Park integrated energy system demand response method based on electric energy substitution

Abstract

The invention discloses a park integrated energy system demand response method based on electric energy substitution, and belongs to the field of power grid operation management. Comprising electric energy replacement potential identification and multi-energy system modeling; multi-energy system modeling and flexible resource identification are carried out; designing a multi-time scale response strategy; and optimizing a scheduling model and a solving algorithm. Structural optimization and dynamic adjustment of the park integrated energy system are realized by fusing an electric energy substitution path and a multi-time scale demand response mechanism; electric energy replacement is used as an adjustable resource for modeling, a terminal load with electrification potential in the park is identified, and the economy and feasibility of converting the terminal load into electric energy driving in different time periods are evaluated; the replaceable loads are incorporated into a system scheduling model and cooperatively controlled with flexible resources including photovoltaic resources, wind power resources, energy storage resources and electric vehicles to form an energy system with a reconfigurable structure and flexible response, so that the green, flexible and intelligent levels of the park energy system are improved.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system management technology, and relates to a multi-timescale demand response method for an integrated energy system in a park based on electricity substitution, which is used to improve the energy utilization efficiency of the park, optimize the energy structure and enhance the flexible regulation capability of the power system. Background Technology

[0002] my country is vigorously developing clean energy and encouraging users to electrify their energy consumption, thereby increasing the proportion of electricity in final energy consumption and the utilization rate of clean energy.

[0003] As a typical energy-intensive area, the industrial park is facing profound changes in its energy consumption patterns and response capabilities. Traditional industrial park energy systems rely heavily on high-carbon energy sources such as coal, oil, and natural gas, resulting in low energy utilization efficiency and rigid load characteristics, which are not conducive to participating in grid interaction and regulation.

[0004] Meanwhile, electricity, as a highly efficient, clean, and controllable end-use energy source, is being widely used to replace traditional fossil fuels, achieving energy substitution pathways such as "electricity replacing coal," "electricity replacing oil," and "electricity replacing gas." Especially in areas such as industrial boilers, heating systems, electric transportation, and hot water supply, electricity substitution has favorable implementation conditions and economic viability. Therefore, electricity substitution has become an important means for industrial parks to build a green and low-carbon energy system and achieve energy electrification and carbon emission reduction targets.

[0005] Faced with challenges such as a high proportion of renewable energy integration, increased peak load pressure on the power system, and significant fluctuations in electricity market prices, the existing energy system in the industrial park lacks the capacity to respond effectively and is unable to participate effectively in the demand response and ancillary services market.

[0006] For example, the invention patent with an authorization announcement date of April 7, 2023, and authorization announcement number CN 115511230 B, discloses "a method for analyzing and predicting the potential of electricity substitution," which includes: obtaining and quantifying the influencing factors of electricity substitution to obtain multiple substitution factors; obtaining the correlation degree between each substitution factor and the electricity substitution amount, and further obtaining a set of subjective and objective weights; fusing the subjective and objective weights to obtain a fused weight; combining the fused weights to correct and fit multiple substitution factors to obtain corrected fitting values ​​based on multiple substitution factors; calculating multiple substitution factors through a test function to obtain test data values; and constructing a prediction model based on the corrected fitting values ​​and test data values ​​based on multiple substitution factors to obtain a prediction estimate. This method analyzes the transfer of existing oil, coal, and natural gas energy consumption demand, assesses the impact of electricity substitution on load, quantifies the factors affecting the electricity substitution amount, obtains substitution factors, obtains a set of subjective and objective weights by obtaining the correlation degree between substitution factors and the electricity substitution amount, and fuses the substitution factors based on the subjective and objective weights (see paragraph

[0066] of its specification). However, the technical solution does not address the issue of optimizing low-carbon economic operation for users, nor does it consider the efficient consumption of clean energy or the improvement of the operational efficiency and stability of the park's integrated energy system.

[0007] The invention patent CN 118410921 B, with an authorization announcement date of September 10, 2024, discloses a "Comprehensive Energy Hub Optimization Method and System Considering the Demand Response Willingness of a Comprehensive Energy System." It accurately assesses the demand response willingness of a comprehensive energy system through an evaluation mechanism using a demand response economic index and a demand response potential index. This simulates different energy usage behaviors and, combined with the demand response willingness and the absorption demand of each energy supply device included in the comprehensive energy system, optimizes energy dispatch to minimize the operating costs of the comprehensive energy system, thereby improving the energy utilization efficiency and economy of the comprehensive energy system. However, this method uses a fuzzy algorithm to evaluate the demand response willingness of a comprehensive energy system based on the demand response economic index and demand response potential index. It does not address the optimization and improvement of the system's demand response capability, nor does it consider the operational efficiency and stability of the comprehensive energy system.

[0008] Clearly, most current electricity substitution projects are mainly static retrofits, lacking coordination and integration with real-time load regulation mechanisms. They have not yet incorporated electricity substitution into the overall framework of demand response, resulting in insufficient overall system regulation capabilities.

[0009] Traditional demand response methods focus on peak-valley pricing strategies or peak shaving and valley filling control, lacking systematic modeling of load substitutability, response flexibility, and time scale differences, and are also difficult to cope with the complex adjustment needs brought about by the volatility of new energy sources.

[0010] Furthermore, with the penetration and integration of various energy forms (such as cooling, heating, electricity, gas, water, energy storage, and electric vehicles) in industrial parks, the construction of integrated energy systems that are multi-energy coordinated, structurally adjustable, and responsive has become a future development trend. However, current research on integrated energy systems largely focuses on equipment integration and scheduling optimization, lacking a systematic approach centered on "electricity substitution + flexible response," particularly in areas such as multi-timescale coordinated control, identification of substitutable loads, and design of tiered response mechanisms.

[0011] Therefore, this invention proposes a demand response method for integrated energy systems in industrial parks based on electricity substitution, which integrates the electricity substitution path with a multi-level flexible response mechanism. This method can effectively improve the greenness of the energy structure and enhance the system's adaptability to changes in external signals (such as electricity prices, load, and carbon emissions), thereby realizing the "low-carbon, flexible, and intelligent" transformation of the energy system in industrial parks. Summary of the Invention

[0012] This invention aims to address the problems of current industrial park energy systems, such as an energy structure biased towards fossil fuels, insufficient demand response capabilities, and difficulties in flexible energy regulation. It proposes a demand response method for integrated industrial park energy systems based on electricity substitution. This technical solution integrates electricity substitution with a multi-timescale load response mechanism to achieve structural optimization and dynamic adjustment of energy-consuming objects within the park. This effectively improves the greenness of the energy structure and enhances the system's adaptability to changes in external signals (such as electricity prices, load, and carbon emissions), thus realizing the "low-carbon, flexible, and intelligent" transformation of the industrial park's energy system.

[0013] The technical solution of this invention is: to provide a demand response method for a comprehensive energy system in a park based on electricity substitution, characterized by including:

[0014] 1) Identification of potential for electricity substitution and multi-energy system modeling: Identify load objects in the park that originally used traditional energy and assess their feasibility and response potential for electricity substitution, forming a structurally adjustable resource pool;

[0015] 2) Multi-energy system modeling and flexible resource identification: Construct a coupled model of the park's integrated energy system, including the dynamic evolution of cooling, heating and power loads and the modeling of adjustable resource characteristics;

[0016] 3) Multi-timescale response strategy design: Construct a multi-timescale response strategy system, divide the response behavior into three control levels: minute, hour and day. Define response objectives, control mechanisms and implementation constraints for different levels to achieve full-process, multi-level response coordination from rapid dynamic response to medium- and long-term structural optimization.

[0017] 4) Optimize scheduling model and solution algorithm: Under the premise of considering operating economy, carbon emission level and response capability, generate the optimal operating strategy for the whole system; call minute-level, hour-level and daily-level strategy inputs in different scheduling cycles to adapt to various resource models, equipment boundaries, load response conditions and operating objectives.

[0018] The proposed demand response method for integrated energy systems in industrial parks based on electricity substitution achieves structural optimization and dynamic adjustment of the integrated energy system by integrating electricity substitution paths with multi-timescale demand response mechanisms. It models electricity substitution as an adjustable resource, identifies end-loads within the park with electrification potential, and assesses the economics and feasibility of converting them to electricity-driven operation at different times. By incorporating these "substitutable loads" into the system scheduling model and coordinating them with flexible resources including photovoltaics, wind power, energy storage, and electric vehicles, a reconfigurable and responsive energy system is formed.

[0019] Specifically, the identification of electricity substitution potential and multi-energy system modeling includes:

[0020] 1.1) Data acquisition and raw load modeling;

[0021] 1.2) Substitution cost comparison model;

[0022] 1.3) Comprehensive evaluation model for alternative feasibility;

[0023] 1.4) Alternative load modeling.

[0024] Specifically, the multi-energy system modeling and flexible resource identification include:

[0025] 2.1) Energy system load modeling;

[0026] 2.2) Distributed resource modeling;

[0027] 2.3) Flexible resource identification.

[0028] Furthermore, the multi-timescale response strategy design includes a hierarchical strategy architecture and strategy expression. The hierarchical strategy architecture involves constructing a multi-timescale response strategy architecture, dividing the control strategies into three levels: minute-level, hour-level, and day-level, to achieve hierarchical response and coordinated control of the park's integrated energy system to external signals. The three-level strategies operate collaboratively using a constraint transmission and rolling correction mechanism between upper and lower levels, forming a rapid, precise, economical, efficient, and long-term low-carbon multi-level response system. The strategy expression includes constructing corresponding response objective functions and strategy expression forms for the minute-level, hour-level, and day-level response, respectively, incorporating the adjustment behavior of various resources into a unified and solvable strategy model framework. The strategy expression for each time level includes three parts: objective function construction, control variable definition, and constraint condition setting, to ensure that the strategy is executable, computable, and controllable.

[0029] Furthermore, the minute-level strategy focuses on rapid response to grid frequency fluctuations and new energy disturbances, relying on resources including energy storage and fast start-stop loads, and has a high response speed; the hour-level strategy focuses on time-of-use pricing response and load shifting, and the control targets include medium-speed adjustable loads such as air conditioners, electric boilers, and electric vehicle charging, to achieve energy economy optimization; the daily-level strategy is oriented towards the arrangement of electricity substitution paths and the achievement of carbon emission targets, and achieves long-term adjustment of the park's energy structure through the optimized configuration of alternative equipment and operating modes.

[0030] Specifically, the optimized scheduling model and solution algorithm include:

[0031] 4.1) Definition of multi-objective functions;

[0032] 4.2) Set of constraints;

[0033] 4.3) Solution algorithm.

[0034] Furthermore, considering the economic efficiency, low carbon emissions, and responsiveness of the comprehensive energy system operation, the aforementioned multi-objective function definition constructs the following multi-objective scheduling objective function:

[0035] minΙ=ω1·Cost total +ω2·Carbon total +ω3·ResponseScore

[0036] Among them: Cost total Total operating cost; Carbon total ω1 represents the total carbon emissions; ResponseScore represents the system's response efficiency score to the control signal; ω1, ω2, and ω3 are the target weight coefficients set by the user side based on the actual application scenario.

[0037] Furthermore, the aforementioned set of constraints includes at least energy balance constraints, substitution activation state and switching logic constraints, nested response strategy constraints, user-side operational restrictions, and carbon emission constraints. Specifically, the energy balance constraint ensures that the system achieves energy supply and demand balance for cooling, heating, and electrical loads within any scheduling time t; the substitution activation state and switching logic constraints control whether equipment with electricity substitution capabilities operates on electricity; the nested response strategy constraints ensure that scheduling behavior does not exceed the instruction boundaries of the upper-level strategy; user-side operational restrictions include comfort temperature control and industrial load continuity; comfort temperature control ensures that user experience is not affected under response control; industrial load continuity limits the physical protection constraints on industrial loads regarding start-stop frequency and minimum operating time; the overall objective of the carbon emission quota constraint is to control the total emissions within the scheduling plan to not exceed environmental or market quota boundaries. These constraints collectively define the solution space boundary in the model, ensuring that the scheduling optimization results are economically reasonable, carbon emissions are controllable, operation is safe, and engineering is feasible.

[0038] Furthermore, a mixed-integer linear programming algorithm is adopted as the core solution algorithm for the optimized scheduling model.

[0039] Compared with the prior art, the advantages of the present invention are:

[0040] 1. The core of the technical solution of this invention lies in integrating the electricity substitution path with a multi-timescale demand response mechanism to achieve structural optimization and dynamic adjustment of the park's integrated energy system. It models electricity substitution as an adjustable resource, identifies end loads with electrification potential within the park (such as gas boilers, diesel equipment, etc.), and evaluates the economics and feasibility of converting them to electric drive at different times. By incorporating these "substitutable loads" into the system scheduling model, and coordinating them with flexible resources such as photovoltaics, wind power, energy storage, and electric vehicles, a structurally reconfigurable and responsively flexible energy system is formed.

[0041] 2. The technical solution of this invention constructs a multi-timescale response strategy at the minute, hour, and day levels, realizing unified optimization of rapid frequency response, time-of-use electricity price arbitrage, and long-term low-carbon operation path; it uses intelligent algorithms to solve the problem with operating cost, carbon emissions, and user satisfaction as comprehensive objectives; it realizes real-time execution of the response strategy and grid linkage, significantly improving the greenness, flexibility, and intelligence level of the park's energy system;

[0042] 3. The technical solution of this invention embodies a complete process reconstruction from the "substitutability" of the energy structure source to the "responsiveness" of the control mechanism, promoting the upgrading of the park's energy system towards "electrification, flexibility, and intelligence". Attached Figure Description

[0043] Figure 1This is a schematic diagram of the integrated park's energy system architecture;

[0044] Figure 2 This is a schematic diagram of the hierarchical response and collaborative control architecture of the integrated energy system for the park in this invention to external signals;

[0045] Figure 3 This is a schematic diagram of the overall architecture of the demand response method for the integrated energy system of the park for electricity substitution according to the present invention. Detailed Implementation

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] This invention proposes a method for regulating a comprehensive energy system in a park that integrates electricity substitution pathways with a multi-level flexible response mechanism. This method can effectively improve the greenness of the energy structure and enhance the system's adaptability to changes in external signals (such as electricity prices, load, and carbon emissions), thereby achieving the "low-carbon, flexible, and intelligent" transformation of the park's energy system.

[0048] 1. Identification of Electricity Substitution Potential and Load Modeling:

[0049] The goal of this step is to identify loads in the park that originally used traditional energy sources (such as natural gas, coal, diesel, etc.) and assess their feasibility and response potential for switching to electricity, thereby forming a structurally adjustable resource pool.

[0050] Figure 1 The diagram above illustrates the architecture of a comprehensive park energy system. The technical solution of this invention is based on... Figure 1 The integrated park energy system architecture shown is used for implementation and operation.

[0051] 1.1 Data Acquisition and Raw Load Modeling:

[0052] This step is the foundation of the entire method. It aims to establish a high-fidelity load evolution model by comprehensively collecting operational data from energy-using equipment within the park, providing reliable raw input for subsequent electricity substitution assessment, multi-energy system modeling, and response optimization.

[0053] Multi-dimensional data at both the device and system levels can be obtained through IoT sensors, energy metering devices, BAS system interfaces, historical data databases, and other means.

[0054]

[0055] The data acquisition phase comprehensively acquires and processes key data such as power curves, energy consumption types, energy prices, operating hours, and environmental parameters (e.g., temperature, humidity, and sunlight) of various energy-consuming equipment within the park. This provides a fundamental support for the system to construct accurate electricity, heat, and cooling load models. This data is not only used to identify high-carbon loads with potential for electricity substitution but also to quantify the response capabilities and flexibility levels of various loads, further supporting the classification, identification, and modeling of flexible resources. Simultaneously, indicators such as energy prices and operating hours provide a basis for the economic analysis of electricity substitution and the calculation of annual operating costs. Environmental and meteorological data are used to assist in constructing dynamic models of cooling and heating loads in response to climate change.

[0056] Based on the nature and control characteristics of the load, it can be divided into the following categories. Each category of load has a different modeling method, and the modeling is based on the actual function and operating characteristics of the user equipment:

[0057] 1. Combined heating and power system (CHP):

[0058] The gas turbine and waste heat boiler constitute a combined heat and power system, and its mathematical model is shown below:

[0059]

[0060] Among them, P GT The electrical power output of a combined heat and power system; H WHm For medium-grade thermal power output from a combined heat and power system; H WHl Low-grade heat power output from a combined heat and power system; V GT For the natural gas consumption of the combined heat and power system; η GT η is the power generation efficiency of the gas turbine. WHm η WHl These represent the medium-grade and low-grade thermal efficiencies of the waste heat boiler, respectively; L gas This refers to the calorific value of natural gas.

[0061] 2: Gas boiler (GB):

[0062] A gas-fired boiler heats water by burning natural gas to produce high-temperature steam. Its mathematical model is shown below:

[0063] H GB T = η GB L gas V GB (1-2)

[0064] Among them, H GB V represents the output thermal power of the gas-fired boiler. GB η represents the natural gas consumption of the gas-fired boiler.GT This refers to the heat production efficiency of a gas-fired boiler.

[0065] 3: Air conditioner (AC):

[0066] Its mathematical model is shown below:

[0067] Q EC =COP EC P EC (1-3)

[0068] Among them, Q EC P represents the cooling capacity of the electric chiller. EC The power consumption of the electric chiller; COP EC The energy efficiency ratio of an electric chiller;

[0069] 5: Steam-driven equipment (SD):

[0070] The steam-driven equipment is powered by medium-grade hot steam and recovers low-grade hot steam. Its mathematical model is shown below:

[0071] H SDl =η SD H SDm (1-4)

[0072] Among them, H SDl Low-grade heat power recovered for steam-driven equipment; H SDm For steam load; η SD The heat recovery efficiency of steam-driven equipment.

[0073] 6: Absorption refrigeration (AR) chiller:

[0074] Its mathematical model is shown below:

[0075] Q AC =COP AC P AC (1-5)

[0076] Among them, Q AC P represents the cooling capacity of the absorption chiller. AC The heat output of the absorption chiller; COP AC The energy efficiency ratio of an absorption chiller;

[0077] 7: AC-DC converter (AD):

[0078] Its mathematical model is shown below:

[0079]

[0080] In the formula: P ADa and P ADd These represent the AC input power and DC output power of the AC-DC converter, respectively; η AD and η AD These represent the conversion efficiencies from AC to DC and from DC to AC, respectively.

[0081] 8. Electricity storage (ES):

[0082] Its mathematical model is shown below:

[0083]

[0084] In the formula: S BA,t P represents the battery's charge at the end of time period t. BAc,t and P BAd,t λ represents the charging power and discharging power of the battery during time period t, respectively; BA η is the self-loss coefficient of the battery. BAc and η BAd These represent the battery's charging efficiency and discharging efficiency, respectively.

[0085] 9: Ice storage (IS) unit:

[0086] Its mathematical model is shown below:

[0087]

[0088] In the formula: Q IS Q ref and Q ice These represent the cooling capacity of the ice storage device, the chiller, and the ice storage tank, respectively; P IS P ref and P ice These are the power consumptions of the ice storage unit, the chiller, and the ice storage tank, respectively; COP ref S is the energy efficiency ratio of the refrigeration unit. ice,t λ represents the amount of ice stored in the ice storage tank at the end of time period t; ice COP is the self-loss coefficient of the ice storage tank. ice and η ice These are the ice-making energy efficiency ratio and refrigeration efficiency of the ice storage tank, respectively.

[0089] To further support the design of flexible identification and alternative strategies, the following metrics need to be extracted from the modeling data:

[0090] Average load level:

[0091]

[0092] Volatility indicator (standard deviation):

[0093]

[0094] Daily load curve shape factor (peak factor):

[0095]

[0096] These data provide quantitative support for subsequent scoring of electricity substitution potential and assessment of response speed.

[0097] 1.2 Substitution Cost Comparison Model:

[0098] The purpose of this model is to determine whether a load has the economic feasibility of electricity substitution by comparing the operating costs of the original equipment operating with conventional energy sources with the operating costs of replacing it with electrical equipment. In short, if "electricity operating costs < current operating costs," then the equipment is considered to have the economic basis for electricity substitution and can be included in the dispatch system as a potential structural adjustment resource.

[0099] The model uses the following comparison function to calculate the difference in operating costs after unit power substitution:

[0100]

[0101] in:

[0102] ΔC i : The difference in operating cost per unit time for device i (unit: yuan / h).

[0103] Original unit energy price.

[0104] P i Rated power consumption of the original equipment (kW).

[0105] Price per unit of electricity (yuan / kWh).

[0106] Input electrical power (kW) of the electric substitution equipment.

[0107] η i Equivalent thermal efficiency or electro-thermal conversion efficiency (dimensionless, typically ranging from 0.3 to 1.0).

[0108] 1.3 Comprehensive Evaluation Model for Alternative Feasibility:

[0109] To further improve the practical feasibility of the electricity substitution targets in this invention and ensure that the loads selected in the response model have high adaptability in terms of technology, economy and control, a comprehensive evaluation model for substitution feasibility is proposed. This model is used to score various load resources with electricity substitution potential in the park using multiple indicators, thereby screening out truly flexible and adjustable loads that can be used for regulation and optimization.

[0110] The model uses a score S i The mathematical expression representing the feasibility of replacing device i is as follows:

[0111] S i =α·T i +β·E i +γ·F i (1-14)

[0112] in:

[0113] S i A comprehensive alternative feasibility score is given for device i.

[0114] T i ∈[0,1] represents the technological maturity of the energy substitution scheme corresponding to the device.

[0115] E i ∈[0,1] represents the operational economic indicators of equipment substitution, based on the substitution cost difference ΔC. i The value is normalized to the maximum saving value in the candidate equipment set, and the specific calculation method is as follows:

[0116]

[0117] Negative savings results (i.e., substitutions that are not economical) will be reset to zero and will not be included in the score increase.

[0118] F i ∈[0,1] represents the flexible response capability score of the equipment after load replacement. The score is based on the equipment's response speed, adjustment accuracy, and response duration, and is normalized using a weighted average method.

[0119] α, β, γ∈[0,1] are weighting coefficients that satisfy the condition α+β+γ=1 and can be adjusted according to the actual operation goals or priority strategies of the park's power system.

[0120] This invention sets a feasibility scoring threshold θ∈[0.5,0.8], if and only if S i When the value is greater than θ, device i is considered to have comprehensive feasibility for electricity substitution, and it is incorporated into the subsequent flexible resource regulation model to participate in load response control and optimal scheduling.

[0121] 1.4 Alternative Load Modeling:

[0122] Based on the completion of the economic analysis and comprehensive feasibility assessment of the alternative, for load objects that meet the alternative conditions (i.e., their score S) i The technical solution of the present invention further constructs a dynamic modeling structure for its alternative loads, so as to uniformly incorporate them into the multi-energy system scheduling model for response control and optimization decision-making.

[0123] The core idea of ​​the alternative load modeling is to describe the difference in operating behavior and switching logic of the same load under the "traditional energy supply state" and "electricity alternative state" in mathematical form, and to introduce state control variables so that the system can dynamically decide whether to switch to the electricity supply path.

[0124] For each alternative load i, define the following variables:

[0125] Power demand (kW) of the load using the original energy source;

[0126] Power demand (kW) under electricity substitution methods;

[0127] η i Electrical substitution efficiency is defined as the ratio of energy required to perform the same function (usually η). i <1);

[0128] δ i (t) = {0, 1}: A binary control variable indicating whether the equipment is in an energy substitution state.

[0129] δ i (t) = 1: This indicates that device i uses electrical energy as a substitute at time t;

[0130] δ i (t) = 0: This indicates that device i maintains its original energy supply mode.

[0131] Based on the above variables, the state transition model is introduced as follows:

[0132]

[0133] in:

[0134] P i (t): Represents the total power demand of device i, which is used as input in the scheduling optimization model;

[0135] δ i When (t) = 0, it indicates that the equipment operates in the traditional way, consuming non-electric energy;

[0136] δ iWhen (t) = 1, it means that the equipment is powered by electrical energy, and the power value is adjusted according to energy efficiency.

[0137] This model can be directly embedded into the energy balance equation of a multi-energy system to determine the load configuration of each energy branch in the system.

[0138] When using electricity substitution, the original energy load needs to be further mapped to an equivalent electrical load:

[0139]

[0140] in:

[0141] Q i (t): The original equipment's thermal / cooling power demand (kW) at any given time;

[0142] η i Thermal efficiency, or coefficient of performance (COP), reflects the energy efficiency of equipment and is typically measured in the range of 0.95 to 1.0.

[0143] 2. Multi-functional system modeling and flexible resource identification:

[0144] This step constructs a coupled model of the park's integrated energy system, including the dynamic evolution of cooling, heating, and power loads and the modeling of adjustable resource characteristics.

[0145] 2.1 Energy system load modeling:

[0146] In order to accurately characterize the dynamic load demand characteristics of various energy forms in the integrated energy system of the park, this invention proposes a unified modeling method applicable to three types of loads: heat, electricity, and cooling, collectively referred to as "Energy Display System Load Modeling".

[0147] This model provides basic load curve input for the scheduling system and is a fundamental component of multi-energy system collaborative optimization.

[0148]

[0149]

[0150] 2.2 Distributed Resource Modeling:

[0151] 2.2.1 Photovoltaic Model:

[0152] P PV (t)=η PV ·A PV ·I(t) (2-1)

[0153] η PV Photovoltaic conversion efficiency;

[0154] A PV Installed area (m²)2 );

[0155] I(t): Solar radiation intensity (W / m²) 2 );

[0156] 2.2.2 Energy Storage System Modeling:

[0157]

[0158] SOC(t): State of charge of energy storage at time t (unit: kWh); Pe(t), P(t): Charging and discharging power (kW);

[0159] Restrictions:

[0160] Energy storage batteries have charge / discharge constraints and capacity constraints as follows:

[0161] SOC min ≤SOC t ≤SOC max (2-3)

[0162] P dis-max ≤P st (t)≤P ch-max (2-4)

[0163] P dis ·P ch =0 (2-5)

[0164] SOC in formulas (2-3) to (2-4) max and SOC min Upper and lower limits of energy storage batteries, P dis-max and P ch-max The power limit for charging and discharging a battery per unit time, P dis In the discharge state, P ch It is in charging state.

[0165] 2.2.3 Electric Vehicle Modeling:

[0166] Assuming the electric vehicle has V2G functionality, including three states: charging, discharging, and off-grid driving, its operating model considers power upper and lower limit constraints and state of charge constraints:

[0167]

[0168]

[0169] In the formula: and These are the state variables for energy storage charging and discharging; when A value of 1 indicates that the energy storage is in a charging state. A value of 1 indicates that the electric vehicle is in a discharging state; and This indicates the maximum charging and discharging power of an electric vehicle. The power of the electric vehicle during time period t is positive, indicating charging, and negative, indicating discharging. Let t represent the battery charge of the electric vehicle during time period t; The total capacity of the electric vehicle's battery; These are the upper and lower limits of the state of charge of an electric vehicle battery, respectively. The minimum required state of charge of the battery when an electric vehicle leaves a charging station: This represents the initial state of charge of the electric vehicle battery. The charging and discharging coefficient for electric vehicles; Let Δt be the power of the electric vehicle during operation, and Δt be the time interval.

[0170] 2.2.4 Thermal storage modeling:

[0171] Cold and heat storage devices achieve peak load shifting by pre-storing heat / cold energy, and have the ability to regulate in time.

[0172] State variables:

[0173] T storage (t): Temperature of the energy storage medium;

[0174] E thermal (t): Energy storage system heat / cold storage capacity (kWh heat / cold);

[0175] Energy balance model (simplified):

[0176]

[0177] η s Energy storage efficiency.

[0178] The same constraints apply, such as not being able to charge and discharge simultaneously, and the SOC range.

[0179] 2.2.5 Flexible Loads Modeling:

[0180] It includes air conditioners, electric boilers, and some industrial loads, and has a certain degree of regulation capability.

[0181] Modeling method:

[0182] Power adjustment range:

[0183]

[0184] Respond to boundary constraints (such as temperature, comfort, and equipment start / stop logic);

[0185] Auxiliary constraints such as response duration, maximum response power, and total energy consumption balance;

[0186] 2.3 Flexible Resource Identification:

[0187] In the integrated energy system for industrial parks proposed in this invention, to achieve efficient load response and multi-energy coordinated dispatch, it is necessary to analyze the flexibility characteristics of various energy-consuming devices within the system to identify a set of flexible resources with adjustment capabilities that can participate in demand response. To this end, this invention designs a flexible resource identification method based on multi-dimensional index quantification and response potential assessment.

[0188] 2.3.1 Definition of Flexible Resources:

[0189] Flexible resources refer to energy-consuming load units that possess power adjustment capabilities, time shifting capabilities, and rapid start-stop capabilities without affecting core production, safety, or user comfort. These mainly include, but are not limited to: interruptible loads, shiftable loads, fast-response loads, energy storage resources (such as batteries, cold and heat storage), and electric vehicle fleets (V1G).

[0190] 2.3.2 Identifying the target and output format:

[0191] The target of identification is the entire load set of the park. Select flexible load sets The following serves as the execution object for response strategies and optimized scheduling:

[0192] Define a load response potential index and a set of flexible resources:

[0193]

[0194] R i This indicates the degree of power fluctuation (response capability).

[0195] For response accessibility and sustainability scoring;

[0196] ∈: Adjustable critical value.

[0197] 2.3.3 Response potential index R:

[0198] Used to measure the degree of fluctuation in equipment power during the operating cycle, it is an important signal of load flexibility and is defined as the standard deviation of the power curve:

[0199]

[0200] in:

[0201] P i (t) represents the operating power of the device at time t;

[0202] The average power of the device during the cycle;

[0203] T: Total number of time steps in the scheduling cycle.

[0204] The greater the fluctuation, the more intermittent and customizable the equipment operation is, and the more likely it is to have responsiveness.

[0205] 2.3.4 Flexibility Assessment

[0206] The following three key dimensions are considered when assigning scores to generate a normalized score.

[0207]

[0208] The final score can be constructed using a weighted average:

[0209]

[0210] 2.3.5 Output of Flexible Resource Classification Results:

[0211] The identified flexible load resources will be categorized according to their response capabilities for use in subsequent strategy configuration:

[0212]

[0213] The classification results will be stored in the response resource database and matched with the time-dimensional control strategy for subsequent multi-time-scale response controller calls.

[0214] 3. Multi-timescale response strategy design:

[0215] To improve the adaptability of the park's integrated energy system to various external disturbances such as the electricity market, grid dispatch signals, and carbon emission constraints, this invention constructs a multi-timescale response strategy system.

[0216] Figure 2 A schematic diagram of the hierarchical response and collaborative control architecture of the integrated energy system for the park in this invention to external signals is provided.

[0217] As shown in the figure, the system divides the response behavior into three control levels: minute, hour, and day. It defines response objectives, control mechanisms, and implementation constraints for different levels, thereby achieving full-process, multi-level response coordination from rapid dynamic response to medium- and long-term structural optimization.

[0218] 3.1 Layered Strategy Architecture:

[0219] This invention constructs a multi-timescale response strategy architecture, dividing the control strategies into three levels: minute-level, hour-level, and day-level, to achieve hierarchical response and coordinated control of the park's integrated energy system to external signals. The minute-level strategy focuses on rapid response to grid frequency fluctuations and new energy disturbances, primarily relying on resources such as energy storage and fast-start / stop loads, exhibiting high response speed. The hour-level strategy focuses on time-of-use pricing response and load shifting, controlling medium-speed adjustable loads such as air conditioners, electric boilers, and electric vehicle charging, achieving energy economy optimization. The day-level strategy addresses the arrangement of electricity substitution pathways and the achievement of carbon emission targets, optimizing the configuration of alternative equipment and operating modes to achieve long-term adjustments to the park's energy consumption structure. The three levels of strategies operate collaboratively through upper-lower level constraint transmission and rolling correction mechanisms, forming a rapid, precise, economical, efficient, and long-term low-carbon multi-level response system.

[0220] 3.2 Strategy Expression:

[0221] To effectively transform multi-timescale control strategies into optimized scheduling models, this invention constructs corresponding objective functions and strategy expressions for three response levels: minute-level, hour-level, and day-level, incorporating the adjustment behaviors of various resources into a unified and solvable strategy model framework. The strategy expression for each time level includes three parts: objective function construction, control variable definition, and constraint setting, ensuring that the strategy is executable, computable, and controllable.

[0222] Minute-level response strategy expression:

[0223] Applicable Scenarios: Addressing dynamic events such as real-time power balance demands of the power grid, power disturbances from renewable energy sources, and system frequency shifts. Response Objective Function: A tracking-type residual function is constructed to minimize the difference between the expected and actual response of the power grid.

[0224]

[0225] in:

[0226] ΔP grid (t): The regulation power (such as frequency modulation command) required by the system at time t;

[0227] ΔP i (t): The actual response power of the i-th adjustable load at time t;

[0228] κ: A collection of rapidly responding resources that can participate in minute-level control, such as battery storage and electric boilers.

[0229] Constraints:

[0230] Response delay ≤ instruction cycle (e.g., 1 minute); single adjustment range is limited by the physical characteristics of the equipment; number of start-stop cycles is constrained by the maximum daily response frequency.

[0231] Hourly response strategy expression:

[0232] Applicable scenarios: Responding to time-of-use pricing, electric vehicle group control and regulation, peak shaving for cold loads, and energy storage "valley charging and peak releasing" scenarios. Objective function: Establishing a price-driven economic optimization function with the goal of minimizing electricity purchase costs.

[0233]

[0234] in:

[0235] C(t): Electricity price at time t (yuan / kWh), which can be the real-time electricity price or the day-ahead price;

[0236] L elec (t): System electrical load demand;

[0237] P PV (t): Photovoltaic output

[0238] P C (t), P d (t): Charging and discharging power of the energy storage system;

[0239] Auxiliary constraints:

[0240] Energy storage charge and discharge constraints: SOC min ≤SOC(t)≤SOC max ;

[0241] Energy required for electric vehicle to complete charging: ∑ t P EV (t)·Δt≥E req ;

[0242] User comfort constraints (e.g., room temperature controlled at 24±2℃);

[0243] Load start-up and shutdown delay, minimum continuous operating time.

[0244] Daily-level response strategy expression:

[0245] Applicable Scenarios: Used for structural problems such as electricity substitution path scheduling, carbon emission control, and selection of power-heat operation modes. Joint Objective Function: Constructs a multi-objective scheduling model with the combined objectives of minimizing carbon emissions and maximizing electricity substitution.

[0246] min(ω1·Carbon total -ω2ReplaceEnergy elec (3-3)

[0247] in:

[0248] Total carbon emissions, where λi is the carbon factor per unit power;

[0249] Electrical energy replaces energy;

[0250] ω1, ω2: Weighting coefficients of the objective function, which can be flexibly adjusted according to the degree of carbon constraint.

[0251] 4. Optimize the scheduling model and solution algorithm:

[0252] To achieve unified control and calculation of multi-timescale response strategies, flexible resource characteristics, and multi-energy system coupling models, this invention constructs a multi-objective, multi-constraint optimization scheduling model. This model can generate the optimal operating strategy for the entire system, taking into account operational economy, carbon emission levels, and response capabilities. The model invokes minute-level, hourly-level, and daily-level strategy inputs within different scheduling cycles, adapting to various resource models, equipment boundaries, load response conditions, and operational objectives.

[0253] 4.1 Definition of a multi-objective function:

[0254] This invention considers three key objectives for the operation of integrated energy systems: economy, low carbon emissions, and responsiveness, and constructs the following multi-objective scheduling objective function:

[0255] minΙ=ω1·Cost total +ω2·Carbon total +ω3·ResponseScore (4-1)

[0256] in:

[0257] Cost total Total operating costs include electricity purchase costs, energy storage degradation costs, and electricity price arbitrage gains and losses.

[0258] λi represents the total carbon emissions, and λi represents the carbon factor per unit power.

[0259] ResponseScore: The system's response efficiency score to the control signal;

[0260] ω1, ω2, ω3: These are target weight coefficients set by the user side based on the actual application scenario.

[0261] 4.2 Constraint Set:

[0262] To ensure the feasibility and stability of the optimized scheduling model in practical engineering, this invention designs a systematic set of constraints, covering multiple aspects such as energy balance, equipment boundaries, response strategy interfaces, user-side limitations, and carbon emission constraints. These constraints collectively define the solution space boundary in the model, ensuring that the optimized scheduling results are not only economically reasonable and have controllable carbon emissions, but also operationally safe and engineering-feasible.

[0263] Energy balance constraints:

[0264] These constraints are used to ensure that the system achieves energy supply and demand balance of cooling, heating and electricity loads within any scheduling time t, and are the basic constraints of the optimization model.

[0265] (1) Electrical energy balance:

[0266]

[0267] in:

[0268] L elec (t): Total electrical load demand of the park at time t, in kW;

[0269] P PV (t): The power generation of the photovoltaic system at time t, in kW;

[0270] P d (t): Discharge power (kW) of the energy storage system at time t;

[0271] P grid (t): Power supplied by the power grid (purchased electricity);

[0272] P c (t) The charging power of the energy storage system at time t;

[0273] ΔP i (t): The adjustable power of the i-th flexible load at time t;

[0274] A flexible response resource set.

[0275] Resource capabilities and state constraints:

[0276] (1) Energy storage status update and capacity boundary:

[0277]

[0278] SOC min ≤SOC(t)≤SOC max (4-4)

[0279] P c (t)·P d (t)=0 (4-5)

[0280] in:

[0281] SOC(t) represents the state of charge of the energy storage system at time t, and its unit is kWh.

[0282] η c η d These are charging efficiency and discharging efficiency, respectively.

[0283] Δt is the length of the scheduling cycle;

[0284] P c (t), P d (t) represents the charging and discharging power, respectively, in kW;

[0285] SOC min SOC max This represents the boundary of the charged state, with units of kWh.

[0286] This constraint describes the dynamic behavior of energy storage charging and discharging and the safe operating boundaries.

[0287] (2) Electric vehicle charging restrictions:

[0288]

[0289] P EV (t) represents the electric vehicle charging power (kW) at time t;

[0290] t in t out These are the vehicle's connection time and departure time, respectively.

[0291] EV represents the user's expected total charging energy, measured in kWh.

[0292] Ensure that users' charging needs are met during EV connectivity, while balancing adjustability and service guarantee.

[0293] Alternate enable status and switching logic constraints:

[0294] Used to control whether equipment with electricity substitution capabilities operates using electricity:

[0295]

[0296] δ i(t)∈{0,1}(4-8)

[0297] in:

[0298] P i (t) represents the total power of device i at time t;

[0299] δ i (t) indicates whether device i has enabled electricity substitution (1 for enabled, 0 for original energy);

[0300] Power generated using traditional energy sources;

[0301] Electricity replaces power.

[0302] This logic enables dynamic decision modeling for electrification paths and is the core of the control variables expressed in the daily strategy. Response strategy nesting constraints:

[0303] To ensure that scheduling behavior does not exceed the instruction boundaries of the upper-level policy, policy boundary constraints are set:

[0304]

[0305]

[0306] illustrate:

[0307] The device is restricted to responding only within a specified time window;

[0308] The control response intensity must not exceed the set boundary;

[0309] Guarantee the minimum continuous response time (avoid "jitter");

[0310] User-side operational limitations:

[0311] (1) Comfort temperature control (e.g., air conditioning):

[0312] T indoor (t)∈[T target -ΔT,T target +ΔT] (4-12)

[0313] illustrate:

[0314] The control and regulation must not cause the room temperature to exceed the user's acceptable range;

[0315] Ensure that the user experience is not affected under response control.

[0316] (2) Industrial load continuity:

[0317]

[0318] Physical protection constraints used to limit the start-stop frequency and minimum running time of industrial loads.

[0319] Carbon emission quota constraints:

[0320]

[0321] in:

[0322] λ i Carbon emission factor per unit power, expressed in kgCO. / kWh;

[0323] C budget The maximum allowable carbon emissions, expressed in kgCO.

[0324] The overall goal is to control the total emissions within the scheduling plan to not exceed the environmental or market quota boundaries.

[0325] 4.3 Solution Algorithm:

[0326] This invention selects Mixed-Integer Linear Programming (MILP) as the core solution algorithm for the optimized scheduling model. The reason for this is that the scheduling model constructed in this invention has a clearly defined linear objective function and linear constraints, while also containing a large number of 0-1 binary variables (such as energy substitution states, start-up and shutdown states, etc.) and continuous variables (such as power, energy storage states, etc.), fully conforming to the modeling characteristics of MILP. Compared with heuristic algorithms, MILP guarantees global optimality, has high solution accuracy, clear modeling logic, and controllable computational efficiency, making it particularly suitable for structurally complex but linearizable problems. This algorithm has significant advantages in engineering implementation, solution stability, and result interpretability, ensuring that the scheduling system of this invention has high reliability and high practicality in actual operation.

[0327] In summary, the core of this invention lies in integrating electricity substitution pathways with multi-timescale demand response mechanisms to achieve structural optimization and dynamic adjustment of the park's integrated energy system. Unlike traditional response methods that only address peak shaving and valley filling on the load side, this invention, for the first time, models electricity substitution as an adjustable resource, identifying end-loads within the park with electrification potential (such as gas boilers and diesel equipment) and assessing their economic viability and feasibility for conversion to electricity-driven operation at different times. By incorporating these "substitutable loads" into the system scheduling model and coordinating them with flexible resources such as photovoltaics, wind power, energy storage, and electric vehicles, a structurally reconfigurable and responsive energy system is formed.

[0328] Figure 3 A schematic diagram of the overall architecture of the demand response method for integrated energy systems in industrial parks based on electricity substitution, as presented in this invention, is given.

[0329] Depend on Figure 3 It is understood that the present invention provides a demand response method for a comprehensive energy system in a park based on electricity substitution, which includes:

[0330] 1) Identification of electricity substitution potential and multi-energy system modeling:

[0331] Identify load objects in the park that originally used traditional energy and assess their feasibility and response potential for switching to electricity to form a structurally adjustable resource pool;

[0332] 2) Multi-energy system modeling and flexible resource identification: Construct a coupled model of the park's integrated energy system, including the dynamic evolution of cooling, heating and power loads and the modeling of adjustable resource characteristics;

[0333] 3) Multi-timescale response strategy design:

[0334] A multi-timescale response strategy system is constructed, dividing response behavior into three control levels: minute, hour, and day. Response objectives, control mechanisms, and implementation constraints are defined for different levels, thereby achieving full-process, multi-level response coordination from rapid dynamic response to medium- and long-term structural optimization.

[0335] 4) Optimize the scheduling model and solution algorithm:

[0336] Taking into account operational economy, carbon emission levels, and response capabilities, the optimal operating strategy for the entire system is generated; minute-level, hour-level, and day-level strategy inputs are invoked within different scheduling cycles to adapt to various resource models, equipment boundaries, load response conditions, and operational objectives.

[0337] To accurately respond to external power grid price signals, load regulation demands, and carbon emission assessments, this invention constructs a multi-timescale response strategy at the minute, hour, and day levels, achieving unified optimization of rapid frequency response, time-of-use pricing arbitrage, and long-term low-carbon operation paths. Simultaneously, a multi-objective optimization scheduling model is designed, using operating costs, carbon emissions, and user satisfaction as comprehensive objectives, and solved using intelligent algorithms. This enables real-time execution of the response strategy and grid linkage, significantly improving the greenness, flexibility, and intelligence of the park's energy system. The overall approach embodies a complete restructuring from the "substitutability" of the energy structure at its source to the "responsiveness" of the control mechanism, driving the park's energy system towards "electrification, flexibility, and intelligence."

[0338] This invention can be widely used in the field of power operation and dispatch management of park power grids.

Claims

1. A demand response method for a comprehensive energy system in a park based on electricity substitution, characterized by: include: 1) Identification of potential for electricity substitution and multi-energy system modeling: Identify load objects in the park that originally used traditional energy and assess their feasibility and response potential for electricity substitution, forming a structurally adjustable resource pool; 2) Multi-energy system modeling and flexible resource identification: Construct a coupled model of the park's integrated energy system, including the dynamic evolution of cooling, heating and power loads and the modeling of adjustable resource characteristics; 3) Multi-timescale response strategy design: Construct a multi-timescale response strategy system, divide the response behavior into three control levels: minute, hour and day. Define response objectives, control mechanisms and implementation constraints for different levels to achieve full-process, multi-level response coordination from rapid dynamic response to medium- and long-term structural optimization. 4) Optimize scheduling model and solution algorithm: Under the premise of considering operating economy, carbon emission level and response capability, generate the optimal operating strategy for the whole system; call minute-level, hour-level and daily-level strategy inputs in different scheduling cycles to adapt to various resource models, equipment boundaries, load response conditions and operating objectives.

2. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 1, characterized in that: The proposed demand response method for integrated energy systems in industrial parks based on electricity substitution achieves structural optimization and dynamic adjustment of the integrated energy system by integrating electricity substitution paths with multi-timescale demand response mechanisms. It models electricity substitution as an adjustable resource, identifies end-loads within the park with electrification potential, and assesses the economic viability and feasibility of converting them to electricity-driven operation at different times. By incorporating these "substitutable loads" into the system scheduling model and coordinating them with flexible resources including photovoltaics, wind power, energy storage, and electric vehicles, a reconfigurable and responsive energy system is formed.

3. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 1, characterized in that: The aforementioned identification of electricity substitution potential and multi-energy system modeling includes: 1.1) Data acquisition and raw load modeling; 1.2) Substitution cost comparison model; 1.3) Comprehensive evaluation model for alternative feasibility; 1.4) Alternative load modeling.

4. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 1, characterized in that: The aforementioned multi-energy system modeling and flexible resource identification include: 2.1) Energy system load modeling; 2.2) Distributed resource modeling; 2.3) Flexible resource identification.

5. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 1, characterized in that: The multi-timescale response strategy design includes a hierarchical strategy architecture and strategy expression; The hierarchical strategy architecture includes constructing a multi-timescale response strategy architecture, dividing the control strategies into three levels: minute, hour, and day, to realize the hierarchical response and coordinated control of the park's integrated energy system to external signals; the three-level strategies operate in coordination through upper and lower level constraint transmission and rolling correction mechanisms, forming a fast, precise, economical, efficient, and long-term low-carbon multi-level response system. The strategy expression includes constructing corresponding objective functions and strategy expression forms for three response levels: minute, hour, and day. This incorporates the adjustment behavior of various resources into a unified and solvable strategy model framework. The strategy expression for each time level includes three parts: objective function construction, control variable definition, and constraint setting, to ensure that the strategy is executable, computable, and controllable.

6. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 5, characterized in that: The minute-level strategy focuses on rapid response to grid frequency fluctuations and new energy disturbances, relying on resources including energy storage and fast start-stop loads, and has a high response speed; the hour-level strategy focuses on time-of-use pricing response and load shifting, and the control targets include medium-speed adjustable loads such as air conditioners, electric boilers, and electric vehicle charging, to achieve energy economy optimization. The daily-level strategy focuses on the arrangement of electricity substitution pathways and the achievement of carbon emission targets. It aims to achieve long-term adjustments to the energy consumption structure of the park through the optimized configuration of alternative equipment and operating modes.

7. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 1, characterized in that: The optimized scheduling model and solution algorithm include: 4.1) Definition of multi-objective functions; 4.2) Set of constraints; 4.3) Solution algorithm.

8. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 7, characterized in that: The aforementioned multi-objective function definition, considering the comprehensive energy system operation's economy, low carbon emissions, and response efficiency, constructs the following multi-objective scheduling objective function: minΙ=ω1·Cost total +ω2·Carbon total +ω3·ResponseScore Among them: Cost total Total operating cost; Carbon total ω1 represents the total carbon emissions; ResponseScore represents the system's response efficiency score to the control signal; ω1, ω2, and ω3 are the target weight coefficients set by the user side based on the actual application scenario.

9. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 7, characterized in that: The set of constraints includes at least energy balance constraints, alternative activation status and switching logic constraints, nested response strategy constraints, user-side operation restrictions, and carbon emission constraints. Among them, the energy balance constraint is used to ensure that the system achieves energy supply and demand balance of cooling, heating and electrical loads within any scheduling time t; Alternative activation status and switching logic constraints are used to control whether devices with power substitution capabilities operate using electricity. Nested response policy constraints are used to ensure that scheduling behavior does not exceed the instruction boundaries of the upper-level policy; User-side operational constraints include comfort temperature control and industrial load continuity; comfort temperature control is used to ensure that the user experience is not affected under response control; industrial load continuity is used to limit the physical protection constraints on the start-stop frequency and minimum running time of industrial loads. The overall objective of carbon emission quota constraints is to control the total emissions within the scheduling plan to not exceed the environmental or market quota boundaries. The above constraints collectively define the spatial boundary in the model, ensuring that the scheduling optimization results are economically reasonable, have controllable carbon emissions, are safe to operate, and are feasible for engineering.

10. The demand response method for a comprehensive energy system in a park based on electricity substitution as described in claim 7, characterized in that: A mixed-integer linear programming algorithm is adopted as the core solution algorithm for the optimization scheduling model.

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