Port multi-energy optimization scheduling system and control method considering carbon emission reduction incentive

Abstract

The application discloses a port multi-energy optimal scheduling system considering carbon emission reduction incentive and a control method thereof, and belongs to the technical field of energy system optimal scheduling. The system comprises an energy supply side, an energy conversion side, an energy storage side and an optimal scheduling module. The energy conversion side comprises a combined cooling, heat and power unit, a gas boiler, an electric refrigerator, an electrolytic cell and a hydrogen compressor. The energy storage side comprises a storage battery and a hydrogen storage tank. The optimal scheduling module constructs an optimal scheduling model with the minimum total operation cost as the target. The model contains the charge-discharge mutual exclusion constraint of the storage battery and the hydrogen storage tank, adopts the Big-M method to introduce binary variables for linearization processing, is converted into a mixed integer linear programming model and is solved, so that the optimal output plan of each device is obtained and scheduling control is performed. The application can effectively guide the port energy system to operate in a low-carbon direction, and realizes the collaborative optimization of economy and low carbon.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization scheduling technology, and in particular to a port multi-energy optimization scheduling system and its control method that takes into account carbon emission reduction incentives. Background Technology

[0002] As a core node in the global logistics chain, ports consume a large amount of energy, and the energy sources are diverse. Port areas are seeing extensive deployment of distributed photovoltaic and decentralized wind power, large-scale construction of shore power facilities to replace fuel-fired power generation for ship auxiliary machinery, and the gradual implementation of hydrogen energy systems such as electric hydrogen production, hydrogen compressors, and hydrogen storage tanks. Simultaneously, combined cooling, heating, and power (CCHP) units and energy storage power stations are being continuously integrated. Ports are evolving from single electricity load nodes into comprehensive energy systems that deeply integrate multiple energy forms such as electricity, heat, cooling, hydrogen, and gas.

[0003] Currently, several technical solutions for the optimized scheduling of integrated energy systems in ports have been publicly disclosed. For example, Qingdao Port's patented solution for a "multi-timescale scheduling method for integrated energy systems coupled with electricity" achieves coordinated scheduling of electricity and hydrogen energy by constructing an electric-hydrogen coupling subsystem model. Furthermore, some existing technologies propose multi-objective optimization-based scheduling methods for integrated energy systems in ports, solving the problem by setting multiple optimization objectives and using dynamic weighting coefficients. In addition, some solutions disclose multi-energy, multi-timescale optimization scheduling methods that consider carbon trading mechanisms, incorporating carbon emission costs into the optimization objective function.

[0004] However, the aforementioned existing technologies still have the following shortcomings: First, existing carbon trading mechanisms typically incorporate carbon emissions solely as a punitive cost in the objective function, meaning the more emissions, the higher the cost. While this "one-way penalty" model can curb high carbon emissions to some extent, it fails to fully reflect the positive incentive effect of emission reduction. Current technologies lack effective modeling methods to simultaneously transform carbon trading revenue and atmospheric pollutant emission tax reductions into incentive signals for system operation.

[0005] Secondly, the port's integrated energy system involves five energy forms: electricity, heat, cooling, hydrogen, and gas. These energy sources are deeply coupled through equipment such as combined cooling, heating, and power (CCHP), hydrogen electrolysis, gas-fired boilers, and electric chillers. Optimal scheduling of this system must simultaneously meet multiple energy balance constraints and equipment operating characteristic constraints. Most existing technologies only focus on electricity-heat coupling or electricity-hydrogen coupling; a mature solution has not yet been proposed for a complete coupled system encompassing all five energy forms and its coordinated scheduling methods.

[0006] Third, energy storage devices such as batteries and hydrogen storage tanks in port integrated energy systems exhibit nonlinear characteristics of mutual exclusion between charging and discharging, meaning that charging / hydrogen charging and discharging / hydrogen releasing cannot occur simultaneously within the same time period. Existing technologies often employ heuristic algorithms or simplified methods to address this nonlinear constraint, making it difficult to guarantee global optimality and computational efficiency. How to transform this nonlinear problem into an efficiently solvable mathematical form while accurately describing the physical characteristics of energy storage devices remains a technical challenge in this field.

[0007] Fourth, in port energy use scenarios, the consumption of auxiliary resources such as water for hydrogen compressors and water for electrolyzers has a real impact on system operating costs. Most existing technologies neglect detailed modeling of water purchase costs and hydrogen transportation costs, resulting in deviations between optimized scheduling results and actual operation. Summary of the Invention

[0008] The purpose of this invention is to provide a port multi-energy optimization scheduling system and its control method that takes into account carbon emission reduction incentives, so as to realize the unified scheduling optimization of the port's integrated energy system, effectively reduce system operating costs and carbon emissions while meeting the demand of multiple energy loads, and overcome the shortcomings of existing technologies such as carbon trading mechanisms being used only as punitive costs, incomplete energy coupling forms, and insufficient accuracy in handling nonlinear constraints of energy storage.

[0009] To achieve the above objectives, in a first aspect, the present invention provides a port multi-energy optimized scheduling system considering carbon emission reduction incentives, comprising: an energy supply side, an energy conversion side, an energy storage side, and an optimized scheduling module, wherein: The energy supply side is used to provide the system with electricity, natural gas, and water; The energy conversion side includes combined cooling, heating and power units, gas boilers, electric chillers, electrolyzers and hydrogen compressors, used to achieve the coupling conversion of multiple energy forms such as electricity, heat, cold, hydrogen and gas; Energy storage includes batteries and hydrogen storage tanks; The optimized scheduling module is used to obtain the electricity, heat, cooling, and hydrogen load data of the port at different times, as well as the output forecast data of wind power and photovoltaic power. The optimized scheduling model is constructed with the goal of minimizing the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, minus the emission reduction subsidy item consisting of carbon trading revenue and air pollutant emission tax exemption. The optimized scheduling model includes the mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks. The optimized scheduling module linearizes the charging and discharging mutual exclusion constraints and then solves the solution to obtain the optimal output plan of each device and performs scheduling control.

[0010] Secondly, the present invention also provides a port multi-energy optimal scheduling and control method considering carbon emission reduction incentives, comprising the following steps: S1. Obtain data on the port's power load, heat load, cooling load, and hydrogen energy load for each time period, as well as power output forecast data for wind power and photovoltaic power. S2. Construct an objective function with the goal of minimizing the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, and subtracts the emission reduction subsidy item consisting of carbon trading revenue and air pollutant emission tax exemption. S3. Construct constraints for the port's integrated energy system, including at least electrical power balance constraints, thermal power balance constraints, cold power balance constraints, hydrogen energy balance constraints, natural gas balance constraints, upper and lower limits of output of each piece of equipment, and mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks, to form an optimized scheduling model. S4. For the mutual exclusion constraint of filling and releasing, introduce binary variables and use the Big-M method for linearization to transform the optimization scheduling model into a mixed integer linear programming model. S5. Use a mixed-integer linear programming solver to solve the mixed-integer linear programming model, obtain the optimal output plan of each energy device in each time period, and carry out scheduling control based on the optimal output plan.

[0011] Therefore, the present invention employs the above-mentioned port multi-energy optimal scheduling system and control method considering carbon emission reduction incentives, which has the following beneficial effects: (1) A comprehensive energy synergistic optimization scheduling model covering five energy forms—electricity, heat, cooling, hydrogen, and gas—was established. This model comprehensively considers the operating characteristics and multi-energy coupling relationships of equipment such as combined cooling, heating, and power units, gas boilers, electric chillers, electrolyzers, hydrogen compressors, batteries, and hydrogen storage tanks, thereby achieving unified scheduling optimization of various equipment in the port's integrated energy system. Through the synergistic effect of electric power balance constraints, thermal power balance constraints, cooling power balance constraints, hydrogen energy balance constraints, and natural gas balance constraints, this model fully explores the complementary potential between various energy forms, overcoming the limitations of existing technologies that only consider the coupling of a single or a few energy forms. (2) An emission reduction subsidy term is introduced into the objective function. This emission reduction subsidy term consists of two parts: carbon trading revenue and air pollutant emission tax reduction. This transforms emission reduction behavior into a positive incentive for the total operating cost of the system. Compared with the "one-way punishment" mode in the prior art that only treats carbon emissions as a punitive cost, this invention provides a more effective economic driving force for the low-carbon operation of the port's multi-energy system by "rewarding emission reduction" rather than "punishing carbon emissions." (3) To address the nonlinear constraints of mutual exclusion between charging and discharging of batteries and hydrogen storage tanks, binary variables are introduced and linearization is performed using the Big-M method. This transforms the original nonlinear optimization problem into a mixed-integer linear programming model, which is then solved efficiently using a mature solver. This approach ensures both accurate description of the physical characteristics of the energy storage device and avoids the loss of solution accuracy caused by heuristic algorithms or simplification, thus ensuring the global optimality of the scheduling scheme. Furthermore, the equivalence simplification of the energy storage model in this invention makes the connection between the energy storage physical model and the linearized constraints clear and logically consistent. (4) Through 24-hour scheduling verification at a container port, the optimized scheduling scheme proposed in this invention, which considers emission reduction subsidies, reduced the total operating cost of the system by RMB 0.37 million and carbon emissions by 0.95 tons compared to the independent optimized scheduling without considering emission reduction incentives. Meanwhile, the scheduling results showed an energy structure optimization trend with a 19.1% decrease in power generation from combined cooling, heating, and power (CCHP) units and a 41.1% increase in electricity consumption from electric chillers. This indicates that the emission reduction subsidy mechanism can effectively guide the port's integrated energy system towards low-carbon operation, verifying the dual promoting effect of this invention in reducing operating costs and carbon emissions.

[0012] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the architecture of the port multi-energy optimized scheduling system that takes carbon emission reduction incentives into account in this invention; Figure 2 This is a flowchart illustrating the port multi-energy optimal scheduling and control method that takes carbon emission reduction incentives into account, as described in this invention. Figure 3 This is a comparison curve of the power purchase capacity of the power grid in two scenarios according to an embodiment of the present invention; Figure 4 This is a comparison curve of the power generation of the combined cooling, heating and power unit in two scenarios according to an embodiment of the present invention; Figure 5 This is a comparison curve of the power consumption of the electrolytic cell in two scenarios according to an embodiment of the present invention; Figure 6 This is a comparison curve of the power consumption of the electric chiller in two scenarios according to an embodiment of the present invention; Figure 7 This is a comparison curve of the power consumption of the hydrogen compressor in two scenarios according to an embodiment of the present invention; Figure 8 This is a comparison curve of the battery charging and discharging power in two scenarios according to an embodiment of the present invention (where positive values ​​represent charging and negative values ​​represent discharging). Figure 9This is a comparison curve of hydrogen production in the electrolyzer of this invention under two scenarios; Figure 10 This is a comparison curve of the hydrogen charging and discharging amount of the hydrogen storage tank in two scenarios according to an embodiment of the present invention (where positive values ​​represent hydrogen charging and negative values ​​represent hydrogen discharging). Figure 11 This is a comparison curve of the hydrogen processing capacity of the hydrogen compressor in two scenarios according to an embodiment of the present invention. Detailed Implementation

[0014] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example I. System Structure like Figure 1 As shown, this embodiment provides a port multi-energy optimized scheduling system considering carbon emission reduction incentives, including: an energy supply side, an energy conversion side, an energy storage side, and an optimized scheduling module, wherein: The energy supply side is used to provide the system with electricity, natural gas, and water; among them, electricity comes from the external power grid, natural gas comes from the natural gas grid, and water comes from water sources (such as municipal water supply).

[0017] The energy conversion side includes a combined cooling, heating and power (CCHP) unit, a gas-fired boiler, an electric chiller, an electrolyzer, and a hydrogen compressor, used to achieve the coupled conversion of multiple energy forms such as electricity, heat, cold, hydrogen, and gas. Specifically, the CCHP unit uses natural gas as fuel and simultaneously produces electricity, heat, and cold energy; the gas-fired boiler burns natural gas to generate heat energy, which serves as a heat source supplement; the electric chiller consumes electricity to generate cold energy; the electrolyzer consumes electricity to produce hydrogen; and the hydrogen compressor pressurizes the low-pressure hydrogen produced by the electrolyzer to meet the pressure requirements of hydrogen storage or hydrogen load.

[0018] The energy storage side includes batteries and hydrogen storage tanks, which are used to store electrical energy and hydrogen energy respectively, enabling time-shifting of energy. The optimized scheduling module is used to acquire electricity, heat, cooling, and hydrogen load data of the port at different times, as well as output forecast data of wind power and photovoltaic power (e.g., through historical data forecasting or real-time acquisition). The optimized scheduling model is constructed with the objective function of minimizing the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, minus the emission reduction subsidy item consisting of carbon trading revenue and air pollutant emission tax exemption. The optimized scheduling model includes the mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks. The optimized scheduling module linearizes the charging and discharging mutual exclusion constraints and then solves the solution to obtain the optimal output plan of each device and performs scheduling control.

[0019] II. Detailed Mathematical Description of the Optimized Scheduling Model 1. Objective function The objective function is: ; in, Indicates time period index ( , (Total number of time periods within the scheduling cycle) The total energy purchase cost within the dispatch cycle is determined based on the time-of-use electricity price and the unit price of natural gas. The total cost of purchasing water during the scheduling cycle is determined based on the water consumption and water price of the electrolyzer and hydrogen compressor. This represents the total maintenance cost of all equipment within the scheduling period. This represents the total aging cost of energy storage equipment during the scheduling cycle. This represents the total cost of transporting hydrogen during the scheduling period. The environmental cost during the scheduling cycle consists of the cost of treating CO2 emissions generated from electricity and gas purchases. This refers to emission reduction subsidies during the scheduling cycle.

[0020] Specifically, the formulas for calculating each cost are as follows: (1) Total cost of energy purchase Calculated using the following formula: ; in, for Time-of-use electricity price (unit: yuan / kWh); for Power purchased from external power grid during the time period (unit: MW); Price per cubic meter of natural gas (unit: yuan / m³) 3 ); for Gas purchase capacity from the natural gas network during the time period (unit: m³) 3 / h); This formula indicates that the cost of energy purchase consists of the sum of the cost of electricity and the cost of gas.

[0021] (2) Total cost of purchasing water The calculation formula is: ; in, Water price (yuan / m³) 3 ), The water consumption coefficient for the electrolytic cell (t / kg) Water consumption coefficient (t / kg) for hydrogen compressors. Electrolytic cell Hydrogen production capacity (kg / h) during the time period. Hydrogen compressor Hydrogen production capacity (kg / h) during the specified time period. Water purchase costs are primarily incurred during the electrolyzer hydrogen production process and the cooling or washing process of the hydrogen compressor.

[0022] (3) Total equipment operation and maintenance cost The calculation formula is: ; in, Indicates the device type. These correspond to wind power equipment, photovoltaic equipment, combined cooling, heating and power units, gas boilers, storage batteries, electric chillers, electrolyzers, hydrogen storage tanks, and hydrogen compressors, respectively. Represents energy form, These correspond to electricity, natural gas, heat, hydrogen, and cold energy, respectively. The operation and maintenance cost coefficient for each piece of equipment (the unit varies depending on the equipment, such as yuan / MWh or yuan / kg). For equipment exist The corresponding energy power consumed or generated during a given period.

[0023] (4) Total cost of energy storage aging The calculation formula is: ; in, and The unit aging costs (RMB / MWh and RMB / kg) are for batteries and hydrogen storage tanks, respectively. and Residual batteries Charging power (MW) and discharging power (MW) during the time period. and Hydrogen storage tanks Hydrogen charging power (kg / h) and hydrogen discharging power (kg / h) during the time period. , These refer to the charging efficiency and discharging efficiency of the battery, respectively. , These represent the hydrogen filling efficiency and hydrogen discharging efficiency of the hydrogen storage tank, respectively. Aging cost reflects the reduction in the lifespan of the energy storage device caused by the charging / discharging / hydrogen charging / discharging process.

[0024] (5) Total cost of hydrogen transportation The calculation formula is: ; in, The unit price for hydrogen transportation is (yuan / kg). For the port Hydrogen load per hour (kg / h). This cost covers the transportation of hydrogen from hydrogen production or storage sites to hydrogen-using terminals.

[0025] (6) Environmental costs Calculated using the following formula: ; in, Cost of CO2 emission treatment (RMB / tCO2e). The CO2 emission factor for electricity purchase (tCO2e / MWh, taken as 0.2004 in this example) is used. The CO2 emission factor of natural gas (tCO2e / m³) 3 In this embodiment, the value is 0.00196. Environmental cost represents the cost of controlling carbon emissions generated from the purchase of electricity and gas.

[0026] (7) Emission reduction subsidies Calculated using the following formula: ; in, The price is the carbon trading price (yuan / tCO2e). and These represent the reduction in purchased electricity and gas capacity relative to the unincentivized baseline under carbon emission reduction incentives (units are MW and m³, respectively). 3 / h). It should be noted that this benchmark is usually taken as the optimized power and gas purchase capacity for each time period without considering emission reduction subsidies, therefore and A non-negative value indicates the amount of externally purchased energy reduced after the introduction of emission reduction subsidies. and SO x and NO x Emission tax (yuan / kg) and SO emissions from burning natural gas x and NO xEmission coefficient (kg / m³) 3 ), and This is an equivalent factor (dimensionless, used to convert emissions into taxable equivalents). This emissions reduction subsidy consists of two parts: the first part is the carbon trading revenue corresponding to the carbon emissions saved by reducing electricity and gas purchases (i.e., proceeds from selling excess carbon emission allowances); the second part is the SO2 reduction resulting from reducing natural gas consumption. x and NO x Emissions tax reductions corresponding to emissions. Together, these constitute a positive economic incentive for the system to reduce emissions.

[0027] 2. Energy balance constraint The optimized scheduling model also includes the following energy balance constraints to ensure real-time balance between supply and demand for each form of energy.

[0028] Hydrogen energy balance constraints: ; in, Hydrogen compressor Hydrogen production power during the time period (kg / h); and Hydrogen storage tanks Hydrogen release power and hydrogen charging power (kg / h) for each time period, with hydrogen release being positive and hydrogen charging being negative; For the port The hydrogen load for a given period (kg / h) is expressed as follows: This formula represents the sum of hydrogen production from the compressor and hydrogen discharge from the storage tank, minus the amount of hydrogen added to the storage tank, which precisely meets the hydrogen load requirement.

[0029] Electric power balance constraints: ; in, , , These include wind power equipment, photovoltaic equipment, and combined cooling, heating and power (CCHP) units. Power generation (MW) during the time period; for Power purchased from the external power grid during the specified time period (MW); and Residual batteries The discharge power (MW) and charging power (MW) of a time period, with discharge being positive and charging being negative; , , These are an electric chiller, an electrolytic cell, and a hydrogen compressor. Power consumption (MW) during the time period; For the port The power load (MW) for a given period is expressed as the sum of all power outputs (wind power, photovoltaic, combined cooling, heating and power, purchased electricity, and battery discharge) minus the consumption of electrical equipment (electric chillers, electrolyzers, hydrogen compressors, and battery charging), which equals the power load.

[0030] Thermal power balance constraint: ; in, For combined cooling, heating and power units Heat production capacity (MW) for a given period; Gas-fired boiler Heat production capacity (MW) for a given period; For the port Heat load (MW) for a given period; this formula indicates that the sum of the heat generated by the combined cooling, heating and power unit and the gas-fired boiler meets the heat load.

[0031] Cold power balance constraints: ; in, For combined cooling, heating and power units Cooling power (MW) for a given time period; For electric refrigeration Cooling power (MW) for a given time period; For the port Cooling load (MW) for a given period; this formula indicates that the combined cooling, heating, and power (CCHP) unit and the electric chiller together meet the cooling load.

[0032] Natural gas balance constraints: ; in, and These are combined cooling, heating and power units and gas-fired boilers. Natural gas power consumed during a period of time (m 3 / h); for The time period is from the gas purchase capacity (m) of the natural gas network 3 / h). This formula indicates that all purchased natural gas is used for combined cooling, heating and power units and gas-fired boilers.

[0033] 3. Inequality constraints (operational boundary constraints) To ensure the safe and stable operation of the system, the optimized scheduling model also includes the following inequality constraints: Power purchase constraints: ; in, and These are the minimum and maximum power purchase amounts (MW), respectively, which are limited by the grid connection line capacity or contractual agreements; Gas purchase power constraints: ; in, and These are the minimum and maximum values ​​of the gas purchasing power (m). 3 / h), limited by the capacity of the gas supply network; Output constraints for each piece of equipment: ; in, Indicates the device type. Represents energy form, , ; Indicates wind power equipment, Indicates photovoltaic equipment, This refers to a combined cooling, heating and power (CCHP) unit. Indicates a gas-fired boiler. Indicates storage battery. Indicates an electric refrigeration unit. Indicates an electrolytic cell. Indicates hydrogen storage tank, Indicates a hydrogen compressor; Indicates electrical energy. Indicates natural gas. Indicates thermal energy. It represents hydrogen energy. Indicates cold energy; and These are the minimum and maximum values ​​of energy consumed or generated by each device, respectively. Ramp-up rate constraints for combined cooling, heating and power units and gas-fired boilers: ; in, This indicates a combined cooling, heating and power (CCHP) unit or a gas-fired boiler. and These represent the minimum and maximum ramp rates (MW / min) for combined cooling, heating and power (CCHP) units and gas-fired boilers, respectively. The scheduling time interval is 15 minutes (15 hours in this embodiment, i.e., 0.25 hours); this constraint limits the output variation between adjacent time periods to no more than the equipment's ramp-up capability.

[0034] Energy storage capacity constraints: ; in, and These are the minimum and maximum values ​​(MWh) of the energy stored in the battery, respectively. and These are the minimum and maximum values ​​(kg) of energy that the hydrogen storage tank can store; this constraint ensures that the energy storage device will not be overcharged or over-discharged.

[0035] Energy storage start and end state constraints: ; in, and The batteries and hydrogen storage tanks are respectively located at the end of the dispatch cycle. The remaining energy; and These represent the initial energy of the batteries and the hydrogen storage tanks, respectively. This constraint ensures that the energy of the energy storage equipment does not experience a net change during the dispatch cycle, thus maintaining long-term operational sustainability.

[0036] 4. Output Model of Key Energy Equipment The output models for combined cooling, heating and power units, gas-fired boilers, electric chillers, electrolyzers, and hydrogen compressors are as follows.

[0037] Combined cooling, heating and power (CCHP) units: ; ; ; in, , , These are the power generation efficiency, heat generation efficiency, and coefficient of performance (COP) of the combined cooling, heating, and power (CCHP) unit, respectively, which are taken as 0.30, 0.40, and 0.70 in this embodiment. The calorific value of natural gas (MWh / m³) 3 (In this embodiment, the value is 0.0098). Let be the proportion of waste heat used for heating (dimensionless, taken as 0.5 in this embodiment), then The above three equations represent the proportion used for cooling; they indicate that a combined cooling, heating, and power (CCHP) unit consumes natural gas and simultaneously produces electricity, heat, and cooling energy at a fixed efficiency, where the ratio of heat to cooling is determined by the waste heat distribution coefficient. Decide.

[0038] Gas-fired boilers: ; in, Let be the heat production efficiency of the gas-fired boiler (dimensionless, taken as 0.90 in this embodiment). This formula indicates that the gas-fired boiler consumes natural gas to produce heat, serving as a supplementary heat source when the heat production of the combined cooling, heating, and power unit is insufficient.

[0039] Electric refrigeration unit: ; in, Let be the coefficient of performance (COP) of the electric chiller (dimensionless, taken as 6.0 in this embodiment), representing the cooling capacity generated per unit of electricity consumption. This formula indicates that the electric chiller consumes electricity to generate cooling energy, which, together with the cooling capacity of the combined cooling, heating, and power (CCHP) unit, meets the cooling load.

[0040] Electrolytic cell: ; in, The hydrogen production efficiency of the electrolyzer (dimensionless, taken as 1.005 in this embodiment); The value is the calorific value of hydrogen (MWh / kg, taken as 0.0397 in this example). This formula represents the electricity consumed by the electrolyzer and the efficiency with which electrical energy is converted into hydrogen energy.

[0041] Hydrogen compressor: ; ; in, The loss coefficient during hydrogen compression (dimensionless, taken as 0.01 in this embodiment) represents a 1% hydrogen loss during compression. The power consumption coefficient of the hydrogen compressor (dimensionless, taken as 0.005 in this embodiment) represents the power consumption required to compress a unit mass of hydrogen. The first equation represents the amount of high-pressure hydrogen output after the low-pressure hydrogen produced by the electrolyzer is pressurized by the compressor, after deducting losses; the second equation represents the power consumed by the compressor during the pressurization process.

[0042] 5. Energy Storage Model The energy storage model for batteries is as follows: ; in, for Battery storage capacity at the end of the period (MWh); The self-discharge rate of the battery (dimensionless, taken as 0.00001 in this embodiment, i.e. 0.001%) represents the proportion of energy storage reduced due to internal losses per unit time period. and These are the charging efficiency and discharging efficiency of the battery (dimensionless, both taken as 0.9 in this embodiment). and They are respectively Linearized variables of the charging power (MW) and discharging power (MW) of the battery over a period of time; The scheduling time interval (h) is represented by this formula, which means that the stored energy at the end of the current time period is equal to the remaining amount after deducting self-discharge from the stored energy at the end of the previous time period, plus the charging amount minus the discharging amount (where the discharging amount is converted into the actual release amount according to the discharging efficiency).

[0043] The energy storage model for the hydrogen storage tank is as follows: ; in, for The amount of hydrogen remaining in the hydrogen storage tank at the end of the period (kg). The self-dissipation rate of the hydrogen storage tank (dimensionless, taken as 0.001 in this embodiment) represents the loss of hydrogen due to leakage or natural dissipation. and These are the hydrogen filling efficiency and hydrogen discharging efficiency of the hydrogen storage tank (dimensionless, taken as 0.95 and 0.9 respectively in this embodiment). and They are respectively The hydrogen charging power (kg / h) and hydrogen discharging power (kg / h) of the time-limited hydrogen storage tank are linearized variables. The physical meaning of this formula is the same as that of the battery energy storage model.

[0044] 6. Fill-and-release mutual exclusion constraints and their linearization processing In actual operation, batteries and hydrogen storage tanks cannot be simultaneously in a charging (hydrogen charging) and discharging (hydrogen discharging) state; otherwise, an internal circulating current will form, causing energy loss and equipment damage. Therefore, the mutual exclusion constraint for battery charging and discharging is: ; in, and Residual batteries Discharge power and charging power (MW) during the time period; The mutual exclusion constraint for filling and discharging hydrogen storage tanks is as follows: ; in, and Hydrogen storage tanks Hydrogen release power and hydrogen charging power (kg / h) during the time period; The above constraints are nonlinear product constraints, which cannot be directly handled in linear programming. Therefore, this invention introduces binary variables. and And linearization transformation is performed on the large M constant, where, , , Indicates the battery charging status. Indicates the discharge state; This indicates the hydrogen storage tank's hydrogen filling status. Indicates a hydrogen-releasing state; The linearized constraints of the charge-discharge mutual exclusion constraint of the battery using the Big-M method are as follows: ; ; ; ; ; ; in, and These are the linearized variables (MW) of the battery charging power and discharging power, respectively. and For a large M constant; the logic of the above constraint is: when At that time, the first form allows There is a positive upper limit, while the fourth form is mandatory. At the same time, the second and third moves forced The sixth move forces Conversely, when At that time, the first form was forced The fourth form allows There is a positive upper limit; the fifth and sixth forms force... This achieves a mutually exclusive effect where charging and discharging power are not simultaneously non-zero.

[0045] The mutual exclusion constraints for filling and discharging hydrogen storage tanks are linearized using the same Big-M method.

[0046] III. Optimize the solution process This embodiment also provides a port multi-energy optimal scheduling and control method considering carbon emission reduction incentives, which is applied to the aforementioned system. For example... Figure 2 As shown, the method specifically includes the following steps: S1. Obtain data on the port's power load, heat load, cooling load, and hydrogen energy load for each time period, as well as power output forecasts for wind power and photovoltaic power.

[0047] In this step, the optimized scheduling module first reads the forecast data for the scheduling cycle from the port energy management system (EMS) or historical database. Since wind and solar power output is subject to uncertainty due to weather conditions, this embodiment employs a short-term forecasting method based on numerical weather prediction (NWP) to obtain 15-minute intervals (i.e., scheduling time intervals) for the next 24 hours. Wind power output forecast (hours) And photovoltaic power output forecast At the same time, the power load is predicted based on port operation plans (such as container cranes, refrigerated container yards, and shore power access for ships). Based on the heating, ventilation, and domestic hot water demand forecasts in the port area, the heat load is predicted. Predict the cooling load based on the cooling demand of cold storage and air conditioning. Based on hydrogen refueling plans for hydrogen-powered vehicles and industrial hydrogen consumption plans, the hydrogen energy load is predicted. All data are organized in time series format and used as input parameters for the optimization model.

[0048] S2. Construct an objective function that minimizes the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, minus the emission reduction subsidy consisting of carbon trading revenue and air pollutant emission tax exemption.

[0049] S3. Construct constraints for the port's integrated energy system, including at least electrical power balance constraints, thermal power balance constraints, cold power balance constraints, hydrogen energy balance constraints, natural gas balance constraints, upper and lower limits of output of each equipment, and mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks, to form an optimized scheduling model.

[0050] S4. For the mutual exclusion constraint of filling and releasing, introduce binary variables and use the Big-M method for linearization to transform the optimization scheduling model into a mixed integer linear programming model.

[0051] S5. Use a mixed-integer linear programming solver to solve the mixed-integer linear programming model, obtain the optimal output plan of each energy device in each time period, and carry out scheduling control based on the optimal output plan.

[0052] In this step, the optimization scheduling module calls the open-source CBC mixed-integer linear programming solver (or commercial solvers such as Gurobi and CPLEX) to solve the MILP model transformed from S4. The solver searches for the global optimal solution that minimizes the objective function within the feasible region using a combination of branch and bound and cutting planes. After solving, the optimal values ​​of each decision variable are extracted, including the power purchased from the grid, the gas purchased from the natural gas grid, the power / heat / cooling output of the combined cooling, heating and power (CCHP) units, the heat output of the gas boiler, the cooling output of the electric chiller, the power consumption and hydrogen production of the electrolyzer, the power consumption and hydrogen production of the hydrogen compressor, the charging and discharging power of the battery and the charging and discharging power of the hydrogen storage tank, and the state of charge of the energy storage equipment. The optimization scheduling module converts these optimal output plans into operating instructions for each device (such as power setpoints, start / stop signals, etc.), and sends them to the local controllers via industrial Ethernet or fieldbus to achieve closed-loop scheduling control of the port's integrated energy system.

[0053] IV. Specific Implementation Examples and Simulation Verification The effectiveness of the system proposed in this invention is verified through a specific embodiment. This embodiment uses a container port as an example, setting the scheduling cycle to 24 hours and the total number of time periods... Time interval Minutes (0.25 hours).

[0054] 1. Input data and parameters The port's power load, heat load, cooling load, and hydrogen load curves for different time periods are obtained from historical data (specific curve data is omitted here; their trends conform to port operation patterns, such as higher loads during the day and lower loads at night). Wind power and solar power output forecasts are generated based on local meteorological conditions and installed capacity.

[0055] The main simulation parameters are shown in Table 1. The power grid adopts a time-of-use pricing mechanism: peak price is 1.35 yuan / kWh (8:30-11:30, 18:00-23:00), off-peak price is 0.90 yuan / kWh (7:00-8:30, 11:30-18:00), and off-peak price is 0.47 yuan / kWh (23:00-7:00). The natural gas price is 2.70 yuan / m³. 3 The carbon trading price is 82 yuan / tCO2e, SO x and NO x The emission tax is 2.5 yuan / kg. Other equipment parameters, including efficiency, cost coefficient, ramp rate, and capacity limits, are listed in Table 1.

[0056] Table 1 Parameter Table

[0057] 2. Comparison of scene settings To verify the effectiveness of emission reduction subsidies, this invention sets up two comparative scenarios: Scenario 1 (without considering emission reduction incentives): The optimization objective does not include emission reduction subsidies. , that is to say Independent optimization scheduling is performed with the goal of minimizing total operating costs.

[0058] Scenario 2 (Considering emission reduction incentives): The optimization objective includes a complete emission reduction subsidy item. That is, by subtracting carbon trading revenue and emission tax exemptions from the objective function, the system is guided to reduce the purchase of high-carbon energy (natural gas).

[0059] Both scenarios use the same load data, equipment parameters, and electricity and gas prices. The only difference is whether the objective function includes an emission reduction subsidy item.

[0060] 3. Simulation Results and Analysis The CBC solver was used to optimize and solve the two scenarios separately, obtaining the output plans of each device at each time period. For ease of comparison, Figures 3 to 11 Comparison curves were plotted for the following parameters under two scenarios: power purchased from the grid, power generated by the combined cooling, heating and power (CCHP) unit, power consumed by the electrolyzer, power consumed by the electric chiller, power consumed by the hydrogen compressor, power charged and discharged by the battery (where positive values ​​indicate charging and negative values ​​indicate discharging), hydrogen production by the electrolyzer, hydrogen filling and discharging by the hydrogen storage tank (where positive values ​​indicate filling and negative values ​​indicate discharging), and hydrogen processing capacity of the hydrogen compressor.

[0061] Table 2 summarizes the main optimization results for the two scenarios.

[0062] Table 2 Partial Simulation Results

[0063] comprehensive Figures 3 to 11 Based on the results in Table 2, the following analysis can be performed: (1) Overall Cost and Carbon Emission Analysis. The total operating cost of Scenario 2 is RMB 319,700, which is RMB 3,700 lower than that of Scenario 1 (RMB 323,400). At the same time, the CO2 emissions of Scenario 2 are 57.09 tons, which is 0.95 tons lower than that of Scenario 1 (58.04 tons), a decrease of about 1.64%. The emission reduction subsidy itself is RMB 3,700, which means that the system directly converts the carbon trading revenue and emission tax exemption obtained by reducing the purchase of electricity and gas into an equivalent reduction in total cost, verifying the dual promotion effect of the emission reduction subsidy on the low-carbon economic operation of the port—both reducing economic costs and reducing carbon emissions.

[0064] (2) Analysis of Equipment Output Changes. The power generation of the combined cooling, heating, and power (CCHP) unit in Scenario 2 is 13.44 MW·h, a decrease of 19.1% compared to 16.61 MW·h in Scenario 1. This is because emission reduction subsidies provide positive incentives for carbon emissions and air pollutant emissions from natural gas consumption (i.e., reducing emissions yields benefits), and optimized scheduling tends to reduce the natural gas consumption of the CCHP unit, thereby reducing its power generation output. Correspondingly, to make up for the cooling load gap, the power consumption of the electric chiller increases to 2.11 MW·h in Scenario 2, an increase of 41.1% compared to 1.50 MW·h in Scenario 1. This increased power demand is supplemented by grid power purchases, so the grid power purchase in Scenario 2 is 224.98 MW·h, an increase of 1.7% compared to 221.33 MW·h in Scenario 1.

[0065] (3) Analysis of energy storage dispatch behavior. From Figure 8 (Comparison of battery charging and discharging power) It can be seen that the battery charging and discharging strategies did not show significant differences in the two scenarios. Emissions reduction subsidies have a relatively small impact on energy storage behavior because the subsidies mainly target the total amount of electricity and gas purchased, and do not directly change the relative relationship of time-of-use electricity prices. From... Figure 9 and Figure 10It can be seen that hydrogen energy dispatch (including hydrogen production from electrolyzers and hydrogen filling and discharging from hydrogen storage tanks) is basically the same in both scenarios. This is mainly because the hydrogen load is relatively stable, and the adjustment capacity of hydrogen storage tanks is limited, so the marginal incentive effect of emission reduction subsidies on the hydrogen energy side is not obvious. Figure 11 It can be seen that the hydrogen compressor's processing capacity did not show a significant difference, because its operation mainly follows the hydrogen production rhythm of the electrolyzer.

[0066] (4) Economic Mechanism Analysis. Emission Reduction Subsidies In the calculation, the carbon trading price is 82 yuan / tCO2e and SO2. x / NO x The emission tax of 2.5 yuan / kg together constitutes a signal to inhibit natural gas consumption. In Scenario 2, due to the reduction in natural gas purchases (reflected in a decrease in combined cooling, heating, and power (CCHP) power generation), the system gained 3,700 yuan in carbon trading revenue and emission tax exemption. This figure exactly equals the reduction in total cost, while changes in other costs (such as electricity purchase costs and operation and maintenance costs) are basically offset. This indicates that emission reduction subsidies can effectively internalize external environmental costs, guiding the system to prioritize low-carbon energy while meeting load demand, achieving a synergy between economic efficiency and low carbon emissions. Compared to the existing "one-way penalty" model that only treats carbon emissions as a punitive cost, this mechanism provides a more effective economic driver for the low-carbon operation of the port's multi-energy system by "rewarding emission reduction" rather than "punishing carbon emissions."

[0067] Therefore, the present invention employs the above-mentioned port multi-energy optimization scheduling system and control method that considers carbon emission reduction incentives, and has the following technical effects: (1) Construct a collaborative optimization model covering five energy sources: electricity, heat, cold, hydrogen, and gas, and achieve unified scheduling of port comprehensive energy through multi-energy complementarity; (2) Introduce an emission reduction subsidy term consisting of carbon trading revenue and emission tax reduction into the objective function to transform emission reduction behavior into positive economic incentives and effectively guide the system to operate in a low-carbon manner; (3) For the nonlinear constraint of mutual exclusion between energy storage charging and discharging, the Big-M method is used to linearize it and then transform it into a MILP model, which takes into account both the solution accuracy and efficiency. (4) After 24 hours of simulation verification at a port, considering the emission reduction subsidy, the total system cost was reduced by RMB 0.37 million, carbon emissions were reduced by 0.95 tons, combined cooling, heating and power generation decreased by 19.1%, and electricity consumption for electric cooling increased by 41.1%, achieving a synergistic improvement in economic efficiency and low carbon emissions.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A port multi-energy optimal scheduling system considering carbon emission reduction incentives, characterized in that, include: The energy supply side, energy conversion side, energy storage side, and optimization scheduling module include: The energy supply side is used to provide the system with electricity, natural gas, and water; The energy conversion side includes combined cooling, heating and power units, gas boilers, electric chillers, electrolyzers and hydrogen compressors, used to achieve the coupling conversion of multiple energy forms such as electricity, heat, cold, hydrogen and gas; Energy storage includes batteries and hydrogen storage tanks; The optimized scheduling module is used to obtain the electricity, heat, cooling, and hydrogen load data of the port at different times, as well as the output forecast data of wind power and photovoltaic power. The optimized scheduling model is constructed with the goal of minimizing the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, minus the emission reduction subsidy item consisting of carbon trading revenue and air pollutant emission tax exemption. The optimized scheduling model includes the mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks. The optimized scheduling module linearizes the charging and discharging mutual exclusion constraints and then solves the solution to obtain the optimal output plan of each device and performs scheduling control.

2. The system according to claim 1, characterized in that, The objective function is: ； in, Indicates time period index, The total energy purchase cost within the dispatch cycle is determined based on the time-of-use electricity price and the unit price of natural gas. The total cost of purchasing water during the scheduling cycle is determined based on the water consumption and water price of the electrolyzer and hydrogen compressor. This represents the total maintenance cost of all equipment within the scheduling period. This represents the total aging cost of energy storage equipment during the scheduling cycle. This represents the total cost of transporting hydrogen during the scheduling period. The environmental cost during the scheduling cycle consists of the cost of treating CO2 emissions generated from electricity and gas purchases. This refers to emission reduction subsidies during the scheduling cycle.

3. The system according to claim 2, characterized in that, The total cost of energy purchase Environmental costs and emission reduction subsidies Calculate using the following formulas respectively: ； ； ； in, This represents the total number of time periods within the scheduling cycle. for Time-of-use electricity pricing for different time periods; for The amount of electricity purchased from the external power grid during a given period; This refers to the unit price of natural gas. for The time period for purchasing gas from the natural gas network; Cost of CO2 emission treatment; The CO2 emission factor for electricity purchase; This represents the CO2 emission coefficient of natural gas. For carbon trading prices; and These represent the reduction in electricity and gas purchase capacity relative to the unincentivized baseline under carbon emission reduction incentives, respectively. and They are respectively and Emissions tax; and Emissions from burning natural gas and The emission coefficient; and This is the equivalent coefficient.

4. The system according to claim 3, characterized in that, The optimized scheduling model also includes the following energy balance constraints: Hydrogen energy balance constraints: ； in, Hydrogen compressor Hydrogen production capacity during a given time period; and Hydrogen storage tanks Hydrogen release power and hydrogen charging power during the same period; For the port Hydrogen load during the period; Electric power balance constraints: ； in, , , These include wind power equipment, photovoltaic equipment, and combined cooling, heating and power (CCHP) units. Power generation during a given time period; for The amount of electricity purchased from the external power grid during a given period; and Residual batteries Discharge power and charging power during the time period; , , These are an electric chiller, an electrolytic cell, and a hydrogen compressor. Power consumption during a given time period; For the port Electricity load during a given time period; Thermal power balance constraint: ； in, For combined cooling, heating and power units Heat production capacity during a given time period; Gas-fired boiler Heat production capacity during a given time period; For the port Heat load during a given period; Cold power balance constraints: ； in, For combined cooling, heating and power units Cooling power during the time period; For electric refrigeration Cooling power during the time period; For the port Cooling load during a given period; Natural gas balance constraints: ； in, and These are combined cooling, heating and power units and gas-fired boilers. The amount of natural gas consumed during a given period; for The time period is determined by the gas purchase capacity from the natural gas network.

5. The system according to claim 4, characterized in that, The optimized scheduling model also includes the following inequality constraints: Power purchase constraints: ； in, and These are the minimum and maximum values ​​of the purchased electricity capacity, respectively. Gas purchase power constraints: ； in, and These are the minimum and maximum values ​​of the gas purchasing power, respectively; Output constraints for each piece of equipment: ； in, Indicates the device type. Represents energy form, , ; Indicates wind power equipment, Indicates photovoltaic equipment, This refers to a combined cooling, heating and power (CCHP) unit. Indicates a gas-fired boiler. Indicates storage battery. Indicates an electric refrigeration unit. Indicates an electrolytic cell. Indicates hydrogen storage tank, Indicates a hydrogen compressor; Indicates electrical energy. Indicates natural gas. Indicates thermal energy. It represents hydrogen energy. Indicates cold energy; and These are the minimum and maximum values ​​of energy consumed or generated by each device, respectively. Ramp-up rate constraints for combined cooling, heating and power units and gas-fired boilers: ； in, This indicates a combined cooling, heating and power (CCHP) unit or a gas-fired boiler. and These are the minimum and maximum ramp rates for combined cooling, heating and power (CCHP) units and gas-fired boilers, respectively. The scheduling time interval; Energy storage capacity constraints: ； in, and These are the minimum and maximum values ​​of the energy stored in the battery, respectively. and These represent the minimum and maximum values ​​of energy stored in the hydrogen storage tank, respectively. Energy storage start and end state constraints: ； in, and These represent the remaining energy of the battery and the hydrogen storage tank at the end of the scheduling cycle, respectively. and These are the initial energies of the battery and the hydrogen storage tank, respectively.

6. The system according to claim 5, characterized in that, The output models for the combined cooling, heating and power unit, gas boiler, electric chiller, electrolyzer, and hydrogen compressor are as follows: Combined cooling, heating and power (CCHP) units: ； ； ； in, , , These are the power generation efficiency, heat generation efficiency, and coefficient of performance (COP) of a combined cooling, heating, and power (CCHP) unit, respectively. The calorific value of natural gas; This refers to the proportion of waste heat used for heating. Gas-fired boilers: ； in, The heat production efficiency of a gas-fired boiler; Electric refrigeration unit: ； in, The coefficient of performance (COP) of the electric refrigeration unit; Electrolytic cell: ； in, The hydrogen production efficiency of the electrolyzer; This refers to the calorific value of hydrogen. Hydrogen compressor: ； ； in, This is the loss coefficient during the hydrogen compression process; This represents the power consumption coefficient of the hydrogen compressor.

7. The system according to claim 1, characterized in that, The energy storage model of the battery is as follows: ； in, for The amount of energy stored in the battery at the end of the period; This refers to the self-discharge rate of the battery. and These are the charging efficiency and discharging efficiency of the battery, respectively. and They are respectively Linearized variables of the charging and discharging power of the battery over a given period; The scheduling time interval; The energy storage model of the hydrogen storage tank is as follows: ； in, for The amount of hydrogen remaining in the hydrogen storage tank at the end of the period; The self-dissipation rate of the hydrogen storage tank; and These are the hydrogen filling efficiency and hydrogen discharging efficiency of the hydrogen storage tank, respectively. and They are respectively Linearized variables for hydrogen filling and discharging power of hydrogen storage tanks during different time periods.

8. The system according to claim 7, characterized in that, The charging and discharging mutual exclusion constraint of the battery is as follows: ； in, and Residual batteries Discharge power and charging power during the time period; The mutual exclusion constraint for filling and discharging the hydrogen storage tank is as follows: ； in, and Hydrogen storage tanks Hydrogen release power and hydrogen charging power during the same period; Introducing binary variables and And linearization transformation is performed on the large M constant, where, , , Indicates the battery charging status. Indicates the discharge state; This indicates the hydrogen storage tank's hydrogen filling status. Indicates a hydrogen-releasing state; The charging and discharging mutual exclusion constraint of the battery, after being linearized using the Big-M method, is as follows: ； ； ； ； ； ； in, and These are the linearized variables for battery charging power and discharging power, respectively. and It is a large M constant; The mutual exclusion constraints for filling and discharging of the hydrogen storage tank are linearized using the same Big-M method.

9. A port multi-energy optimal scheduling and control method considering carbon emission reduction incentives, applied to the system according to any one of claims 1 to 8, characterized in that, Includes the following steps: S1. Obtain data on the port's power load, heat load, cooling load, and hydrogen energy load for each time period, as well as power output forecast data for wind power and photovoltaic power. S2. Construct an objective function with the goal of minimizing the total operating cost within the scheduling cycle. The total operating cost includes energy purchase cost, water purchase cost, equipment operation and maintenance cost, energy storage aging cost, hydrogen transportation cost, and environmental cost, and subtracts the emission reduction subsidy item consisting of carbon trading revenue and air pollutant emission tax exemption. S3. Construct constraints for the port's integrated energy system, including at least electrical power balance constraints, thermal power balance constraints, cold power balance constraints, hydrogen energy balance constraints, natural gas balance constraints, upper and lower limits of output of each piece of equipment, and mutual exclusion constraints of charging and discharging of batteries and hydrogen storage tanks, to form an optimized scheduling model. S4. For the mutual exclusion constraint of filling and releasing, introduce binary variables and use the Big-M method for linearization to transform the optimization scheduling model into a mixed integer linear programming model. S5. Use a mixed-integer linear programming solver to solve the mixed-integer linear programming model, obtain the optimal output plan of each energy device in each time period, and carry out scheduling control based on the optimal output plan.

10. The port multi-energy optimal scheduling and control method considering carbon emission reduction incentives according to claim 9, characterized in that, The constraint conditions of the charge-discharge mutual exclusion constraint after linearization using the Big-M method are as follows: ； ； ； ； ； ； in, and These are the linearized variables for battery charging power and discharging power, respectively. and It is a large M constant; The mutual exclusion constraints for filling and discharging of the hydrogen storage tank are linearized using the same Big-M method.

Images