Comprehensive dispatch optimization method based on coal-fired power, electric-to-gas, heat and power, and demand response

Abstract

This application discloses a comprehensive scheduling optimization method based on coal-fired power, electricity-to-gas (EPG), combined heat and power (CHP), and demand response. It constructs and initializes an energy scheduling system architecture model; establishes a comprehensive objective function with the goal of minimizing the total operating cost of the energy scheduling system; and sequentially constructs dynamic models for coal-fired power units and carbon capture, EPG and carbon cycle, CHP, and energy storage systems to form thermal balance constraints. Based on the dynamic models of the CHP and energy storage systems, a two-stage optimization scheduling framework demand response model is constructed and optimized using thermal balance constraints, thereby achieving dynamic modeling and closed-loop adaptive scheduling of demand response. This application realizes the synergistic optimization of different energy systems to minimize the total system operating cost, reduce carbon emissions, and improve the flexibility and reliability of the energy system, thus supporting a more efficient and greener energy transition process.

Description

Technical Field

[0001] This application relates to the field of integrated energy management technology, specifically to an integrated scheduling optimization method based on coal, electricity-to-gas, cogeneration, and demand response. Background Technology

[0002] With the ongoing global energy transition, improving energy system flexibility, promoting renewable energy consumption, and reducing carbon emissions have become research hotspots. Traditional coal-fired units are stable but have high carbon emissions, contradicting the "dual carbon" goal. P2G (electricity-to-gas) technology can convert surplus wind and solar power and captured CO2 into hydrogen or methane, enabling cross-seasonal storage and utilization of renewable energy. Combined heat and power (CHP) improves energy efficiency through waste heat recovery. Demand response (DR) flexibly adjusts load through price or incentive mechanisms, enhancing system regulation capabilities.

[0003] Current research indicates that combining CCS (carbon capture and storage) with P2G (penetrating-to-grid) technologies can achieve deep decarbonization of energy systems. However, most existing studies focus primarily on the coupling of individual devices and carbon trading mechanisms, lacking a systematic analysis of the coordinated scheduling of four key technologies: coal-fired power units, P2G equipment, CHP systems, and demand response. This results in high system operating costs, low efficiency, high carbon emissions, and poor flexibility and reliability. Summary of the Invention

[0004] Therefore, this application provides a comprehensive scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response, to solve the problems of high operating costs, low efficiency, high carbon emissions, and poor flexibility and reliability of existing comprehensive energy management methods.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A comprehensive scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response includes:

[0007] Step 1: Construct and initialize the energy dispatch system architecture model; the energy dispatch system architecture model includes coal-fired power units, carbon capture and storage system, power-to-gas conversion device, combined heat and power system, energy storage system and demand response, and establish a topology structure based on energy flow;

[0008] Step 2: With minimizing the total operating cost of the energy dispatch system as the optimization objective, construct a comprehensive objective function based on carbon emission constraints and the energy dispatch system architecture model;

[0009] Step 3: Construct the basic constraint and emission model of the coal-fired unit based on the comprehensive objective function, and construct the upper bound model of carbon capture and storage system and the regeneration heat demand model.

[0010] Step 4: Based on the carbon dioxide emissions of the coal-fired unit and the upper limit of the carbon capture and storage system, construct the carbon source supply constraint of the power-to-gas conversion device, and construct the electrical conversion constraint, stoichiometric constraint and thermal coupling constraint in combination with the regeneration heat demand. At the same time, construct a carbon cycle model to track the carbon flow of the system.

[0011] Step 5: Based on the electrical conversion constraints, stoichiometric constraints, and thermal coupling constraints of the electro-gas conversion device, as well as the carbon cycle model, construct dynamic models of the combined heat and power system and the energy storage system, and form thermal balance constraints; the thermal balance constraints are:

[0012]

[0013] in, Indicates the heating capacity of the combined heat and power (CHP) system; Indicates the amount of heat storage / charge; Indicates the amount of waste heat recovered by the unit; Indicates heat load; This indicates the regeneration heat requirement of the carbon capture and storage system; This indicates the heat demand of the electro-gas conversion device;

[0014] Step 6: Construct a demand response model based on the dynamic models of the cogeneration system and the energy storage system, and optimize it using the thermal balance constraints; the demand response model adopts a two-stage optimization scheduling framework to realize dynamic modeling and closed-loop adaptive scheduling of demand response; extract the spatiotemporal features of demand response resources based on graph attention network to realize dynamic modeling and closed-loop adaptive scheduling of demand response.

[0015] Preferably, in step 1, the initialization process inputs the capacity, efficiency, thermoelectric ratio, energy storage capacity and charge / discharge efficiency, demand response adjustable range, fuel price, carbon trading price, electricity / heat / gas load forecast, and renewable energy forecast for each device.

[0016] Preferably, in step 2, the comprehensive objective function is:

[0017]

[0018] Where min represents the minimum; J represents the total operating cost of the energy dispatch system; t represents the time index; T represents the dispatch period; C fuel (t) represents fuel cost; C buy (t) represents the net cost of interacting with the power grid; C op (t) represents the equipment operation and maintenance cost; Cco2(t) represents the carbon trading or carbon emission cost; C curt (t) represents the penalty cost for wind and solar power curtailment; C DR(t) represents the demand response compensation cost.

[0019] Preferably, in step 3, the basic constraints and emission model of the coal-fired unit includes: upper and lower limits of power generation output and online status, minimum online time, ramping constraints, and fuel consumption and CO2 emissions.

[0020] Preferably, in step 4, the carbon cycle model includes dynamic carbon inventory and system net emissions.

[0021] Preferably, in step 6, the two-stage optimization scheduling framework is: determining the commitment variables and hourly plan a day-ahead and adjusting the plan in real time based on rolling time domain.

[0022] Preferably, in step 6, the demand response model is constructed based on a graph attention network, where each demand response resource is treated as a node in the graph, the coupling relationship between resources is defined as an edge in the graph, and the features of neighboring nodes are aggregated through an attention mechanism to calculate the response quantity.

[0023] Compared with the prior art, this application has at least the following beneficial effects:

[0024] Based on further analysis and research of existing technical problems, this application provides a comprehensive scheduling optimization method based on coal-fired power generation, electricity-to-gas conversion, combined heat and power (CHP), and demand response. It constructs and initializes an energy scheduling system architecture model; with minimizing the total operating cost of the energy scheduling system as the optimization objective, it constructs a comprehensive objective function based on carbon emission constraints and the energy scheduling system architecture model, and sequentially constructs basic constraint and emission models for coal-fired units, upper bound models for carbon capture and storage systems and regenerative heat demand models, electrical conversion constraints, stoichiometric constraints and thermal coupling constraints for electricity-to-gas conversion devices, a carbon cycle model, and dynamic models for CHP and energy storage systems, forming a thermal balance constraint. Based on the dynamic models of the CHP and energy storage systems, a two-stage optimization scheduling framework demand response model is constructed and optimized using thermal balance constraints, thereby achieving dynamic modeling and closed-loop adaptive scheduling of demand response. This application achieves synergistic optimization of different energy systems to minimize total system operating costs, reduce carbon emissions, and improve the flexibility and reliability of energy systems, thus supporting a more efficient and greener energy transition process. Attached Figure Description

[0025] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0026] Figure 1 A flowchart illustrating a comprehensive scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response, provided in Embodiment 1 of this application;

[0027] Figure 2 This is a schematic diagram of the overall process provided in Embodiment 1 of this application;

[0028] Figure 3 This is a schematic diagram of energy flow provided for Embodiment 1 of this application. Detailed Implementation

[0029] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," "third," etc., in this application are intended to distinguish the objects referred to and do not have any special meaning in terms of technical connotation (e.g., they should not be construed as an emphasis on importance or order). Expressions such as "including," "comprising," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0031] The terms used in this application, such as "upper," "lower," "left," "right," and "middle," are generally used to indicate the general relative positional relationship for the purpose of intuitive understanding by referring to the accompanying drawings, and are not absolute limitations on the positional relationship in the actual product.

[0032] Example 1

[0033] Please see Figure 1 , Figure 2 and Figure 3 This embodiment provides a comprehensive scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response, including:

[0034] S1: Construct and initialize the energy dispatch system architecture model; the energy dispatch system architecture model includes coal-fired units, carbon capture and storage system, power-to-gas conversion unit, combined heat and power system, energy storage system and demand response, and establish a topology structure based on energy flow;

[0035] Specifically, this embodiment first requires constructing an energy dispatch system architecture model and performing architecture initialization. The energy dispatch system architecture model includes:

[0036] Coal-fired power units: use coal as fuel to provide electricity and some heat, while emitting carbon dioxide;

[0037] CCS (Carbon Capture and Storage) system: used to capture CO2 emitted by coal-fired power units for use by P2G units;

[0038] P2G (electricity-to-gas) device: uses electricity to perform electrolysis and methanation reactions, converting electrical energy and captured CO2 into hydrogen and methane, realizing gas energy storage across time scales;

[0039] CHP (Combined Heat and Power) system: Burns natural gas or methane to provide electricity and waste heat;

[0040] Energy storage systems include electrical energy storage, thermal energy storage, and gas energy storage, achieving energy balance across different time scales.

[0041] Demand response: Adjusting the load-side flexibility of users' electricity, heat, and gas through price incentives or subsidy mechanisms.

[0042] During architecture initialization, the following data must be entered: capacity, efficiency, heat-to-power ratio, energy storage capacity, charge-discharge efficiency, adjustable demand response range, fuel price, carbon trading price, electricity / heat / gas load forecast, and renewable energy forecast for each device.

[0043] This step aims to integrate the supply and demand relationships of electricity, heat, and hydrogen energy into a unified framework by establishing an energy dispatch system architecture model and clarifying its constituent equipment and coupling relationships, so as to optimize it in subsequent steps.

[0044] S2: With minimizing the total operating cost of the energy dispatch system as the optimization objective, a comprehensive objective function is constructed based on carbon emission constraints and the energy dispatch system architecture model;

[0045] Specifically, this step defines the optimization objective (primarily minimizing the total operating cost of the energy dispatch system) and structures the energy dispatch system boundary, equipment list, and prediction inputs into a parameter set. The output of this step, along with the prediction set P, provides a unified data interface (device parameters, predictions, initial states, and baselines, etc.) for subsequent steps, ensuring consistency and reproducibility. The output of this step directly determines the feasible region and objective expression of the optimization problem in step S3; its accuracy determines the feasibility and physical reliability of the scheduling results.

[0046] More specifically, to achieve both economic efficiency and low carbon emissions in the energy dispatch system, the following comprehensive objective function was designed. This comprehensive objective function includes minimizing operating costs and constraining carbon emissions; that is, the comprehensive objective function is:

[0047] (1)

[0048] In equation (1), min represents the minimum; J represents the total operating cost of the energy dispatch system, in yuan; t represents the time index (time slot), in hours; T represents the dispatch cycle; C fuel (t) represents fuel cost, in yuan; C buy(t) represents the net cost of interacting with the power grid, in yuan; C op (t) represents the equipment operation and maintenance cost, in yuan; Cco2(t) represents the carbon trading or carbon emission cost, in yuan, usually... ,in, The carbon emission intensity or carbon emission factor for time period t (usually in kg CO2 / kWh or t CO2 / MWh) represents the CO2 emissions per unit of net energy production or consumption, which may vary over time (e.g., depending on grid carbon intensity or energy source). This represents the net energy consumption, production, or injection during period t (usually in kWh or MWh). Net value may refer to the balance after deducting self-consumption from electricity purchased from the grid or generated, or the overall net energy flow of the system. This formula is used to calculate the carbon emission cost or total amount generated by energy use in period t and is commonly found in low-carbon optimal scheduling models; Ccurt(t) represents the cost of wind and solar curtailment penalties, in yuan; C DR (t) represents the demand response compensation cost, in yuan, typically... , represents the demand response compensation cost (usually in yuan) for time period t, which is the total amount of economic compensation provided to users or equipment participating in demand response. Here, j is the compensation unit price or incentive coefficient of the j-th demand response resource (user, load or equipment) (usually in yuan / kW or yuan / kWh), representing the compensation amount corresponding to a unit response quantity. This represents the response amount of the j-th demand response resource in time period t (the unit is usually kW or kWh), such as load shedding, shifting, or adjustment power.

[0049] This step allows for the quantitative definition of operating costs and carbon emissions, providing an evaluation standard for subsequent optimization.

[0050] S3: Construct the basic constraint and emission model of the coal-fired unit based on the comprehensive objective function, and construct the upper bound model of carbon capture and storage system and the regeneration heat demand model.

[0051] Specifically, this step establishes the start-up, shutdown, output, ramp-up, fuel consumption and emission models of coal-fired units (i.e., the basic constraints and emission models of coal-fired units), and establishes the upper limit of CCS capture and regeneration heat demand models as input constraints for P2G (step S4) and heating network (step S5).

[0052] S301: Constructing a basic constraint and emission model for coal-fired power units;

[0053] More specifically, the basic constraints and emission models for coal-fired power units include: upper and lower limits of power generation output and online status, minimum online time, ramp-up constraints (upper / lower limits), and fuel consumption and CO2 emissions, among which:

[0054] The upper and lower limits of power generation output and online status are as follows:

[0055] (2)

[0056] In equation (2), i represents the index of the coal-fired power unit; P i,t This represents the power generation capacity of coal-fired unit i at time t, in MW; Indicates the online status of the coal-fired unit (1 = online); These represent the minimum and maximum output of the coal-fired power unit, respectively, in MW.

[0057] Minimum online time is:

[0058] (3)

[0059] In equation (3), This indicates the minimum online time for a coal-fired power unit, expressed in hours (h). This represents the operating status of the i-th device during time period t (start / stop variable), and is an integer variable (binary variable) ranging from 0 to 1: e.g. This indicates that the device is in an operational (power-on, network-connected) state during time period t. This indicates that the equipment is in a stopped state during time period t; This represents an auxiliary time variable, used as the index of the intermediate time period for summation (which varies within the summation range). This indicates whether the device performed a "start-up" action during time period t: if the difference is 1, it indicates that the device was started.

[0060] The climbing constraints (upper / lower bounds) are:

[0061] (4)

[0062] In equation (4), These represent the upper and lower limits of the gradient, respectively, in MW / h.

[0063] Fuel consumption and CO2 emissions are as follows:

[0064] (5)

[0065] In equation (5), F i,t This indicates fuel consumption, expressed in MWh. This represents the thermal efficiency of power generation, and is dimensionless. This indicates the CO2 emissions from coal-fired power units, expressed in tons of CO2 (tCO2). This represents the emission factor per unit of fuel, expressed in tCO2 / MWh2 / GJ.

[0066] S302: Constructing a model of the upper bound of carbon capture and storage (CFS) system and a model of regeneration heat demand;

[0067] More specifically, the upper bound model for carbon capture and storage systems is as follows:

[0068] (6)

[0069] In equation (6), The amount of CO2 at time t is expressed in tCO2. This indicates the capture efficiency of the carbon capture and storage system.

[0070] The regeneration heat demand model for carbon capture and storage systems is as follows:

[0071] (7)

[0072] In equation (7), This represents the heat required for the regeneration of a carbon capture and storage system, expressed in GJ. This indicates the regeneration heat consumption, expressed in GJ / tCO2.

[0073] Because the upper limit of CCS capture capacity directly affects the operation of the P2G system, and the determination of the capture capacity needs to consider operating costs, and because the CCS regeneration heat demand is part of the heat balance constraint, and the optimization of the heat balance constraint also needs to consider operating costs, this step will also construct an operating cost model for the carbon capture and storage system. The operating cost model for the carbon capture and storage system is as follows:

[0074] (8)

[0075] In equation (8), Ccap(t) represents the unit capture cost, with the unit being yuan / tCO2.

[0076] This step uses the two models described above to calculate the upper limit of CO2 available for P2G. Regeneration heat requirements of CCS And the upper limit of the output / emission sequence of coal-fired units.

[0077] S4: Based on the carbon dioxide emissions of coal-fired units and the upper limit of carbon capture and storage systems, construct carbon source supply constraints for power-to-gas conversion devices, and construct electrical conversion constraints, stoichiometric constraints, and thermal coupling constraints in combination with regeneration heat demand. At the same time, construct a carbon cycle model to track the carbon flow of the system.

[0078] Specifically, this step establishes P2G electricity-to-gas conversion constraints, stoichiometry (CO2 demand) constraints, and thermal coupling constraints under the upper bound of CCS capture in step S3 and the electricity price / prediction in step S2. At the same time, carbon inventory variables are used to track the carbon cycle to avoid over-counting.

[0079] S401: P2G electro-gas conversion (energy-based) constraints and stoichiometric constraints;

[0080] More specifically, define the upper limit of P2G power and methane energy production:

[0081] (9)

[0082] (10)

[0083] In equations (9) and (10), This represents the electrical power input of the P2G at time t, in MW; This indicates the maximum electrical power of the P2G, measured in MW. The methane production at time t is expressed in MWh. This represents the overall energy efficiency of the electromethane transfer process. This parameter is dimensionless and typically ranges from 0.7 to 0.8.

[0084] Defining the measurement and conversion of CO2 demand and supply constraints:

[0085] (11)

[0086] (12)

[0087] In equations (11) and (12), The amount of CO2 required to produce P2G methane is expressed in tCO2; K represents the conversion factor from energy to CO2 mass. This indicates the amount of CO2 purchased, expressed in tCO2.

[0088] This step constrains the CO2 demand of P2G to the sum of CCS capture and external purchase through the above four formulas, and forms a "carbon availability-driven upper bound for P2G", that is, constructs the electrical conversion constraint and stoichiometric constraint of the power-to-gas device.

[0089] S402: P2G thermal coupling constraint;

[0090] Define the upper bound of heat demand for P2G:

[0091] (13)

[0092] In equation (13), This represents the amount of heat required by P2G at time t, expressed in GJ. This represents the regenerative heat from the CCS, expressed in MWh. This indicates the available waste heat that CHP can allocate for P2G regeneration, in MWh. This indicates the amount of heat stored and released, expressed in MWh.

[0093] Equation (13) forces the heat demand of P2G to not exceed the regeneration heat and waste heat that the system can provide.

[0094] S403: Carbon Account (to prevent over-counting), i.e., carbon cycle model.

[0095] The carbon cycle model includes a dynamic carbon inventory and net system emissions, where the dynamic carbon inventory is:

[0096] (14)

[0097] In equation (14), This represents the CO2 inventory within the system that can be used for synthesis or storage, expressed in tCO2. This represents the CO2 used for P2G in the current period, in units of tCO2; This indicates the amount of CO2 permanently stored in the current period, expressed in tCO2.

[0098] The system's net emissions are:

[0099] (15)

[0100] In equation (15), This represents the system's net CO2 emissions for the current period, used for carbon cost calculations, and is expressed in tCO2.

[0101] This step can calculate the methane production using the constraints described above. The amount of CO2 used for P2G Carbon inventory And the electrical / thermal requirements of P2G.

[0102] S5: Based on the electrical conversion constraints, stoichiometric constraints, and thermal coupling constraints of the power-to-gas conversion device, as well as the carbon cycle model, construct a dynamic model of the cogeneration system and energy storage system, and form a thermal balance constraint.

[0103] Specifically, this step establishes the dynamic equations and capacity constraints of the fuel → electricity / heat relationship, heat-to-power ratio constraint, waste heat availability, and various energy storage (electricity / heat / gas) for CHP based on the constraints generated in step S4, and forms thermal balance constraints for subsequent optimization steps.

[0104] S501: CHP power generation-heating relationship;

[0105] Define CHP fuel-electric / thermal output:

[0106] (16)

[0107] In equation (16), m represents the CHP unit index; This indicates the power generation capacity of the CHP plant, measured in MW. This indicates the amount of fuel energy consumed by CHP; This indicates the heating power of CHP, measured in mWth. These represent the electrical efficiency and thermal efficiency of CHP, respectively.

[0108] Define the CHP thermoelectric ratio and waste heat availability:

[0109] (17)

[0110] In equation (17), These represent the allowed hotspot ratio ranges for CHP; This represents the amount of heat (available heat output) that the m-th CHP unit can supply during time period t, typically expressed in kWh or MWh (if energy) or kW (if power, depending on the model). Here, it represents the portion of the heat generated by the unit that can be supplied externally. This represents the proportion of waste heat (0-1) of the m-th CHP unit that can be used for regeneration or external heating.

[0111] S502: Energy Storage (Electric / Heat / Gas) Dynamics;

[0112] Define the dynamic equation for electric energy storage:

[0113] (18)

[0114] In equation (18), This represents the energy stored in electricity at time t, expressed in MWh. These represent the charging / discharging power of the energy storage, in MW. This indicates the charging / discharging efficiency of the energy storage system.

[0115] Define the limits for electrical energy storage capacity and power:

[0116] (19)

[0117] In equation (19), This indicates the maximum energy storage capacity, measured in MWh.

[0118] S503: Heat network (simplified energy balance).

[0119] Define a simplified balance for the heating network;

[0120] (20)

[0121] In equation (20), Indicates thermal storage release / charge capacity, in MWh; This indicates the amount of waste heat recovered by the unit, expressed in MWh. This represents heat load, measured in MWth. This indicates the heat demand for CCS regeneration; This indicates the heat demand of the electro-gas conversion device.

[0122] The output of this step includes the available electrical / thermal upper and lower limits of CHP, energy storage state variables and their available charge / discharge amounts, and the heating network's supply-demand balance capacity, which are provided to step S6.

[0123] S6: Construct a demand response model based on the dynamic model of the cogeneration system and energy storage system, and optimize it using thermal balance constraints; the demand response model adopts a two-stage optimization scheduling framework to realize dynamic modeling and closed-loop adaptive scheduling of demand response; extract the spatiotemporal features of demand response resources based on graph attention network to realize dynamic modeling and closed-loop adaptive scheduling of demand response.

[0124] Specifically, this step summarizes the objectives and constraints of steps S1–S5 to construct a two-stage solvable framework: Day-ahead (DA) determines committed variables (such as coal-fired unit start-up and shutdown) and hourly plans; real-time (RT) adjusts the plans based on rolling time-domain adjustments and prioritizes the use of energy storage and DR to buffer fluctuations, ensuring both economic efficiency and reliability. This step also provides backup constraints, the two-stage connection relationship, and implementation suggestions (linearization, decomposition algorithms, solver selection), forming a closed-loop improvement.

[0125] S601: DR response modeling;

[0126] (twenty one)

[0127] In equation (21), This represents the response of DR resource j at time t, in MW, with a positive value indicating a decrease in load. This represents the baseline load of DR resources, in MW. This represents the price elasticity coefficient; This represents the linear price sensitivity coefficient, expressed in MW / (yuan / MWh). This indicates the real-time electricity price and the benchmark electricity price, in yuan / MWh; This represents the incentive price for DR, expressed in yuan / MWh, used to calculate the compensation cost. .

[0128] S602: Electricity / Heat / Gas / Carbon Balance, i.e.:

[0129] (twenty two)

[0130] In equation (22), This represents the power input to the power grid, in MW. This indicates the power load, measured in MW. This indicates the amount of wind and solar power that has been curtailed, measured in MW.

[0131] Thermal equilibrium:

[0132] (twenty three)

[0133] Gas (methane) balance:

[0134] (twenty four)

[0135] In equation (24), This indicates the amount of gas energy released, measured in MWh / gas. This indicates the amount of gas sold to external parties, measured in MWhgas.

[0136] S603: Two-stage decision-making logic and related constraints;

[0137] The continuity constraint for DA ↔RT is:

[0138] (25)

[0139] In equation (25), This indicates the day-to-day and real-time output, in MW. Indicates the allowed real-time float, in MW.

[0140] S604: Reserve capacity constraint, i.e.:

[0141] (26)

[0142] In equation (26), This indicates the required reserve capacity, expressed in MW. This indicates that it is callable.

[0143] This step can output the DA executable plan (unit commitment, P2G / CHP / energy storage daily plan, DR budget), RT dispatch instructions (actual output, DR trigger, energy storage call) and operational indicators.

[0144] This embodiment provides a comprehensive scheduling optimization method based on coal-fired power generation, electricity-to-gas conversion, combined heat and power (CHP), and demand response. The method prioritizes minimizing the total system operating cost while also considering carbon emission reduction and renewable energy consumption. The core innovation lies in treating demand response (DR) as an active and learnable scheduling resource: a graph attention network (GAT) is used to model the collaborative response capabilities among multiple users / regions, and the GAT output (dynamic attention / available DR capabilities) is embedded into a two-stage (day-ahead / real-time) mixed integer optimization model to form a closed-loop adaptive scheduling mechanism.

[0145] This embodiment introduces a multi-energy coupling and optimization scheduling model, combined with an adaptive weighting mechanism and a graph attention network (GAT), to achieve collaborative optimization of different energy systems, thereby minimizing the total operating cost of the system, reducing carbon emissions, and improving the flexibility and reliability of the energy system, thus supporting a more efficient and greener energy transition process.

[0146] The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response provided in this embodiment has the following advantages:

[0147] 1. Traditional demand response (DR) models are mostly independent price elasticity models. This example uses GAT to learn the dependencies between nodes (geographic / user type / temporal features), which can generate dynamic attention weights or predict the available DR capacity. This makes demand response a "networked and learnable" control resource, thereby improving the efficiency of group response and robustness to jitter.

[0148] 2. CCS–P2G carbon closed-loop coupling into the scheduling model: The CCS capture amount is directly used as the upper bound of P2G, and the carbon inventory variable is used to track the carbon cycle to avoid the problem of "over-counting carbon". At the same time, the CCS regeneration heat is used as part of the heat balance to form a linkage constraint of heat-carbon-electricity-gas.

[0149] 3. Two-stage (DA / RT) + GAT closed-loop adaptation: The output of GAT is injected into the RT layer in real time or hourly (or as a scenario parameter of DA), making scheduling more adaptive in the face of uncertainty.

[0150] 4. Multi-objective (cost-carbon-consumption) optimization and engineering solution: Complex MINLP transformation and decomposition solution strategies are incorporated into the implementation plan (linearization, SOCP relaxation, Benders / C&CG decomposition, rolling optimization) to balance solvability and proximity to the global optimum.

[0151] Example 2

[0152] This embodiment provides a comprehensive scheduling optimization system based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response, including:

[0153] The system modeling and initialization module is used to construct and initialize the energy dispatch system architecture model. The energy dispatch system architecture model includes coal-fired units, carbon capture and storage systems, power-to-gas conversion devices, combined heat and power systems, energy storage systems, and demand response, and establishes a topology structure based on energy flow.

[0154] The objective function construction module is used to construct a comprehensive objective function based on carbon emission constraints and the energy dispatch system architecture model, with the goal of minimizing the total operating cost of the energy dispatch system.

[0155] The coal-fired unit and carbon capture modeling module is used to construct the basic constraint and emission model of the coal-fired unit based on the comprehensive objective function, and to construct the upper bound model of the carbon capture and storage system and the regeneration heat demand model.

[0156] The P2G system and carbon cycle modeling module are used to construct the carbon source supply constraints of the power-to-gas device based on the carbon dioxide emissions of the coal-fired unit and the upper limit of the capture of the carbon capture and storage system, and to construct electrical conversion constraints, stoichiometric constraints and thermal coupling constraints in combination with the regeneration heat demand. At the same time, a carbon cycle model is constructed to track the carbon flow of the system.

[0157] The modeling module for cogeneration system and energy storage device is used to construct a dynamic model of the cogeneration system and the energy storage system based on the electrical conversion constraints, stoichiometric constraints, thermal coupling constraints of the power-to-gas device and the carbon cycle model, and to form thermal balance constraints.

[0158] The demand response and integrated scheduling module is used to construct a demand response model based on the dynamic models of the cogeneration system and the energy storage system, and to optimize it using the thermal balance constraints. The demand response model adopts a two-stage optimization scheduling framework to realize dynamic modeling and closed-loop adaptive scheduling of demand response. The spatiotemporal features of demand response resources are extracted based on graph attention networks to realize dynamic modeling and closed-loop adaptive scheduling of demand response.

[0159] For details on the specific implementation of each module in a comprehensive scheduling optimization system based on coal, electricity-to-gas, cogeneration, and demand response, please refer to the above description of the limitations of a comprehensive scheduling optimization method based on coal, electricity-to-gas, cogeneration, and demand response; these details will not be repeated here.

[0160] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A comprehensive scheduling optimization method based on coal-fired, electricity-to-gas, heat and power, and demand response, characterized in that, include: Step 1: Construct and initialize the energy dispatch system architecture model; the energy dispatch system architecture model includes coal-fired power units, carbon capture and storage system, power-to-gas conversion device, combined heat and power system, energy storage system and demand response, and establish a topology structure based on energy flow; Step 2: With minimizing the total operating cost of the energy dispatch system as the optimization objective, construct a comprehensive objective function based on carbon emission constraints and the energy dispatch system architecture model; Step 3: Construct the basic constraint and emission model of the coal-fired unit based on the comprehensive objective function, and construct the upper bound model of carbon capture and storage system and the regeneration heat demand model. Step 4: Based on the carbon dioxide emissions of the coal-fired unit and the upper limit of the carbon capture and storage system, construct the carbon source supply constraint of the power-to-gas conversion device, and construct the electrical conversion constraint, stoichiometric constraint and thermal coupling constraint in combination with the regeneration heat demand. At the same time, construct a carbon cycle model to track the carbon flow of the system. Step 5: Based on the electrical conversion constraints, stoichiometric constraints, and thermal coupling constraints of the electro-gas conversion device, as well as the carbon cycle model, construct dynamic models of the combined heat and power system and the energy storage system, and form thermal balance constraints; the thermal balance constraints are: wherein, represents the heat supply power of cogeneration; represents the heat storage / charge amount; represents the waste heat recovery amount of the unit; represents the heat load; represents the regeneration heat demand of the carbon capture and storage system; represents the heat demand of the electric-gas conversion device; Step 6: Construct a demand response model based on the dynamic models of the cogeneration system and the energy storage system, and optimize it using the thermal balance constraints; the demand response model adopts a two-stage optimization scheduling framework to realize dynamic modeling and closed-loop adaptive scheduling of demand response; extract the spatiotemporal features of demand response resources based on graph attention network to realize dynamic modeling and closed-loop adaptive scheduling of demand response.

2. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 1, the initialization process involves inputting the capacity, efficiency, thermoelectric ratio, energy storage capacity and charge / discharge efficiency, demand response adjustable range, fuel price, carbon trading price, electricity / heat / gas load forecast, and renewable energy forecast for each device.

3. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 2, the comprehensive objective function is: where min denotes minimum; J denotes total operation cost of the energy dispatch system; t denotes time index; T denotes dispatch period; C fuel (t) denotes fuel cost; C buy (t) denotes net cost of interaction with the grid; C op (t) denotes equipment operation and maintenance cost; Cco2(t) denotes carbon trading or carbon emission cost; C curt (t) denotes wind or solar curtailment penalty cost; C DR (t) denotes demand response compensation cost.

4. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 3, the basic constraints and emission model of the coal-fired unit includes: upper and lower limits of power generation output and online status, minimum online time, ramping constraints, and fuel consumption and CO2 emissions.

5. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 4, the carbon cycle model includes dynamic carbon inventory and system net emissions.

6. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 6, the two-stage optimization scheduling framework is as follows: determining the commitment variables and hourly plan a day in advance and adjusting the plan in real time based on rolling time domain.

7. The integrated scheduling optimization method based on coal-fired power, electricity-to-gas conversion, combined heat and power, and demand response as described in claim 1, is characterized in that, In step 6, the demand response model is constructed based on a graph attention network, where each demand response resource is treated as a node in the graph, the coupling relationship between resources is defined as an edge in the graph, and the features of neighboring nodes are aggregated through an attention mechanism to calculate the response quantity.

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