Collaborative optimization scheduling method, device, medium and product for wind, light, biogas, electricity and hot gas comprehensive energy system containing carbon capture and electricity-to-gas conversion

Abstract

The invention discloses a collaborative optimization scheduling method, device, medium and product for a wind, light, methane, electricity and hot gas comprehensive energy system with carbon capture and electricity-to-gas conversion, and relates to the technical field of energy system optimization scheduling, and the method comprises the steps: constructing a multi-energy flow coupling model; based on the multi-energy flow coupling model, adopting Latin hypercube sampling and Kantorovich distance to determine a plurality of typical scenes; based on the multi-energy-flow coupling model and the multiple typical scenes, taking the minimum total operation cost of the wind-light-biogas-electricity hot gas comprehensive energy system as a target function, and taking energy flow distribution balance and equipment operation limit values in the wind-light-biogas-electricity hot gas comprehensive energy system as constraint conditions, constructing a collaborative optimization scheduling model; solving the collaborative optimization scheduling model to obtain a day-ahead 24-hour energy distribution plan; the day-ahead 24-hour energy distribution plan is dynamically corrected, and real-time scheduling is achieved. According to the invention, real-time scheduling of the wind-light-methane-electricity-hot gas comprehensive energy system containing carbon capture and electricity-to-gas conversion is realized.

Description

Technical Field

[0001] The present application relates to the technical field of energy system optimization and scheduling, and in particular to a method, device, medium and product for collaborative optimization and scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas. Background Art

[0002] With fossil energy resources becoming increasingly depleted and natural disasters caused by the greenhouse effect becoming more frequent, promoting clean, low-carbon, safe, and efficient energy utilization has become a key direction and path for achieving global energy transformation. Clean energy generation technologies such as wind power and photovoltaics are becoming increasingly mature, but the development of new energy sources faces multiple challenges, including large-scale development, efficient local consumption, and ensuring a secure and reliable power supply. In this context, integrated energy systems, featuring complementary multi-energy sources, serve as a key platform connecting energy supply and demand. By rationally optimizing the scheduling and allocation of heterogeneous energy sources, they achieve the coupling and combined supply of multiple energy sources, improve the overall efficiency of energy utilization, and effectively enhance the capacity and reliability of renewable energy consumption. Integrated energy systems encompass multiple energy sources, including electricity, gas, and heat, and can be integrated through the coordinated optimization of energy conversion devices, storage facilities, and other equipment to form an integrated production, supply, and marketing system.

[0003] Power-to-gas (P2G) technology offers a new approach to energy conversion and spatial and temporal transfer. By converting surplus wind and solar energy into easily storable natural gas, it addresses the problem of renewable energy consumption and achieves peak load shifting. Currently, P2G is widely used in electrically coupled energy systems, improving wind and solar power consumption capacity and system operational flexibility. Providing natural gas to micro-turbines through P2G systems not only reduces carbon emissions but also lowers the system's natural gas procurement costs, improving the overall system's economic efficiency. The application of P2G devices to integrated energy systems holds significant practical engineering value and significance.

[0004] A Carbon Capture Power Plant (CCPP) is a coal-fired or gas-fired power plant transformed with carbon capture and storage (CCS) technology. It can significantly reduce carbon emissions. By controlling carbon capture energy consumption, the CCPP has the characteristics of flexible operation and rapid response. Compared with traditional power plants, it can achieve low-carbon and economical dispatch operation.

[0005] However, current research on P2G-CCPP mainly focuses on electrical interconnection systems. There is no integrated energy system of electricity, gas and heat, nor is coordinated and optimized scheduling among multiple energy sources such as electricity, gas and heat achieved. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, medium and product for collaborative optimization scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas, so as to realize real-time scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for collaborative optimization scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system including carbon capture and power-to-gas, comprising:

[0009] Construct a multi-energy flow coupling model for a wind-solar-biogas-electricity-heat-gas integrated energy system with carbon capture and power-to-gas;

[0010] Based on the multi-energy flow coupling model, Latin hypercube sampling and Kantorovich distance are used to determine typical scenarios of multiple wind power outputs and multiple photovoltaic outputs;

[0011] Based on the multi-energy flow coupling model, typical scenarios of multiple wind power outputs and typical scenarios of multiple photovoltaic outputs, a collaborative optimization scheduling model is constructed with the minimization of the total operating cost of the wind-solar-biogas-electricity-heat-gas integrated energy system as the objective function, and with the energy flow distribution balance and equipment operation limits in the wind-solar-biogas-electricity-heat-gas integrated energy system as constraints;

[0012] The collaborative optimization scheduling model is solved by adopting a piecewise linearization method to obtain a 24-hour energy distribution plan for the wind, solar, biogas, electricity, heat and gas integrated energy system;

[0013] The energy distribution plan for the previous 24 hours is dynamically revised to achieve real-time scheduling.

[0014] In the second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for collaborative optimization scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas.

[0015] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the collaborative optimization scheduling method for a wind-solar-biogas-electricity-heat-gas integrated energy system containing carbon capture and power-to-gas as described in any of the above items.

[0016] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the collaborative optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas as described in any of the above items.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] The present application discloses a collaborative optimization scheduling method, device, medium and product for a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas. First, a multi-energy flow coupling model of the wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas is constructed; then, based on the multi-energy flow coupling model, Latin hypercube sampling and Kantorovich distance are used to determine multiple typical scenarios of wind power output and multiple typical scenarios of photovoltaic output; secondly, based on the multi-energy flow coupling model, multiple typical scenarios of wind power output and multiple typical scenarios of photovoltaic output, a collaborative optimization scheduling model is constructed with the minimization of the total operating cost of the wind, solar, biogas, electricity, heat and gas integrated energy system as the objective function and the energy flow distribution balance and equipment operation limit values ​​in the wind, solar, biogas, electricity, heat and gas integrated energy system as constraints; subsequently, a piecewise linearization method is used to solve the collaborative optimization scheduling model to obtain a 24-hour energy distribution plan for the wind, solar, biogas, electricity, heat and gas integrated energy system; finally, the 24-hour energy distribution plan is dynamically corrected to achieve real-time scheduling. This application uses a collaborative optimization scheduling model and dynamic correction to achieve real-time scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a method for collaborative optimization scheduling of a wind-solar-biogas-electricity-heat-gas integrated energy system including carbon capture and power-to-gas provided in one embodiment of the present application;

[0021] Figure 2 This is a schematic diagram of the structure of the wind, solar, biomass, electricity, heat and gas integrated energy system;

[0022] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] The purpose of this application is to provide a method, device, medium and product for collaborative optimization scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas, aiming to achieve real-time scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas.

[0025] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0026] In an exemplary embodiment, Figure 1 As shown, a coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system including carbon capture and power-to-gas is provided, including:

[0027] Step 1: Construct a multi-energy flow coupling model for a wind-solar-biogas-electricity-heat-gas integrated energy system with carbon capture and power-to-gas.

[0028] Specifically, the multi-energy flow network topology of a wind, solar, biogas, electricity, and heat integrated energy system was established. This integrated energy sources, including wind, solar, and biogas, were combined with carbon capture and power-to-gas technologies to construct a multi-energy flow coupling model for this system. Load forecasting data was used to determine the energy distribution within the system, providing a foundational framework for subsequent optimized scheduling.

[0029] The structure of the wind, solar, biomass, electricity, heat and gas integrated energy system in this embodiment is as follows Figure 2 The wind, solar, biogas, electricity, and heat and gas integrated energy system includes a biogas tank, a carbon capture device, a combined heat and power unit, an electric energy storage device, an electric boiler, a gas boiler, a heat storage device, a power-to-gas device, and an electrolyzer. The carbon capture device consumes grid electricity to capture CO2 produced by the cogeneration unit and sends it to a dedicated CO2 pipeline network. The power-to-gas device synthesizes natural gas from hydrogen and CO2 produced by water electrolysis and sends it to the natural gas pipeline network for use by gas loads and the cogeneration unit. The natural gas produced by the biogas digester is sent to the natural gas pipeline network, and the CO2 is sent to the CO2 pipeline network for use by gas loads and the power-to-gas device. The cogeneration unit uses pipeline natural gas to produce electricity and heat, with electricity sent to the grid and heat sent to the thermal system. Wind power generation and photovoltaic power generation directly send electricity to the power system. Electric boilers and gas boilers use grid electricity and natural gas from gas pipelines to produce heat and send it to the thermal system. The system load side includes electricity, heat, and gas loads. The electricity load is directly connected to the grid, which is equipped with electric energy storage to provide power supply reliability and economy. The heat load is directly connected to the thermal system, which is equipped with thermal energy storage to provide heating reliability and economy. The gas load is directly connected to the weather heating pipeline. Through multi-energy coupling and coordinated optimization scheduling, clean energy can be consumed and utilized on-site, promoting the low-carbon and green transformation and upgrading of users' energy use.

[0030] Step 2: Based on the multi-energy flow coupling model, Latin hypercube sampling and Kantorovich distance are used to determine the typical scenarios of multiple wind power outputs and multiple photovoltaic outputs.

[0031] As an optional implementation, step 2 includes:

[0032] Step 21: Based on the multi-energy flow coupling model, Latin hypercube sampling is used to generate multiple random scenarios of wind power output and multiple random scenarios of photovoltaic output.

[0033] Step 22: Based on the Kantorovich distance between each random scenario, the random scenarios of wind power output and the random scenarios of photovoltaic output are reduced to obtain multiple typical scenarios of wind power output and multiple typical scenarios of photovoltaic output.

[0034] Specifically, due to the uncertainty of wind and solar power output, the Latin hypercube sampling technique is used to generate multiple random wind power and photovoltaic output scenarios that follow probability distribution constraints. These scenarios are then reduced using a scenario reduction method based on Kantorovich distance to simulate the uncertainty of wind power and photovoltaic output, as follows:

[0035]

[0036] Where F(PV) is the photovoltaic probability density function; σ PV is the standard deviation of photovoltaic power; P V is the actual photovoltaic output; μ PV is the expected photovoltaic power; F(PW) is the expected wind power; σ PW is the standard deviation of wind power; P W is the actual output of wind power; μ PW Expected wind power.

[0037] The steps to set the sampling scale to 1000 and use Latin hypercube sampling to generate photovoltaic multi-scenario output are as follows:

[0038] The probability density function F(PV) curve is divided into 1000 intervals, each with a spacing of 1 / 1000.

[0039] Take a number from each of the 1000 intervals and set the cumulative probability P of the xth interval x for:

[0040] P x =(1 / 1000)r+(x-1) / 1000.

[0041] Where r is a random number between 0 and 1.

[0042] P xBring back F(PV) and F(PW) to get the sampling value K of each interval x1 and K x2 :

[0043]

[0044] The sampling value K of 1000 intervals x1 Construct 1000 random scenes of wind power output, and the sampling value K of 1000 intervals x2 Constitute 1000 random scenarios of photovoltaic output.

[0045] Set the number of scenes after reduction to 5 and calculate the Kantorovich distance D of each scene x (s i ,s j ):

[0046]

[0047] Among them, D x (s i ,s j ) is the Kantorovich distance formula; is the output of scene i in period t; is the output of scene j in period t.

[0048] Select the closest scene j within the range of scene i, and set the product of the probability and Kantorovich distance of the two scenes to be equal.

[0049] Select the scene d with the minimum product and delete it, then update the number of scenes minus 1. At this time, the probability of scene j is P j Updated to P j =P i +P d Until a typical scenario of photovoltaic and wind power output is obtained. i is the probability of scene i; P d is the probability of scenario d.

[0050] Step 3: Based on the multi-energy flow coupling model, typical scenarios of multiple wind power outputs and typical scenarios of multiple photovoltaic outputs, with the minimization of the total operating cost of the wind-solar-biogas-electricity-heat-gas integrated energy system as the objective function, and the energy flow distribution balance and equipment operation limits in the wind-solar-biogas-electricity-heat-gas integrated energy system as constraints, a collaborative optimization scheduling model is constructed.

[0051] As an optional implementation, the objective function includes:

[0052] F=min{f w +f q +f c+f g +f b}.

[0053]

[0054]

[0055] Where F is the objective function; f w is the system operation and maintenance cost; f q Penalty cost for curtailing wind and solar power; f c is the CO2 related cost; f g is the comprehensive energy purchase cost; f b is the biogas digester production cost; T is the day-ahead system scheduling time, 24 hours; Ω is the set of all units in the wind-solar-biogas-electricity-heat-gas integrated energy system, Ω = {EB, GB, CHP, BT, P2G, CCS}, where EB is the electric boiler; GB is the gas boiler, CHP is the combined heat and power unit, BT is the biogas digester, P2G is the power-to-gas unit, and CCS is the carbon capture unit. is the startup cost of the i-th unit; is the operating status of the i-th unit in period t. When it is running, During outage is the operating status of the i-th unit in period t-1; is the shutdown cost of the i-th unit; is the maintenance cost of the i-th unit; P t i is the output power of the i-th unit during period t; C pen is the renewable energy penalty coefficient; is the wind power curtailment power in period t; is the photovoltaic power reduction during period t; r em is the excess carbon emission factor; Q is the amount of CO2 that is not captured and emitted into the atmosphere during the carbon capture device during period t; em is the standard CO2 emission quota; r sto Cost coefficients for storing CO2 in a wind, solar, biomass, electricity, heat and gas integrated energy system; The amount of CO2 stored in the wind, solar, biomass, electricity, heat and gas integrated energy system; e,t is the electricity price during period t; The electricity purchased from the external market for the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; g,t is the gas price during period t; The amount of gas purchased from the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; r bio,t is the cost coefficient of gas production in the biogas digester during period t; is the amount of biogas produced by the biogas digester during period t.

[0056] As an optional implementation method, the constraints include: cogeneration unit constraints, gas boiler operation constraints, electric boiler operation constraints, electric energy storage constraints, thermal energy storage constraints, renewable energy unit constraints, grid power balance constraints, unit output power upper and lower limit constraints, unit ramp constraints, unit start and stop constraints, biogas operation constraints, power-to-gas device constraints and carbon capture output constraints.

[0057] As an optional implementation, the combined heat and power unit constraints include:

[0058]

[0059] in, is the output power of the cogeneration unit during period t; is the maximum output power of the cogeneration unit; k1, k2 and k3 are the operating parameters of the cogeneration unit; is the output thermal power of the cogeneration unit during period t; is the minimum output power of the cogeneration unit; η CHP is the heat-to-electricity conversion rate of the cogeneration unit; H CH4 is the calorific value of natural gas; V is the volume of natural gas consumed by the cogeneration unit during period t; m is the molar volume of the gas under standard conditions.

[0060] As an optional implementation, the gas boiler operation constraints include:

[0061]

[0062] in, is the output thermal power of the gas boiler during period t; η GB The conversion efficiency of gas boiler; is the amount of natural gas consumed by the gas boiler during period t.

[0063] As an optional implementation, the electric boiler operation constraints include:

[0064]

[0065] in, is the output thermal power of the electric boiler during period t; η EB is the energy conversion efficiency of the electric boiler; is the electric power consumed by the electric boiler during period t.

[0066] Specifically, electric energy storage constraints include:

[0067]

[0068] in, is the energy storage capacity of the energy storage device during period t; γ EES is the energy consumption coefficient of the electric energy storage device; is the energy storage capacity of the energy storage device during period t-1; is charging efficiency; is the charging power of the energy storage device during period t; is the discharge efficiency of the electric energy storage device; is the discharge power of the energy storage device during period t; It is the lower limit of electric energy storage capacity; The upper limit of electric energy storage capacity; is the lower limit of charging power; The upper limit of charging power; is the lower limit of discharge power; is the upper limit of discharge power; is the electric energy storage capacity during the initial period; It is the electric energy storage capacity at the end of the period (T period).

[0069] Thermal storage constraints include:

[0070]

[0071] in, is the thermal energy storage capacity of the thermal energy storage device during period t; γ TES is the energy consumption coefficient of the thermal energy storage device; is the thermal energy storage capacity of the thermal energy storage device during period t-1; is the heat storage efficiency; is the thermal storage power of the thermal energy storage device during period t; is the heat release efficiency of the thermal energy storage device; is the heat release power of the thermal energy storage device during period t; is the lower limit of thermal energy storage capacity; is the upper limit of thermal energy storage capacity; is the lower limit of heat storage power; is the upper limit of heat storage power; is the lower limit of heat release power; is the upper limit of heat release power; is the thermal energy storage capacity during the initial period; is the thermal energy storage capacity at the end of the period (T period).

[0072] Renewable energy fleet constraints include:

[0073]

[0074] in, is the available wind power in period t; is the actual power output of the wind turbine during period t; is the available photovoltaic power in period t; is the actual power output of the photovoltaic unit during period t.

[0075] Grid power balance constraints include:

[0076]

[0077] in, The electricity sold to the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; is the electric power consumed by the power-to-gas device during period t; is the electric power load of the carbon capture device during period t; is the electricity load of the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; The heat energy purchased from the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; is the heat load of the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; The heat energy sold to the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; is the amount of CH4 produced by the biogas digester during period t; The amount of CH4 produced by the power-to-gas plant; The gas energy sold to the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; is the gas load of the wind, solar, biomass, electricity, heat and gas integrated energy system during period t.

[0078] The upper and lower limits of unit output power include:

[0079]

[0080] in, is the minimum output power of the i-th unit; is the maximum output power of the i-th unit.

[0081] The unit ramp constraints include:

[0082]

[0083] in, is the maximum downward climbing power of the i-th unit; is the output power of the i-th unit during period t-1; is the maximum upward climbing power of the i-th unit.

[0084] Unit start and stop constraints include:

[0085]

[0086] in, is the start-up time of the i-th unit in period t; is the minimum startup time of the i-th unit; is the operating status of the i-th unit in period t+1; is the stop time of the i-th unit in period t; is the minimum stop time of the i-th unit; is the minimum power for starting and stopping the i-th unit; Ω CHP is the set of combined heat and power units; Ω EB For electric boiler collection; Ω GB For gas boiler collection; Ω BP is the biogas tank collection; Ω P2G For the power-to-gas device assembly; Ω CCS Assembled for carbon capture devices.

[0087] Biogas operation constraints include:

[0088]

[0089] Among them, Q zq is the amount of biogas utilized; is the gas production per unit time in the biogas digester; H BP is the calorific value of biogas.

[0090] Power-to-gas unit constraints include:

[0091]

[0092] in, The minimum limit for the electric power consumption of the power-to-gas device; The maximum limit of electric power consumption of the power-to-gas device; The maximum downward ramp rate of electric power consumed by the power-to-gas device; is the electric power consumed by the power-to-gas device during period t-1; The maximum upward ramp rate at which the gas conversion device consumes electrical power.

[0093] Carbon capture output constraints include:

[0094]

[0095] in, is the minimum electrical power load of the carbon capture device; is the maximum electrical power load of the carbon capture device.

[0096] Step 4: Use the piecewise linearization method to solve the collaborative optimization scheduling model and obtain the day-ahead 24-hour energy distribution plan for the wind, solar, biomass, electricity, heat and gas integrated energy system.

[0097] Specifically, a piecewise linear programming approach was used to solve the collaborative optimization scheduling model with a one-hour step size, resulting in an optimal configuration plan for each energy supply device in the wind, solar, biogas, electricity, heat, and gas integrated energy system—the 24-hour energy allocation plan for the day ahead. A piecewise linearization approach was used to transform the nonlinear optimization model into a linear problem, solving the 24-hour energy allocation plan. This plan clarified the output plan for electricity, heat, and gas supply devices for each time period, and dynamically revised the scheduling plan based on measured load data, achieving real-time optimization.

[0098] Step 5: Dynamically revise the energy distribution plan for the previous 24 hours to achieve real-time scheduling.

[0099] Specifically, based on the previous 24-hour energy allocation plan and combined with real-time load data, the operating status of power-to-gas devices, energy storage devices, etc. will be dynamically adjusted to give priority to absorbing wind and photovoltaic energy, optimize carbon capture efficiency, and ultimately achieve low-carbon economic operation of the system.

[0100] During the dispatching process, the operating status of the wind, solar, biomass, electricity, heat and gas integrated energy system is detected in real time, and the electric load, heat load and gas load of the integrated energy system are obtained in real time.

[0101] By comparing the measured data with the load forecast data of the previous 24 hours, and based on the deviation of the comparison data and the constructed collaborative optimization scheduling model, the energy configuration plan of the optimized scheduling is corrected in real time with a calculation step of 15 minutes, and the optimized scheduling plan for 4 hours within the day is further obtained, giving 16 optimized configuration plans within the day.

[0102] The optimized scheduling plan for 4 hours within a day is corrected in real time using a rolling method, with a rolling cycle of 15 minutes. The optimized configuration plan for the next 4 hours is obtained, that is, the optimized scheduling plan for 4 hours within the day.

[0103] According to the optimized dispatching plan within 4 hours of the day, the output dispatching plan of the energy supply equipment is adjusted in real time.

[0104] During intraday scheduling, rolling corrections are made based on the energy allocation plan for the previous 24 hours. Simultaneously, the system load, including electrical load, thermal load, and gas load, is measured in real time. Based on the measured electrical, thermal, and gas load data, and compared with the load forecast data for the previous 24 hours, the load forecast data is rolled over to obtain the load data for the four hours of the day. Based on the established optimization scheduling model, the four-hour optimization scheduling plan is re-solved to obtain the four-hour intraday optimization scheduling plan. The four-hour intraday optimization scheduling plan uses a 15-minute resolution to solve the four-hour intraday optimization scheduling model, giving a total of 16 optimization results, forming a four-hour intraday energy optimization scheduling plan. The four-hour intraday scheduling must be combined with the intraday load output data, with optimization calculations performed every 15 minutes. With a rolling cycle of one hour, the optimal configuration plan for the next four hours is obtained. This cycle continues to complete the intraday collaborative optimization scheduling plan.

[0105] Specifically, at 0:00 AM, based on the forecast data from the previous 24 hours, a scheduling plan for the next 24 hours can be constructed, i.e., the energy allocation plan for the period from 0:00 AM to 12:00 AM. At 2:00 AM, based on the forecast data for the four hours within the day, a scheduling plan for the next four hours can be constructed, i.e., the plan for 2:00 AM to 6:00 AM. In essence, during the period from 0:00 AM to 2:00 AM, the wind, solar, biogas, electricity, and heat and gas integrated energy system has already obtained measured data, and the deviation between the measured data and the forecast data has been determined. Based on this deviation, the scheduling plan for 2:00 AM to 6:00 AM is adjusted. Simultaneously, the intraday scheduling is calculated every 15 minutes, such as from 2:15 AM to 6:15 AM. At this time, the scheduling plan for 2:15 AM to 6:15 AM is further revised based on the deviation between the measured data and the forecast data from 2:00 AM to 2:15 AM. This cycle repeats, completing the rolling optimization scheduling. For example, if the actual power output of the wind farm in the period of 0:00-2:00 is less than the predicted output, more electricity can be drawn from the transmission network in the next period of 2:00-6:00, that is, the output active power of the power grid in the next period of 2:00-6:00 can be increased, and the energy configuration scheduling plan for the next 4 hours can be corrected in real time.

[0106] In an exemplary embodiment, a computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for collaboratively optimizing the scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system including carbon capture and power-to-gas.

[0107] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for collaboratively optimizing the scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas is implemented.

[0108] In an exemplary embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for collaboratively optimizing the scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system including carbon capture and power-to-gas.

[0109] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a collaborative optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas is realized.

[0110] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0111] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0112] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for collaborative optimization scheduling of a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas, characterized in that: The coordinated optimization scheduling method for the wind, solar, biogas, electricity, heat and gas integrated energy system including carbon capture and power-to-gas includes: Construct a multi-energy flow coupling model for a wind-solar-biogas-electricity-heat-gas integrated energy system with carbon capture and power-to-gas; Based on the multi-energy flow coupling model, Latin hypercube sampling and Kantorovich distance are used to determine typical scenarios of multiple wind power outputs and multiple photovoltaic outputs; Based on the multi-energy flow coupling model, typical scenarios of multiple wind power outputs and typical scenarios of multiple photovoltaic outputs, a collaborative optimization scheduling model is constructed with the minimization of the total operating cost of the wind-solar-biogas-electricity-heat-gas integrated energy system as the objective function, and with the energy flow distribution balance and equipment operation limits in the wind-solar-biogas-electricity-heat-gas integrated energy system as constraints; The collaborative optimization scheduling model is solved by adopting a piecewise linearization method to obtain a 24-hour energy distribution plan for the wind, solar, biogas, electricity, heat and gas integrated energy system; The energy distribution plan for the previous 24 hours is dynamically revised to achieve real-time scheduling.

2. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 1 is characterized in that: Based on the multi-energy flow coupling model, Latin hypercube sampling and Kantorovich distance are used to determine multiple typical wind power output scenarios and multiple typical photovoltaic output scenarios, including: Based on the multi-energy flow coupling model, Latin hypercube sampling is used to generate multiple random scenarios of wind power output and multiple random scenarios of photovoltaic output; Based on the Kantorovich distance between each random scenario, the random scenarios of wind power output and photovoltaic output are reduced to obtain multiple typical scenarios of wind power output and multiple typical scenarios of photovoltaic output.

3. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 1 is characterized in that: The objective function includes: F=min{f w +f q +f c +f g +f b }; Where F is the objective function; f w is the system operation and maintenance cost; f q Penalty cost for curtailing wind and solar power; f c is the CO2 related cost; f g is the comprehensive energy purchase cost; f b is the biogas digester production cost; T is the day-ahead system scheduling time, 24 hours; Ω is the set of all units in the wind-solar-biogas-electricity-heat-gas integrated energy system, Ω = {EB, GB, CHP, BT, P2G, CCS}, where EB is the electric boiler; GB is the gas boiler, CHP is the combined heat and power unit, BT is the biogas digester, P2G is the power-to-gas unit, and CCS is the carbon capture unit. is the startup cost of the i-th unit; is the operating status of the i-th unit in period t. When it is running, During outage is the operating status of the i-th unit in period t-1; is the shutdown cost of the i-th unit; is the maintenance cost of the i-th unit; P t i is the output power of the i-th unit during period t; C pen is the renewable energy penalty coefficient; is the wind power curtailment during period t; is the photovoltaic power reduction during period t; r em is the excess carbon emission factor; Q is the amount of CO2 that is not captured and emitted into the atmosphere during the carbon capture device during period t; em is the standard CO2 emission quota; r sto Cost coefficients for storing CO2 in a wind, solar, biogas, electricity, heat and gas integrated energy system; The amount of CO2 stored in the wind, solar, biomass, electricity, heat and gas integrated energy system; e,t is the electricity price during period t; The electricity purchased from the external market for the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; g,t is the gas price during period t; The amount of gas purchased from the external market by the wind, solar, biomass, electricity, heat and gas integrated energy system during period t; r bio,t is the cost coefficient of gas production in the biogas digester during period t; is the amount of biogas produced by the biogas digester during period t.

4. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 3 is characterized in that: The constraints include: combined heat and power unit constraints, gas boiler operation constraints, electric boiler operation constraints, electric energy storage constraints, thermal energy storage constraints, renewable energy unit constraints, grid power balance constraints, unit output power upper and lower limit constraints, unit ramping constraints, unit start and stop constraints, biogas operation constraints, power-to-gas device constraints and carbon capture output constraints.

5. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 4 is characterized in that: The cogeneration unit constraints include: in, is the output power of the cogeneration unit during period t; is the maximum output power of the cogeneration unit; k1, k2 and k3 are the operating parameters of the cogeneration unit; is the output thermal power of the cogeneration unit during period t; is the minimum output power of the cogeneration unit; η CHP is the heat-to-electricity conversion rate of the cogeneration unit; H CH4 is the calorific value of natural gas; V is the volume of natural gas consumed by the cogeneration unit during period t; m is the molar volume of the gas under standard conditions.

6. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 5 is characterized in that: The gas boiler operation constraints include: in, is the output thermal power of the gas boiler during period t; η GB The conversion efficiency of gas boiler; is the amount of natural gas consumed by the gas boiler during period t.

7. The coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system with carbon capture and power-to-gas according to claim 6 is characterized in that: The electric boiler operation constraints include: in, is the output thermal power of the electric boiler during period t; η EB is the energy conversion efficiency of the electric boiler; is the electric power consumed by the electric boiler during period t.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the coordinated optimization scheduling method for a wind, solar, biogas, electricity, heat and gas integrated energy system containing carbon capture and power-to-gas as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the coordinated optimization scheduling method of the wind-solar-biogas-electricity-heat-gas integrated energy system containing carbon capture and power-to-gas as described in any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the coordinated optimization scheduling method of the wind-solar-biogas-electricity-heat-gas integrated energy system containing carbon capture and power-to-gas as described in any one of claims 1-7.

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