Coordinated optimization and dispatching method and system of integrated energy system considering dynamic characteristics

Abstract

This invention belongs to the field of energy dispatching technology and provides a method and system for collaborative optimization dispatching of integrated energy systems considering dynamic characteristics. The method includes: calculating and comparing the benefits of different energy supply schemes based on source-load data and an outer-loop static optimization model to obtain a static hourly dispatching scheme; extracting and discretizing the full-cycle static output curves of each device based on the static hourly dispatching scheme; obtaining the dynamic output curves of each device based on the discretized full-cycle static output curves and an inner-loop dynamic optimization model; calculating the difference between the dynamic and static output curves of each energy conversion device in each optimization cycle, and then performing integration in the time domain to obtain the energy supply gap or surplus; translating the dynamic output curves in the time domain to reduce the energy level deviation until the integral of the deviation between the translated dynamic and static output curves is zero, thus obtaining the optimal dispatching scheme.

Description

Technical Field

[0001] This invention belongs to the field of energy dispatching technology, and in particular relates to a comprehensive energy system collaborative optimization dispatching method and system that considers dynamic characteristics. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] An integrated energy system is an energy supply system that coordinates the operation of multiple energy sources. It can uniformly regulate different forms of energy, such as cooling, heating, electricity, and gas, to achieve complementary use of multiple energy sources. Integrated energy systems, characterized by multi-energy complementarity ("cooling-heating-electricity-gas") and coordinated operation ("source-storage-load"), provide support for improving energy utilization and renewable energy absorption. Ensuring stable, high-quality, and efficient energy output from integrated energy systems is an effective way to improve energy utilization and address current energy shortages and environmental crises. However, due to the diverse characteristics of energy conversion equipment within the system, the tight coupling of multiple energy flows, and the varying time scales for optimizing heterogeneous energy flows, optimizing the operation of integrated energy systems involves electrical science, thermodynamics, control science, big data technology, and optimization theory. Achieving optimal operation of integrated energy systems and ultimately reaching the goals of energy conservation, emission reduction, cost reduction, and efficiency improvement is extremely difficult.

[0004] Current research on the optimal scheduling of integrated energy systems draws on the multi-timescale control methods of power grids, placing various heterogeneous energy flows such as heating, cooling, and electricity within the system under the same optimal timescale and achieving multi-timescale optimal scheduling by setting multiple different optimization cycles. Most optimal scheduling research focuses on steady-state effects, neglecting the dynamic response process. In fact, power output can achieve an instantaneous response to dispatch commands. However, the response of heating or cooling equipment to dispatch commands requires a certain period of dynamic change to reach a steady-state value. For integrated energy systems containing deeply coupled heterogeneous energy flows and energy conversion equipment with significantly different dynamic response speeds, the inventors found that traditional static optimal scheduling methods cause deviations between the actual operation of the equipment and the dispatch decision results, leading to source-load mismatch and keeping the equipment in a state of constant change, thus impairing equipment performance. Summary of the Invention

[0005] To address the technical problems existing in the background art, the present invention provides a method and system for collaborative optimization scheduling of integrated energy systems that considers dynamic characteristics. It is based on a dual-ring optimization architecture and comprehensively considers dynamic and static characteristics to collaboratively optimize the scheduling of integrated energy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a comprehensive energy system collaborative optimization scheduling method considering dynamic characteristics, comprising:

[0008] Based on source-load data and an outer-loop static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time, the benefits of different energy supply schemes are calculated and compared to obtain a static hourly scheduling scheme.

[0009] Based on the static hourly scheduling scheme, the full-cycle static output curve of each device is extracted and discretized.

[0010] Based on the discretized full-cycle static output curves of each device and the inner-loop dynamic optimization model, the dynamic output curves of each device are obtained.

[0011] Calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integration in the time domain to obtain the energy supply gap or surplus.

[0012] Based on the principle of energy conservation, the dynamic output curve is translated in the time domain to reduce the deviation at the energy level until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, thus obtaining the optimal scheduling scheme based on the dynamic model.

[0013] As one implementation method, the outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment.

[0014] The advantages of the above technical solution are that the outer-loop static optimization model calculates and compares the benefits of different energy supply methods simultaneously. Different solutions compete with each other from a global perspective, aiming to minimize daily operating costs and carbon emissions while maximizing the steady-state operating time of equipment, while meeting the load demand constraints. During the outer-loop optimization process, the theoretical hourly output plans for equipment such as generators, chillers, heat pumps, energy storage devices, power grids, and photovoltaic systems can be obtained.

[0015] As one implementation, the device COP input-output relationship is a performance function of the energy device under multiple steady-state operating conditions, characterized by a polynomial fitting method based on steady-state operating data.

[0016] In one implementation, the energy balance includes electrical balance, thermal balance, and cold balance.

[0017] As one implementation method, the inner loop dynamic optimization model is a dynamic model of equipment based on an autoregressive ergodic model, which analyzes the change process and development law of equipment dynamic data continuously observed within a preset period to characterize the equipment's variable operating condition capability.

[0018] The advantage of the above technical solution lies in that, based on the outer-loop theoretical hourly output scheme, a scheduling scheme considering the dynamic response and energy shift of each energy conversion device is obtained. This scheme will reduce or even resolve the deviation between actual operation and decision results. The existence of the dual optimization loop realizes the coordination and unification of dynamic and static models. The key to eliminating deviation through dual-loop collaborative optimization is simulating dynamic response, power integration, and energy shift.

[0019] A second aspect of the present invention provides a comprehensive energy system collaborative optimization scheduling system considering dynamic characteristics, comprising:

[0020] The static hourly scheduling scheme determination module is used to determine the static hourly scheduling scheme based on source load data and outer ring static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time. It also calculates and compares the benefits of different energy supply schemes.

[0021] The static output curve discretization module is used to extract and discretize the full-cycle static output curve of each device based on the static hourly scheduling scheme.

[0022] The dynamic output curve solving module is used to obtain the dynamic output curve of each device based on the discretized full-cycle static output curve and inner-loop dynamic optimization model of each device.

[0023] The energy level power supply determination module is used to calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integration calculation in the time domain to obtain the energy level power supply gap or surplus.

[0024] The optimal scheduling scheme determination module is used to reduce the deviation at the energy level by translating the dynamic output curve in the time domain according to the principle of energy conservation, until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, thus obtaining the optimal scheduling scheme based on the dynamic model.

[0025] As one implementation method, the outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment.

[0026] As one implementation method, the device COP input-output relationship is a performance function of the energy device under multiple steady-state operating conditions, characterized by a polynomial fitting method based on steady-state operating data.

[0027] In one implementation, the energy balance includes electrical balance, thermal balance, and cold balance.

[0028] As one implementation method, the inner loop dynamic optimization model is a dynamic model of equipment based on an autoregressive ergodic model, which analyzes the change process and development law of equipment dynamic data continuously observed within a preset period to characterize the equipment's variable operating condition capability.

[0029] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described integrated energy system collaborative optimization scheduling method considering dynamic characteristics.

[0030] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described above.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] (1) This invention is based on a dual-ring optimization architecture and a collaborative optimization scheduling method that comprehensively considers dynamic and static characteristics. It deeply integrates static analysis, energy flow analysis and dynamic analysis. In the energy optimization scheduling process, it takes into account the combined influence of the static and dynamic characteristics of the equipment to the greatest extent. The outer optimization ring completes static collaborative scheduling, giving full play to the environmental protection and economy of the system. The inner optimization ring completes dynamic adjustment to achieve source-load matching.

[0033] (2) This invention employs a polynomial fitting method to characterize the performance function of energy equipment under multiple steady-state operating conditions. By systematically identifying and describing the dynamic response process and variable operating condition capabilities of different energy equipment, static full-condition and dynamic response models of the equipment are established. By using power time-domain integration, dynamic simulation, and energy translation to balance the deviation, the discrepancy between the scheduling decision results and the actual operation is reduced, thereby further improving the energy-saving, emission-reduction, cost-reduction, and efficiency-enhancing capabilities of the integrated energy system.

[0034] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0035] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0036] Figure 1 This is the dual-ring optimized architecture of this invention embodiment;

[0037] Figure 2 This is a flowchart of a comprehensive energy system collaborative optimization scheduling method considering dynamic characteristics, according to an embodiment of the present invention. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Example 1

[0042] To reduce the deviation between the actual operation of the system and the scheduling decision results, this embodiment proposes a collaborative optimization scheduling method based on a dual-loop optimization architecture, comprehensively considering dynamic and static characteristics, to optimize the daily operation of the integrated energy system. The outer optimization loop completes static collaborative scheduling, fully leveraging the system's environmental friendliness and economic efficiency, while the inner optimization loop completes dynamic adjustments to achieve source-load matching. Considering the equipment control characteristics, a polynomial fitting method is used to characterize the performance functions of energy equipment under multiple steady-state operating conditions. The dynamic response process and variable operating condition capabilities of different energy conversion devices are described through system identification, and heterogeneous energy sources with different physical and control characteristics are described through multi-timescale hybrid modeling. Energy deviations are balanced through dynamic system simulation, power time-domain integration, and energy translation.

[0043] This embodiment proposes a dual-ring optimized architecture, such as Figure 1As shown, the outer-loop static scheduling optimizer calculates and compares the benefits of different energy supply methods simultaneously. Different schemes compete with each other from a global perspective, aiming to minimize daily operating costs and carbon emissions while maximizing equipment steady-state operating time, all while meeting load demand constraints. During the outer-loop optimization process, theoretical hourly output plans for generators, chillers, heat pumps, energy storage devices, the power grid, and photovoltaic systems are obtained. Subsequently, the inner-loop dynamic adjustment optimizer, based on the theoretical hourly output plans of the outer loop, derives a scheduling scheme that considers the dynamic responses and energy shifts of each energy conversion device. This scheme reduces or even eliminates the deviation between actual operation and decision results. The existence of the dual optimization loops achieves synergy and unification between the dynamic and static models. The key to eliminating deviations through dual-loop collaborative optimization is simulating dynamic response, power integration, and energy shifting.

[0044] This embodiment decouples heterogeneous energy sources at different time scales, establishes a steady-state operation and dynamic response model for the energy conversion device, improves the rationality of the multi-objective collaborative optimization model in practical engineering applications, and thus reduces the deviation between scheduling decision results and actual system operation.

[0045] Reference Figure 2 The integrated energy system collaborative optimization scheduling method considering dynamic characteristics in this embodiment includes the following specific process:

[0046] Step 1: Based on source-load data and the outer ring static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time, the benefits of different energy supply schemes are calculated and compared to obtain a static hourly scheduling scheme.

[0047] To ensure the model is applicable to energy efficiency analysis in field studies and actual operation, the energy balance relationship of the integrated energy system was first analyzed, and the optimization variables were determined. Then, a multi-objective collaborative optimization model was established, aiming to minimize operating costs and carbon emissions while maximizing equipment steady-state operating time. Finally, constraints were determined based on energy balance and input-output relationships.

[0048] Specifically, the outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment. The outer-loop static optimization model calculates and compares the benefits of different energy supply methods simultaneously. Different schemes compete with each other from a global perspective, with the goal of minimizing daily operating costs and carbon emissions while maximizing the steady-state operating time of the equipment, while satisfying the load demand constraints. During the outer-loop optimization process, the theoretical hourly output plans of equipment such as generators, chillers, heat pumps, energy storage devices, the power grid, and photovoltaic systems can be obtained.

[0049] The objective function, energy balance equation, and equipment COP input-output relationship constitute the static optimization scheduling model. Considering the relationship between operating costs, carbon emissions, and equipment steady-state operating time, the objective function F of the static optimization model is expressed as follows:

[0050] F = min f1 + min f2 + max f3

[0051] Among them, f1, f2 and f3 are the daily operating cost, carbon emissions and steady-state operating time of the integrated energy system, respectively.

[0052] The daily operating costs of an integrated energy system include the cost of purchasing electricity from the grid and the cost of purchasing natural gas from a gas station. The objective function for daily operating costs is as follows:

[0053] f1=∑[E grid (t)p grid (t)+F gas (t)p gas (t)]

[0054] Where, p grid (t) and p gas (t) represents the electricity price and the natural gas price, respectively.

[0055] Carbon emissions from an integrated energy system originate from indirect carbon emissions from electricity purchased from the grid and direct carbon emissions from burning natural gas. The objective function for daily carbon emissions is as follows:

[0056] f2=∑[E grid (t)e grid (t)+F gas (t)e gas (t)]

[0057] Among them, e grid and e gas (t) represents the carbon emission coefficients of the power grid and natural gas, respectively.

[0058] To ensure the steady-state and efficient operation of energy equipment, avoid frequent changes in equipment operating conditions, and further prevent equipment performance degradation, the objective function for the steady-state operating time of the equipment is expressed as follows:

[0059] f3=∑[S on (t)-S startup (t)-S shutdown (t)]

[0060] Among them, S on (t), S startup (t) and S shutdown (t) represent the start and stop states, respectively. When S on(t) = 1, which means the device is in operation when S... startup (t) = 1 or S shutdowm (t) = 1, which means that the equipment is in a variable operating condition.

[0061] In the optimal scheduling of integrated energy systems, balancing multiple energy flows is crucial to ensuring that all energy demands are met. The power balance of an integrated energy system is represented as follows:

[0062] E pgu (t)+E re (t)+E grid (t)+E es (t)-E cons (t)-ΔE(t)=E load (t)

[0063] Among them, E pgu (t) and E re (t) represents the electricity generated by generators and renewable energy sources, E grid (t) represents the purchase (E) from the power grid grid (t)>0) or sell to the grid (E) grid Electricity E (t) < 0) es (t) represents the input (E) of the energy storage device. es (t)<0) or output (E) es (t)>0), E cons E(t) and ΔE(t) represent the power consumption of other equipment and the line loss, respectively. load (t) represents the electrical load.

[0064] The integrated energy system provides heating or cooling by relying on the flow of hot and cold water in pipes to transfer energy; the water flow acts only as an energy medium and is not consumed itself. During the heating season, the heat generated by the integrated energy system is transferred to the building floors for heating through the circulating pipe network. The heat balance of the integrated energy system is expressed as:

[0065] H hp (t)+H gb (t)+H pgu (t)+H tes (t)-ΔH(t)=H load (t)

[0066] Among them, H hp (t), H gb (t) and H pgu (t) represents the heat output of the heat pump, gas boiler, and generator, respectively. H tes (t) is the input (H) of the thermal storage device. tes (t)<0) or output (H) tes (t)>0). ΔQh (t) is the heat loss of the circulating pipe network, H load (t) represents the heating load.

[0067] The processes of cooling and heating are similar, only the direction of heat transfer is reversed. During the cooling season, heat from the building is carried away by the circulating pipe network, transferred to the integrated energy system, and finally discharged through the cooling water system. The cold balance in the integrated energy system is represented as follows:

[0068] C hp (t)+C dc (t)+C ac (t)+C ces (t)-ΔC(t)=C load (t)

[0069] Among them, C hp (t), C dc (t) and C ac (t) represents the cooling capacity of the heat pump, direct-fired chiller, and absorption chiller, respectively. C ces (t) represents the input (C) of the cooling and storage device. ces (t)<0) or output (C) ces (t)>0), ΔC(t) is the cooling loss of the circulating pipe network, C load (t) represents the cooling load.

[0070] For short-distance energy transmission in integrated energy systems, the resistance of transmission lines is very small, resulting in losses in the transmission lines that are far less than the power load, which can be ignored. However, due to the large temperature difference between the inside and outside of the pipe and the flow velocity limitation, the flow of hot water in the pipe is accompanied by heat loss. For small-scale distributed heating systems, the temperature of the hot water in the pipe can be approximated as constant. For a steady-state thermal network, the expression for heat loss per unit length of pipe is:

[0071]

[0072] Where ΔQ(t) is the heating loss ΔH(t) in winter or the cooling loss ΔC(t) in summer, and T w (t) and T e (t) represents the internal and external temperatures of the pipe, respectively. R pipe The thermal resistance per unit length of pipe, l pipe This is the total length of the heat pipe.

[0073] The COP input-output relationship of the equipment is a performance function of the energy equipment under multiple steady-state operating conditions, characterized by polynomial fitting based on steady-state operating data. The energy balance includes electrical balance, thermal balance, and cold balance.

[0074] Static analysis and energy flow analysis are deeply integrated. For steady-state operating data, a polynomial fitting method is used to characterize the COP function of the energy conversion equipment under multiple steady-state operating conditions. The static input-output relationship of the energy equipment is expressed as follows:

[0075] Q(t)=E(t)COP

[0076] Where E(t) and Q(t) are the equipment's input and output, respectively, and COP is the equipment performance coefficient. Due to changes in operating conditions, COP fluctuates, and its value is related to the equipment's load rate, as detailed below:

[0077] COP = c0 + c1r + ... + c n r n

[0078]

[0079] Where r is the load factor, obtained experimentally, and c0, c1, ..., c n These are the coefficients of a polynomial, obtained by fitting measurement data.

[0080] Step 2: Based on the static hourly scheduling scheme, extract and discretize the full-cycle static output curve of each device.

[0081] For example, hourly data can be discretized into second-level data to meet the requirements of dynamic system simulation.

[0082] Step 3: Based on the discretized full-cycle static output curves of each device and the inner-loop dynamic optimization model, obtain the dynamic output curves of each device.

[0083] In the specific implementation process, the inner-loop dynamic optimization model uses second-level data as input signals to perform dynamic response simulation, obtaining the dynamic output curves of each device. Then, the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle is calculated. This deviation can be approximated as the deviation between scheduling decisions and actual operation.

[0084] The increase or decrease in heat or cold output of energy equipment is a cumulative effect of output following input over time, but it lags behind the input power, exhibiting significant nonlinearity and time delay. This means that the output at the current moment depends not only on the input but also on the current state, which is jointly determined by historical inputs and outputs. For dynamic operating data, the system identification method using the ARX model is employed to describe the dynamic response process of the equipment and further characterize its variable operating condition capabilities.

[0085] This embodiment continuously observes equipment dynamic data over a certain period, analyzes its change process and development pattern, and establishes an equipment dynamic model based on an autoregressive ergodic (ARX) model. The model regressor calculates the regressor value from current and historical input data and historical output data. The nonlinear estimator includes linear and nonlinear functions, which act on the model regressor to obtain the model output. The input-output relationship is as follows:

[0086]

[0087] Where u(t), y(t), and δ(t) are the input, output, and white noise, respectively, a0 is the offset, and a i (i = 1, 2, ..., n) a ) and b j (j = 1, 2, ..., n) b +n k -1) is the coefficient of the autoregressive term, reflecting the influence of past inputs and outputs on the current output. a n b and n k The representative model structure, including the polynomial order and delay parameters, can be determined by minimizing the Akaike information criterion (AIC), as shown below:

[0088]

[0089] Where V is the loss function, d is the total number of parameters in the relevant structure, and N is the number of data points used for estimation.

[0090] Step 4: Calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integration calculation in the time domain to obtain the energy supply gap or surplus.

[0091] Step 5: Based on the principle of energy conservation, the dynamic output curve is translated in the time domain to reduce the deviation at the energy level until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, thus obtaining the optimal scheduling scheme based on the dynamic model.

[0092] The real-time deviation during the dynamic response period reflects the source-load power mismatch, and the cumulative deviation within the optimization cycle represents the energy-level source-load mismatch. To mitigate the negative impact of source-load deviation, the output plans of all energy conversion devices are adjusted to achieve energy balance. Power time-domain integration and energy shifting, without shortening the original power balance duration, ensure energy balance between source and load within each optimization cycle, thereby achieving the goal of adjusting the unit output plan. Ultimately, an optimized scheduling scheme based on dynamic model simulation can be obtained, which mitigates the negative impact of source-load deviation during the dynamic response process.

[0093] The collaborative optimization scheduling method in this embodiment makes full use of information technology and optimization theory, considers the timing status, efficiency and dynamic response characteristics of energy conversion devices under all optimization windows, optimizes energy production, transmission and storage according to load changes, ensures dynamic balance between energy supply and demand, reduces the deviation between actual operation and scheduling decision results caused by the differences in dynamic characteristics of different devices, and further improves the energy conservation, emission reduction, cost reduction and efficiency improvement capabilities of the integrated energy system.

[0094] This embodiment is based on the outer-loop theoretical hour output scheme, resulting in a scheduling scheme that considers the dynamic response and energy shift of each energy conversion device. This scheme will reduce or even resolve the deviation between actual operation and decision results. The existence of the dual optimization loop realizes the coordination and unification of dynamic and static models. The key to eliminating deviation through dual-loop collaborative optimization is simulating dynamic response, power integration, and energy shift.

[0095] The existence of inner and outer loops enables cooperation and unification between dynamic and static models, maximizing the consideration of both static and dynamic characteristics during the optimization scheduling process to reduce source-load mismatch. The core of inner and outer loop collaborative optimization for bias reduction is simulating dynamic response, power integration, and energy shifting.

[0096] Example 2

[0097] This embodiment provides a comprehensive energy system collaborative optimization scheduling system that considers dynamic characteristics, comprising:

[0098] (1) Static hourly scheduling scheme determination module, which is used to determine the static hourly scheduling scheme based on source load data and outer ring static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time, while calculating and comparing the benefits of different energy supply schemes.

[0099] Specifically, the outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment.

[0100] The device COP input-output relationship is a performance function of the energy device under multiple steady-state operating conditions, characterized by a polynomial fitting method based on steady-state operating data.

[0101] The energy balance includes electrical balance, thermal balance, and cold balance.

[0102] (2) Static output curve discretization module, which is used to extract and discretize the full-cycle static output curve of each device based on the static hourly scheduling scheme.

[0103] (3) Dynamic output curve solving module, which is used to obtain the dynamic output curve of each device based on the discretized full-cycle static output curve and inner loop dynamic optimization model.

[0104] The inner loop dynamic optimization model is a dynamic model of equipment based on an autoregressive ergodic model, which analyzes the change process and development law of equipment dynamic data continuously observed within a preset period to characterize the equipment's variable operating condition capability.

[0105] (4) Energy level power supply determination module, which is used to calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integral calculation in the time domain to obtain the energy level power supply gap or surplus.

[0106] (5) Optimal scheduling scheme determination module, which is used to reduce the deviation of the energy level by translating the dynamic output curve in the time domain according to the principle of energy conservation, until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, and obtain the optimal scheduling scheme based on the dynamic model.

[0107] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0108] Example 3

[0109] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described integrated energy system collaborative optimization scheduling method considering dynamic characteristics.

[0110] Example 4

[0111] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described above.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for collaborative optimization and scheduling of integrated energy systems considering dynamic characteristics, characterized in that, include: Based on source-load data and an outer-loop static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time, the benefits of different energy supply schemes are calculated and compared to obtain a static hourly scheduling scheme. Based on the static hourly scheduling scheme, the full-cycle static output curve of each device is extracted and discretized. Based on the discretized full-cycle static output curves of each device and the inner-loop dynamic optimization model, the dynamic output curves of each device are obtained. Calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integration in the time domain to obtain the energy supply gap or surplus. Based on the principle of energy conservation, the dynamic output curve is translated in the time domain to reduce the deviation at the energy level until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, thus obtaining the optimal scheduling scheme based on the dynamic model. 2.The method of claim 1, wherein, The outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment.

3. The integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described in claim 2, characterized in that, The device COP input-output relationship is a performance function of the energy device under multiple steady-state operating conditions, characterized by a polynomial fitting method based on steady-state operating data.

4. The integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described in claim 2, characterized in that, The energy balance includes electrical balance, thermal balance, and cold balance.

5. The integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described in claim 1 or 2, characterized in that, The inner loop dynamic optimization model is a dynamic model of equipment based on an autoregressive ergodic model, which analyzes the change process and development law of equipment dynamic data continuously observed within a preset period to characterize the equipment's variable operating condition capability.

6. A comprehensive energy system collaborative optimization scheduling system considering dynamic characteristics, characterized in that, include: The static hourly scheduling scheme determination module is used to determine the static hourly scheduling scheme based on source load data and outer ring static optimization model, with the goal of minimizing operating costs and carbon emissions and maximizing equipment steady-state operating time. It also calculates and compares the benefits of different energy supply schemes. The static output curve discretization module is used to extract and discretize the full-cycle static output curve of each device based on the static hourly scheduling scheme. The dynamic output curve solving module is used to obtain the dynamic output curve of each device based on the discretized full-cycle static output curve and inner-loop dynamic optimization model of each device. The energy level power supply determination module is used to calculate the difference between the dynamic output curve and the static output curve of each energy conversion device in each optimization cycle, and then perform integration calculation in the time domain to obtain the energy level power supply gap or surplus. The optimal scheduling scheme determination module is used to reduce the deviation at the energy level by translating the dynamic output curve in the time domain according to the principle of energy conservation, until the integral of the deviation between the translated dynamic output curve and the static output curve is zero, thus obtaining the optimal scheduling scheme based on the dynamic model.

7. The integrated energy system collaborative optimization scheduling system considering dynamic characteristics as described in claim 6, characterized in that, The outer-loop static optimization model aims to minimize the daily operating cost, carbon emissions, and steady-state operating time of the integrated energy system. The constraints are determined based on energy balance and the COP input-output relationship of the equipment.

8. The integrated energy system collaborative optimization scheduling system considering dynamic characteristics as described in claim 7, characterized in that, The device COP input-output relationship is a performance function of the energy device under multiple steady-state operating conditions, characterized by a polynomial fitting method based on steady-state operating data. The energy balance may include electrical balance, thermal balance, and cold balance.

9. The integrated energy system collaborative optimization scheduling system considering dynamic characteristics as described in claim 6, characterized in that, The inner loop dynamic optimization model is a dynamic model of equipment based on an autoregressive ergodic model, which analyzes the change process and development law of equipment dynamic data continuously observed within a preset period to characterize the equipment's variable operating condition capability.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the integrated energy system collaborative optimization scheduling method considering dynamic characteristics as described in any one of claims 1-5.

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