used to convert between microscopic and macroscopic models to ensure consistency in the system's energy consumption and constraints. The numerical simulation module is used to simulate and verify the system, and to simulate the system's performance and stability under different loads and control inputs. The controller module is used to adjust the control strategies of each module based on the collected energy consumption, state variables and external disturbance data, thereby optimizing the system's operating state.

[0043] Specifically, the modeling module is used to construct a mathematical model of the energy system, define the state space and control space of the energy system, and describe the system's operating characteristics according to the dynamic laws of the energy consumption process. The data acquisition module is used to acquire energy consumption data, state variables, and external disturbance information during the system's operation in real time, providing basic data support for subsequent control and analysis. The reachability set analysis module is used to determine the reachable region of the system state based on the constructed model and analyze whether the system meets the stability requirements under different operating conditions. The optimal control module obtains the optimal control law of the system by solving the optimality condition, thereby providing the optimal control input for the controller. The state estimation module uses real-time acquired data combined with an error feedback mechanism to estimate and correct the system state, ensuring the accuracy and stability of the control input. The scale mapping module is used to map between the micro-energy consumption model and the macro-energy consumption model, so that the energy consumption description and constraints under the two scales are consistent; the numerical simulation module is used to simulate and verify the system under various combinations of loads, disturbances and control inputs, and analyze the system performance and stability; the controller module, based on real-time collected energy consumption, state and disturbance data, integrates the optimal control strategy, reachability set analysis results and state estimation information to coordinate and adjust the operation strategies of the above modules, thereby realizing the dynamic optimization and efficient and stable operation of the entire system.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic management of energy consumption, characterized in that, Includes the following steps: S1. Construct a mathematical model of the energy system, define the state space and control space of the energy system, and describe the energy consumption process of the system based on stochastic differential equations; S2. Perform reachability set analysis to calculate the range of possible states of the energy system under specified initial conditions, and use Lyapunov functions to analyze the stability of the energy system state. S3. By solving the HJB equation, the optimal control law of the system is obtained. The selection of the control input is based on the optimality condition of the value function. S4. Design a Kalman-type state estimator to estimate the system state in real time and use an error feedback mechanism to correct the system control input. S5. Define a scale transformation operator to transform model parameters between micro and macro levels for energy and constraint consistency across scales. S6. The HJB equation is discretized and solved using numerical methods, and the control strategy of the system is optimized using iterative algorithms. S7. Verify the system's performance through simulation and experiments to ensure it can operate under different load and constraint conditions and meet optimization objectives.

2. The method for dynamic energy consumption management according to claim 1, characterized in that: S1 further includes: The system acquires energy consumption data, state variable data, and external disturbance data during operation through a parameter acquisition component. Based on the data, construct a state vector and determine the rate of state change; The energy consumption data is used to establish a state-space model based on the energy consumption evolution law, and the state change rate is used to form the dynamic constraint set in the model. The model is not only used to describe the dynamic changes in energy consumption of the system, but also supports energy cost prediction, budget allocation and energy dispatch analysis, and is used for decision support and energy management of enterprises and parks.

3. The method for dynamic energy consumption management according to claim 1, characterized in that: S2 includes: A state reachability threshold is defined, and a continuous region satisfying the threshold is defined as the reachability domain; The reachable domain boundary is analyzed based on the parameters of the state gradient change to determine whether the reachable domain has undergone topological changes. A reachable domain update operation is triggered when a topology change trend occurs in the reachable domain. The state reach threshold is set based on the initial state vector, the energy consumption change rate, and the upper limit of the control input. When conducting stability analysis, external factors such as energy price fluctuations, budget constraints, and energy supply and demand fluctuations are considered to generate a space of feasible management strategies that meet management objectives and support the scheduling and optimal allocation of energy resources.

4. The method for dynamic energy consumption management according to claim 1, characterized in that: S3 further includes: The state deviation amount is set according to the state deviation distribution; Set the control input cost based on changes in control input; The state cost is set based on the changes in system state; The weighted summation of the cost amounts yields the target amount used to control input updates; The target quantity is used to update the optimal control law, which takes into account load scheduling optimization, energy cost minimization, budget constraints, electricity market rules and management objectives, in order to help generate a multi-objective comprehensive optimization energy management strategy.

5. The method for dynamic energy consumption management according to claim 1, characterized in that: S4 further includes: Define the set of state estimation error constraints; The gain of the state estimator is adjusted based on the change in the noise interference term to ensure that the estimation error satisfies the error constraint set. The state estimation error is calculated by the difference between the state prediction and the state correction.

6. The method for dynamic energy consumption management according to claim 1, characterized in that: S5 includes: Define the set of parameters used for the mapping relationship between the microscale and the macroscale; Construct a scale mapping function based on the parameter set; The scale mapping function is updated based on the change in energy consumption to ensure that the parameters of models at different scales remain consistent. Scale mapping is performed by transforming microscopic energy parameters into macroscopic energy consumption parameters.

7. The method for dynamic energy consumption management according to claim 1, characterized in that: S6 includes: Set the discretization step size and discretize the state space in the HJB equations; The value function is updated based on the changes in control input at discrete state points. Determine whether the iteration has converged based on the change in the discrete-valued function; Continue executing the next iteration if the convergence constraint is not met; The discretization process employs a discretization strategy.

8. The method for dynamic energy consumption management according to claim 1, characterized in that: The S7 also includes: Construct an evaluation metric that includes changes in energy consumption, changes in control input, and state deviations; The response characteristics of the energy system under different operating conditions are analyzed based on the evaluation metrics. Among them, the evaluation quantity is used to verify the operating status of the energy system under different load conditions, different time periods, and different external disturbances.

9. The method for dynamic energy consumption management according to claim 1, characterized in that: S3, S4, and S5 are associated through a set of related parameters, which includes: State deviation is used to reflect the difference between the actual state and the target state of the system; Changes in control input are used to measure the impact of changes in control input on the system state. Scale transformation is used to describe the energy conversion and constraint matching between microscopic and macroscopic models; By jointly processing the associated parameters, corresponding joint control inputs are set for optimal control under different states and load conditions.

10. An energy consumption dynamic management system, applied to the energy consumption dynamic management method as described in any one of claims 1-9, characterized in that, The system includes: The modeling module is used to construct mathematical models of energy systems, define the state space and control space of energy systems, and describe the dynamic laws of energy consumption processes. The data acquisition module is used to acquire energy consumption data, state variable data, and external disturbance data in real time during system operation. The reachability set analysis module is used to perform reachability set analysis to determine the reachable regions of the system state and whether the system meets the stability requirements. The optimal control module is used to obtain the optimal control law of the system by solving the HJB equations, and the control input is selected according to the optimality condition. The state estimation module is used to estimate the system state in real time and correct the control input through Kalman filtering or other error feedback mechanisms to ensure system stability. The scale mapping module is used to convert between microscopic and macroscopic models to ensure consistency in the system's energy consumption and constraints. The numerical simulation module is used to simulate and verify the system, and to simulate the system's performance and stability under different loads and control inputs. The controller module is used to adjust the control strategies of each module based on the collected energy consumption, state variables and external disturbance data, thereby optimizing the system's operating state.

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