Comprehensive energy system demand response self-adaptive control method for multi-energy cooperative regulation

Abstract

The invention belongs to the technical field of comprehensive energy system optimization and intelligent control, and particularly relates to a comprehensive energy system demand response self-adaptive control method for multi-energy coordinated regulation, which comprises the following steps: identifying multi-energy response resources; performing multi-energy demand response collaborative decomposition; and closed-loop adaptive adjustment and excitation correction: in the instruction execution process, monitoring the deviation between the actual response and the expected response of each energy source in real time, dynamically generating a compensation control instruction based on the deviation and carrying out online correction on excitation parameters so as to realize the closed-loop adaptive control of the response. According to the method, the sensitivity and prediction precision of the system to load change can be remarkably improved, and the flexible regulation and control capability and response stability of the integrated energy system are enhanced from the source.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization and intelligent control technology, specifically relating to an adaptive control method for demand response of integrated energy systems oriented towards multi-energy coordinated regulation. Background Technology

[0002] With the continuous optimization of the energy structure, Integrated Energy Systems (IES), as an important technological form for achieving multi-energy complementarity and efficient utilization, are gradually becoming the core support platform for regional energy management and intelligent control. This system integrates multiple energy carriers such as electricity, heat, gas, and energy storage within a unified framework, achieving spatiotemporal coupling and coordinated operation of energy, thereby significantly improving energy utilization efficiency and operational economy. However, at present, Integrated Energy Systems still face significant technical challenges in the Demand Response (DR) stage. On the one hand, there are complex physical coupling relationships between different energy sources in multi-energy systems. For example, the energy conversion efficiency, transmission delay, and coupling coefficients of electric heat pumps, gas boilers, and energy storage units vary, leading to difficulties in coordinating multi-energy responses. The system is prone to execution deviations or time lags when faced with unified commands. On the other hand, the dynamic response characteristics of different types of equipment differ significantly. For example, electrical loads change rapidly, thermal loads have large inertia, gas equipment has ramp-up limitations, and energy storage equipment is strongly affected by capacity and power constraints. This makes traditional single-energy optimization models unable to effectively characterize their heterogeneity, limiting response accuracy and executability.

[0003] Furthermore, existing demand response control models generally employ static incentive mechanisms and open-loop optimization control, failing to establish a dynamic correction mechanism based on feedback data. This makes it difficult for the system to achieve continuous and stable operation under load fluctuations or environmental disturbances. Fixed incentive parameters cannot reflect the real-time status of equipment and execution deviations, easily leading to insufficient response responsiveness, energy distribution imbalances, and increased overall incentive costs. Meanwhile, traditional control strategies are mostly based on one-time optimization results, lacking rolling forecasting and closed-loop feedback mechanisms, and cannot correct execution errors and model uncertainties online. In dynamic environments, the system is prone to response degradation and even oscillation instability. Therefore, how to achieve adaptive closed-loop control of demand response under multi-energy coupling conditions, and how to construct a dynamic control method that can automatically correct sensitivity coefficients and incentive parameters based on operating status, has become a key technological direction for the intelligent development of integrated energy systems. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive control method for demand response of a comprehensive energy system oriented towards multi-energy coordinated regulation, so as to solve the problems existing in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an adaptive control method for demand response of a comprehensive energy system oriented towards multi-energy coordinated regulation, characterized by comprising the following steps:

[0006] Step S1, Multi-energy response resource identification: Collect operating data of electric, heat, gas and energy storage equipment, calculate its real-time adjustability margin based on the dynamic response model of the equipment, and combine it with the feasibility assessment to form a candidate set of multi-energy response resources;

[0007] Step S2, Multi-energy Demand Response Coordination Decomposition: Receive the unified demand response command at the system level, establish a decomposition model that integrates energy coupling relationships and sensitivity coefficients, and optimize the solution under the conditions of minimizing execution cost and satisfying energy balance and operation constraints to generate a sequence of coordinated response commands for each energy source, including electricity, heat, gas, and storage.

[0008] Step S3, Closed-loop adaptive adjustment and excitation correction: During the execution of the command, the deviation between the actual response and the expected response of each energy source is monitored in real time. Based on the deviation, a compensation control command is dynamically generated and the excitation parameters are corrected online to achieve closed-loop adaptive control of the response.

[0009] Preferably, step S1 includes:

[0010] S1-1: Collect historical and real-time operating data of electrical power, thermal power, gas flow rate and energy storage charge status, and construct a unified time-series-device-state data mapping set;

[0011] S1-2: Establish a first-order simplified dynamic model for each type of equipment. The model describes the relationship between the control quantity and the output response. Based on the model and the upper and lower limits of equipment operation, calculate the real-time adjustability margin of the equipment.

[0012] S1-3: Introduce an executability factor to weight and correct the adjustable margin, and deduct the risk margin caused by prediction uncertainty to obtain an effective adjustable margin; include resources whose effective adjustable margin meets a preset threshold into the multi-energy response resource candidate set.

[0013] Preferably, the formula for calculating the effective adjustable margin is:

[0014]

[0015] Where, Δ i (t) represents the adjustability margin of device i at time t, σ i (t) represents the standard deviation of the prediction error, κ is the risk coefficient that is greater than zero, and ρ i (t)∈[0,1] is the executability factor characterizing the reliability of the device response.

[0016] Preferably, step S2 specifically includes:

[0017] S2-1: Construct an energy coupling matrix and a sensitivity matrix to characterize the conversion relationship between the four energy sources of electricity, heat, gas and storage and their marginal contribution to the unified demand response index, and establish a decomposition equation constraint Sx = D(t), where D(t) is the unified demand response command and x is the response component vector of each energy source.

[0018] S2-2: Define an execution cost function for each energy response component, the cost function including at least the equipment loss cost proportional to the square of the response and the comfort / process deviation penalty proportional to the absolute value of the response;

[0019] S2-3: With the goal of minimizing the sum of execution costs of all energy sources, and coupling energy balance, network power flow, equipment power and energy storage state constraints, construct and solve the optimization model to obtain the optimal response allocation vector;

[0020] S2-4: Based on the optimal response allocation vector, generate a multi-energy collaborative response instruction sequence for multiple future scheduling periods.

[0021] Preferably, the execution cost function is in the form of a quadratic function:

[0022] Where, α i Reflecting wear / efficiency degradation costs, β i Reflecting the penalty for deviations in comfort / craftsmanship, γ i This is a fixed item.

[0023] Preferably, step S3 specifically includes:

[0024] S3-1: In each control cycle, calculate the deviation ε(t) between the expected response trajectory and the actual response measurement;

[0025] S3-2: Using a proportional-integral-derivative control law, the compensation control quantity is dynamically calculated based on the deviation ε(t), and a safety constraint projection is applied to the compensation control quantity to ensure that the system operates within a safe range;

[0026] S3-3: Based on the deviation ε(t) and the actual response of the equipment, the excitation coefficient is adaptively corrected online, and the correction makes the excitation level match the response execution effect.

[0027] Preferably, the online adaptive correction formula for the excitation coefficient is:

[0028]

[0029] Where λi(t) is the excitation coefficient of device i at time t, and ηλ is the learning rate. Let ζ be the empirical sensitivity, and ζ be the damping coefficient used to suppress over-excitation. This means that the result will be projected onto the preset upper and lower limits of the excitation coefficient.

[0030] Preferably, the method further includes an anomaly handling mechanism: when a device is detected to be offline or its performance is degraded, its executability weight is automatically reduced or it is removed from the response candidate set, and the re-optimization process in step S2 is triggered to reallocate response instructions based on the remaining resources.

[0031] Preferably, steps S2 and S3 are executed within a rolling time-domain optimization framework, which can periodically update response commands and control parameters based on the latest system state and prediction information.

[0032] The beneficial effects of this invention are as follows: First, by constructing a multi-energy response identification mechanism based on multi-energy device operation data and a response capability assessment model, this invention enables unified response capability perception and dynamic margin calculation across electric, thermal, gas, and energy storage systems. This mechanism can accurately identify the system's adjustability potential under various operating scenarios, forming a candidate set of multi-energy response resources, providing high-quality data support for subsequent demand response decomposition and optimization. Compared with traditional single-energy response methods, this invention can significantly improve the system's sensitivity to load changes and prediction accuracy, thereby enhancing the flexible regulation capability and response stability of the integrated energy system from the source.

[0033] Secondly, this invention establishes a multi-energy demand response decomposition model that integrates response sensitivity coefficients and execution cost functions, enabling cross-energy type response task allocation and optimized scheduling. Based on a comprehensive consideration of the energy coupling relationship between electricity, heat, gas, and storage, this model dynamically calculates the optimal response scheme, allowing the system to achieve minimum cost and maximize response coordination while meeting constraints. By introducing a multi-objective optimization solution mechanism, this invention achieves a balance between response economy, flexibility, and reliability, solving problems such as uneven energy distribution, equipment response imbalance, and low system efficiency in traditional methods, thereby significantly improving the overall coordination and economy of the system operation.

[0034] Finally, the closed-loop adaptive adjustment mechanism based on execution deviation feedback and online excitation correction proposed in this invention can compare the difference between the expected response and the actual response in real time, dynamically generate compensation adjustment commands and correct excitation parameters, thereby achieving self-learning and self-optimization of the system. During operation, this mechanism can quickly handle and re-optimize parameters for equipment anomalies, performance degradation, and other situations by continuously updating the sensitivity matrix and constraint interval, thus ensuring that the system maintains a high-efficiency and stable operating state under dynamic disturbance environments. Attached Figure Description

[0035] Figure 1This is the overall flowchart of the present invention;

[0036] Figure 2 This is a schematic diagram of the functional relationship between the modules of the present invention;

[0037] Figure 3 This is a flowchart of the multi-energy collaborative optimization sub-process in this invention;

[0038] Figure 4 This is a flowchart of the closed-loop adaptive adjustment and excitation correction process in this invention. Detailed Implementation

[0039] like Figure 1 As shown, this invention proposes an adaptive demand response control method for integrated energy systems oriented towards multi-energy coordinated regulation. Its overall structure includes three main modules: a multi-energy response identification module, a multi-energy demand response decomposition and optimization module, and a closed-loop adaptive adjustment and excitation correction module. The system first constructs a multi-energy coordinated response identification and evaluation mechanism by real-time acquisition of operating data from electricity, heat, gas, and energy storage devices. Then, based on the response sensitivity matrix and execution cost function, it performs multi-energy demand response decomposition and coordinated optimization. Finally, in the closed-loop adjustment layer, it implements deviation feedback and adaptive correction of the excitation coefficients, thereby forming a dynamically feasible, stable, and efficient multi-energy coordinated control system.

[0040] The implementation environment of this embodiment is a regional integrated energy system that incorporates multiple energy forms, including electricity, heat, gas, and energy storage. This system is equipped with a data acquisition and monitoring control system, an energy management system, and an advanced measurement system, capable of real-time acquisition of electrical load power Pe, heating network flow rate Qh, and supply and return water temperatures T. in / out The flow rate Fg and pressure pg of the gas pipeline network, and the charging and discharging power of the energy storage equipment. and State of Charge (SOC)

[0041] like Figure 2 As shown, step S1 is the specific implementation process of the multi-energy response identification module. This module includes four stages: data acquisition, equipment operation modeling, adjustability margin calculation, and response executability assessment. Through quantitative modeling of the operating states of the power load, heat load, gas unit, and energy storage unit, the real-time adjustability margin Δ is calculated. i (t) and combined with the equipment performance coefficient ρ i (t), generating a candidate set of multi-energy response resources. This module enables the identification of the adjustable potential and quantification of the response capabilities of various energy carriers in an integrated energy system, providing a data foundation for subsequent multi-energy collaborative decomposition and optimization.

[0042] In step S1, among the multi-energy units such as electricity, heat, and gas, adjustable resources are identified and their executable real-time adjustability margins are quantified, and a candidate resource set is output. With the multi-energy response vector R(t). The position and function of this step in the overall method are described in the template under the definition of “S1 perception / recognition - forming candidate set”.

[0043] S1-1 Data Acquisition and Unified Mapping

[0044] Collect multi-source data from systems such as SCADA / EMS / AMI: Electric power P e Voltage U e Heat flow Q h With supply and return water temperature T in / out Gas flow rate F g With pressure p g Energy storage charging and discharging power Together with the SOC; simultaneously read day-ahead forecasts, short-term forecasts, and scenario parameters to construct a time-series-device-state mapping set.

[0045] S1-2 Equipment Dynamics and Margin Characterization

[0046] Establish first-order identifiable dynamics for each type of device:

[0047] x i (t+1)=a i x i (t)+b i u(t),y i (t)=c i x i (t)

[0048] Define real-time adjustable margin (combined expression for upward / downward adjustment):

[0049]

[0050] Summarized into a multi-energy response vector:

[0051]

[0052] S1-3 Feasibility and Reliability Margin

[0053] To avoid "paper capacity", an executability ρ is introduced. i (t)∈[0,1] and uncertainty variance To obtain effective margin:

[0054]

[0055] when Timely inclusion It also binds scene / time period labels for use in S2 decomposition and optimization. The format of "threshold-candidate set-label binding" is consistent with the template.

[0056] In step S2, the unified response demand D(t) is optimally decomposed among electricity / heat / gas / storage, taking into account constraints such as energy coupling, execution cost, and network / comfort, to generate a multi-energy collaborative execution sequence.

[0057] S2-1 Energy Coupling and Sensitivity Modeling

[0058] The mapping and marginal contribution (η) of the "multi-energy-unified index" are represented by the coupling matrix M and the sensitivity matrix S. eh ,η hg ,η gs Equivalence values ​​are assigned based on the project:

[0059] Sx=D(t),x=[x e x h x g x s ] T

[0060] S2-2 Cost Model and Constraint Set

[0061] Cost of each energy unit:

[0062]

[0063] constraint:

[0064]

[0065] Where Gx≤b includes cross-network constraints such as power flow / voltage of the power grid, temperature / pressure difference of the heating network supply and return, pressure / flow of the gas network, and SOC recursion and power limit of energy storage.

[0066] S2-3 Decomposition Optimization and Sequence Generation

[0067] Static single-period model:

[0068]

[0069] Sx=D(t),x∈x(t),Gx≤b

[0070] Multi-period scrolling (window) ):

[0071]

[0072] The optimal allocation x can be obtained by using quadratic programming, interior point method, or multiplier method. * (t: t+H), and output the instruction sequence U(t) (electric power / thermal valve position / gas valve / storage charging and discharging plan).

[0073] In step S3, compensation instructions are generated based on measurement feedback during real-time operation, and excitation parameters are corrected online to improve executability and stability; the format follows the closed-loop concept of template S3: "execution / strategy generation / optimization solution - formation of candidate solutions".

[0074] S3-1 Deviation Detection and Compensation Control

[0075] Expectations vs. Reality:

[0076]

[0077] Compensation Law (PI-D):

[0078]

[0079] And apply a secure projection:

[0080]

[0081] S3-2 Online Correction of Excitation Parameters

[0082] Incentive coefficient:

[0083]

[0084] Where ηλ is the learning rate, φi is the empirical sensitivity, and ζ is the damping coefficient to suppress over-excitation. This formula establishes a positive correlation between excitation and performance, and reduces the excitation for equipment that performs poorly.

[0085] This enables incentives to adaptively match performance, suppressing overcompensation and incentive oscillations.

[0086] S3-3 Rolling Updates and Anomaly Handling

[0087] Rolling updates every cycle With sets Device offline / degraded reduces ρ i Alternatively, resources can be temporarily removed, and the remaining resources can be prioritized based on benefits, risks, and flexibility. After re-optimization, a new round of instructions can be issued to achieve an integrated closed loop of "rolling correction, anomaly handling, and priority scheduling".

[0088] When the system detects that a device is offline or its performance has severely degraded (e.g., its actual response continuously deviates from the command by more than a threshold), it automatically sets its executability ρi(t) to zero or temporarily removes it from the candidate set R(t). Subsequently, the system immediately triggers the re-optimization process in step S2, reallocating response commands based on the remaining effective resources and prioritizing the scheduling of resources with high efficiency and low risk, thereby ensuring the stability and robustness of the system's response under abnormal operating conditions.

[0089] This invention achieves full-process adaptive closed-loop control of integrated energy system demand response from "sensing-decision-execution-feedback" through the cyclical execution of the above three steps, effectively improving the accuracy, efficiency and stability of multi-energy coordinated regulation.

[0090] It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention.

Claims

1. A method for multi-energy coordinated demand response adaptive control of integrated energy system, characterized in that, The method comprises the following steps: Step S1, multi-energy response resource identification: collecting operation data of electricity, heat, gas and energy storage equipment, calculating the real-time adjustable margin of the equipment based on the dynamic response model of the equipment, and combining the executable degree evaluation to form a multi-energy response resource candidate set; Step S2, multi-energy demand response collaborative decomposition: receiving a unified demand response instruction at the system level, establishing a decomposition model integrating energy coupling relationship and sensitivity coefficient, and optimizing and solving under the condition of meeting energy balance and operation constraints to minimize the execution cost to generate collaborative response instruction sequences of electricity, heat, gas and storage; Step S3, closed-loop adaptive adjustment and incentive correction: in the process of instruction execution, the deviation between the actual response and the expected response of each energy is monitored in real time, a compensation control instruction is dynamically generated based on the deviation, and the incentive parameter is online corrected to realize closed-loop adaptive control of the response.

2. The method of claim 1, wherein, The step S1 comprises: S1-1: Collecting historical and real-time operation data of electric power, thermal power, gas flow and energy storage state of charge, and constructing a unified time sequence-equipment-state data mapping set; S1-2: A first-order simplified dynamic model is established for each type of equipment, which describes the relationship between the control quantity and the output response, and calculates the real-time adjustable margin of the equipment based on the model and the upper and lower limits of the equipment operation; S1-3: Introducing an executable degree factor to weight and correct the adjustable margin, and deducting the risk margin caused by prediction uncertainty to obtain an effective adjustable margin; the resources whose effective adjustable margin meet the preset threshold are included in the multi-energy response resource candidate set.

3. The method of claim 2, wherein, The calculation formula of the effective adjustable margin is: where Δ i (t) is the adjustable margin of equipment i at time t, σ i (t) is the standard deviation of the prediction error, κ is a risk factor greater than zero, ρ i (t) ∈ [0, 1] is an executable degree factor representing the reliability of the equipment response.

4. The method of claim 1, wherein, The step S2 specifically comprises: S2-1: Constructing an energy coupling matrix and a sensitivity matrix to represent the conversion relationship between electricity, heat, gas and storage and their marginal contribution to the unified demand response index, and establishing a decomposition equation constraint Sx=D(t), wherein D(t) is a unified demand response instruction, and x is a response component vector of each energy; S2-2: Defining an execution cost function for each energy response component, which at least includes a device loss cost proportional to the square of the response quantity and a comfort / process deviation penalty proportional to the absolute value of the response quantity; S2-3: Taking the minimization of the sum of the execution costs of all energies as the target, coupling the energy balance, network flow, device power and storage state constraints, constructing and solving an optimization model to obtain an optimal response allocation vector; S2-4: Based on the optimal response allocation vector, a multi-energy collaborative response instruction sequence for a plurality of scheduling periods in the future is generated.

5. The method of claim 4, wherein, The execution cost function is in the form of a quadratic function: where α i reflects the wear / efficiency degradation cost, β i reflects the comfort / process deviation penalty, γ i is a constant term.

6. The method of claim 1, wherein, The step S3 specifically comprises: S3-1: In each control period, the deviation ε(t) between the expected response trajectory and the actual response measurement is calculated; S3-2: A proportional-integral-derivative control law is adopted to dynamically calculate a compensation control quantity according to the deviation ε(t), and a safety constraint projection is applied to the compensation control quantity to ensure that the system operates within a safe range; S3-3: According to the deviation ε(t) and the actual response quantity of the equipment, the incentive coefficient is online self-adaptive corrected, and the correction makes the incentive level match the response execution effect.

7. The method of claim 6, wherein, The online self-adaptive correction formula of the incentive coefficient is: where λi(t) is the incentive coefficient of device i at time t, ηλis the learning rate, is the empirical sensitivity, ζ is the damping coefficient for suppressing excessive incentives, represents projecting the result into the preset upper and lower limits of the incentive coefficient.

8. The method of claim 1, wherein, The method further comprises an abnormality handling mechanism: when detecting that a device is offline or performance degradation, automatically reducing the executable degree weight thereof or removing it from the response candidate set, and triggering a re-optimization process of the step S2, re-distributing response instructions based on remaining resources.

9. The method of claim 1, wherein, The steps S2 and S3 are executed in a rolling horizon optimization framework, and can periodically update response instructions and control parameters according to the latest system state and prediction information.

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