Island energy system multi-agent collaborative optimization scheduling method and system

Abstract

The application belongs to the technical field of island energy system optimization, and specifically discloses a kind of island energy system multi-agent collaborative optimization scheduling method and system, method includes: the establishment of integrated electricity-heat-cold-gas-carbon-hydrogen-ammonia-ol whole industry chain island virtual power plant high robust system model;Respectively corresponding energy producer carbon trading cost model, energy producer carbon capture income model and operator carbon penalty cost model of purchase electricity;Define the carbon emission level factor of electricity, heat and cold, respectively calculate the total amount of reward and punishment in the t period after load side two-way carbon response and the total amount of one-way incentive in the t period of source side;Build multi-agent collaborative optimization objective function;Solve the objective function, get the optimal energy production plan, energy storage scheduling and load transfer strategy.The application realizes real-time guidance and accurate inhibition of high carbon emission behavior through the two-way response mechanism facing the load side and the one-way incentive mechanism facing the source side, reduces system carbon emission.

Description

Technical Field

[0001] This invention relates to the field of island energy system optimization technology, and in particular to a multi-entity collaborative optimization scheduling method and system for island energy systems. Background Technology

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

[0003] In the development of deep-sea energy, new energy sources, primarily offshore wind power, exhibit significant random fluctuations and anti-peak-shaving characteristics, posing a dual challenge to traditional energy systems: severe nighttime wind curtailment and high carbon emissions during peak load periods. Furthermore, the isolated geographical location of islands imposes stringent requirements on system regulation capabilities, necessitating robust systems.

[0004] Currently, hydrogen energy, with its superior energy density and flexibility in cross-sectoral decarbonization, is widely recognized as an indispensable strategic pillar in the energy transition. Island energy systems are evolving towards deep integration across the entire industrial chain, encompassing electricity, heat, cooling, gas, carbon, and hydrogen. Virtual power plants, as mature platforms for aggregating distributed resources and coordinating conflicts of interest among multiple stakeholders, have become an important means of supporting the green development of the marine economy.

[0005] At the physical modeling level, existing island multi-energy complementary system frameworks typically only include individual energy sources such as electricity, heat, gas, and hydrogen. They lack deep coupling modeling that places the entire industrial chain of electricity-heat-cold-gas-carbon and hydrogen-ammonia-alcohol under a unified framework, resulting in poor system robustness. Furthermore, existing technologies generally use "normalized equivalent power" to simplify heterogeneous energy sources, leading to low modeling accuracy.

[0006] At the scheduling decision-making level, existing scheduling methods mostly focus on single-objective control, which is difficult to cope with the complexity of the interests of multiple stakeholders in the entire industry chain. In addition, for high-dimensional nonlinear game models, existing solution methods still face the dual challenges of low optimization efficiency and convergence difficulty under complex physical constraints.

[0007] At the level of low-carbon guidance, current low-carbon scheduling schemes mainly rely on tiered carbon trading or static carbon tax mechanisms based on total emission control, and the emission reduction responsibility is mostly anchored to a single entity on the supply side, lacking a closed-loop allocation logic of shared responsibility between sources and loads. At the same time, existing strategies have failed to establish a dynamic mapping logic between instantaneous carbon emissions and reward / penalty signals. Summary of the Invention

[0008] To address the aforementioned issues, this invention proposes a multi-entity collaborative optimization scheduling method and system for island energy systems. It constructs a deeply coupled model integrating the entire industrial chain of electricity, heat, cooling, gas, carbon, hydrogen, ammonia, and alcohol, and establishes a multi-entity collaborative optimization scheduling framework based on master-slave game theory. This framework precisely coordinates the complex balance of interests among multiple energy system operators, energy producers, energy storage operators, and end-users. Through an improved tiered carbon guidance mechanism based on shared responsibility among multiple entities and a two-sided dynamic reward and punishment mechanism based on carbon emission level factors, it clarifies the division of carbon emission responsibilities and establishes a dynamic mapping logic between instantaneous carbon emissions and carbon reward and punishment signals, thereby achieving real-time and precise suppression of high-carbon emission behaviors.

[0009] In some implementations, the following technical solutions are adopted:

[0010] A multi-agent collaborative optimization scheduling method for an island energy system includes:

[0011] Considering the power balance and quality balance of the energy network, a virtual power plant system model for an island integrating the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol is established.

[0012] Using the island virtual power plant system model, the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions from electricity purchases by multi-energy system operators are calculated respectively, and then the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchases by operators are obtained respectively.

[0013] Based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle, carbon emission level factors for electricity, heat, and cooling are defined respectively. Based on the carbon emission level factors, the total amount of rewards and penalties in time period t after the load-side bidirectional carbon response and the total amount of unidirectional incentives in time period t on the source side are calculated respectively.

[0014] With multi-energy system operators as leaders, energy producers, energy storage operators, and end users as followers, and with the goal of maximizing their own profits or minimizing their energy costs, a multi-entity collaborative optimization objective function is constructed.

[0015] Solving the objective function yields the optimal energy production plan, energy storage scheduling, and load transfer strategy.

[0016] In other embodiments, a multi-agent collaborative optimization scheduling system for an island energy system is disclosed, comprising:

[0017] The virtual power plant model building module is used to consider the power balance and quality balance of the energy network and establish an integrated island virtual power plant system model that integrates the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol.

[0018] The carbon trading guidance module is used to calculate the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions of electricity purchased by multi-energy system operators using the island virtual power plant system model, and then obtain the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchased by operators.

[0019] The carbon response module is used to define carbon emission level factors for electricity, heat, and cold based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle. Based on the carbon emission level factors, it calculates the total reward and penalty amount within time period t after the load-side bidirectional carbon response and the total unidirectional incentive amount within time period t on the source side.

[0020] The objective function construction module is used to construct a multi-entity collaborative optimization objective function with multi-energy system operators as leaders, energy producers, energy storage operators and end users as followers, and the goal of maximizing their own profits or minimizing energy costs.

[0021] The objective solution module is used to solve the objective function to obtain the optimal energy production plan, energy storage scheduling and load transfer strategy.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] (1) This invention constructs a deeply coupled model integrating the entire industrial chain of electricity-heat-cooling-gas-carbon and hydrogen-ammonia-alcohol. By incorporating the resource recycling path of hydrogen-based industries, the consumption boundary of marine new energy is expanded, while improving the robustness of the system; by differentiating the power flow and mass flow, the physical representation accuracy of scheduling decisions is significantly improved.

[0024] (2) This invention establishes a hierarchical interaction logic with multi-energy system operators as leaders and energy producers, energy storage operators, and end users as followers, deconstructing the global optimization objective into an autonomous decision-making process for each subject. On this basis, the differential evolution algorithm is used to perform a global search for the upper-level multi-dimensional energy trading scheme, and a mathematical programming solver is used to handle the lower-level complex physical equilibrium and constraint scheduling, effectively solving the optimization problem of high-dimensional nonlinear games under large-scale complex constraints, and ensuring the rapid convergence of the game equilibrium point.

[0025] (3) This invention proposes a tiered carbon guidance strategy with shared responsibility among multiple entities. By deeply coupling carbon quota trading for energy producers, carbon capture incentives for energy producers, and carbon penalties for electricity purchases by multiple energy system operators, environmental responsibility is precisely allocated to each entity, realizing the refined allocation of environmental responsibility among entities within the system. This breaks the traditional one-way carbon management model. This "source-load sharing" mechanism can effectively guide end users to actively adjust their energy consumption habits, forcing energy structure optimization from the consumption end, and realizing emission reduction response across the entire system chain.

[0026] (4) This invention introduces carbon emission level factors for electricity, heat, and cold, quantifies the carbon emission status of the system in real time, and establishes a dynamic mapping logic between carbon emission fluctuations and multi-energy reward and punishment signals. Through a two-way response mechanism oriented towards the load side and a one-way incentive mechanism oriented towards the source side, it realizes real-time guidance and precise suppression of high carbon emission behavior, thereby reducing system carbon emissions.

[0027] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the island virtual power plant system model architecture that integrates the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol in an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of the multi-entity collaborative optimization scheduling method for an island energy system in an embodiment of the present invention;

[0030] Figure 3 This is a flowchart illustrating the process of solving the objective function for multi-agent collaborative optimization in an embodiment of the present invention.

[0031] Figure 4 This is a schematic diagram of power balance under power grid interruption in an embodiment of the present invention;

[0032] Figure 5 This is a schematic diagram of thermal power balance under power grid interruption in an embodiment of the present invention;

[0033] Figure 6 This is a schematic diagram of cooling power balance under power grid interruption in an embodiment of the present invention;

[0034] Figure 7 This is a schematic diagram of power balance under natural gas supply interruption in an embodiment of the present invention;

[0035] Figure 8 This is a schematic diagram of heat power balance under natural gas supply interruption in an embodiment of the present invention;

[0036] Figure 9This is a schematic diagram of cooling power balance under natural gas supply interruption in an embodiment of the present invention. Detailed Implementation

[0037] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0039] Example 1

[0040] In one or more embodiments, a multi-agent collaborative optimization scheduling method for island energy systems is disclosed, combining... Figure 2 Specifically, it includes the following process:

[0041] S101: Considering the power balance and quality balance of the energy network, establish a virtual power plant system model for an island that integrates the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol.

[0042] Figure 1 The system showcases a virtual power plant model for an island based on electricity, heat, cooling, gas, carbon, and hydrogen, as well as a trading framework for a virtual power plant at sea. It uses a multi-energy system operator as the dispatch center and integrates an improved tiered carbon guidance mechanism with a dynamic response mechanism based on carbon emission fluctuations on both the source and load sides to achieve macro-level guidance on the system's low-carbon and economic efficiency. Figure 1 The text details the complex interaction between energy flow and information: energy producers, as followers, have spearheaded a comprehensive industrial chain encompassing island wind and solar power generation, electrolytic hydrogen production, combined cooling, heating and power (CCHP), ice storage and air conditioning, and methanol and ammonia synthesis systems coupled with CCUS technology, achieving physical integration of the entire industry chain from electricity to heat to cooling to gas to carbon to hydrogen; energy storage operations are equipped with batteries, thermal storage devices, cold storage devices, and hydrogen-based industrial storage facilities (including hydrogen, ammonia, alcohol, and carbon dioxide storage equipment), enabling the spatiotemporal transfer and secure supply of energy; the user side includes diverse energy demands such as electricity, heat, cooling loads, hydrogen-powered vehicles, and maritime shipping, achieving demand-side management by responding to price signals.

[0043] The model is set as follows: electricity, heat, and cooling are measured in power (kW); hydrogen, methanol, ammonia, and carbon dioxide are measured in mass (kg).

[0044] The specific models for each part are explained below:

[0045] (1) New energy output model:

[0046] The output models for wind and solar power are based on meteorological data predictions, and their actual absorption capacity is limited by the maximum predicted value. ;

[0047] In the formula, To contribute to renewable energy during the t-th time period; This represents the upper limit of renewable energy output in the t-th time period.

[0048] (2) Load demand response model:

[0049] Considering load demand response, the amount of load transferred by users and the adjusted load must satisfy the shift balancing constraint:

[0050] ;

[0051] In the formula, e, h, and c represent electrical, thermal, and cold energy sources, respectively; This represents the amount of load transferred during the t-th time period; and These are the upper and lower limits of the load transfer amount within the t-th time period; This indicates the initial electrical and heating cooling load. This indicates the amount of electrical, heating, and cooling loads after demand response.

[0052] (3) Purchase of electricity from external power grid:

[0053] The interaction between the system and the upper-level power grid is limited by the transmission line capacity.

[0054] ;

[0055] In the formula, and These represent the purchased and sold electricity power in the t-th time period, respectively. It is a 0-1 state variable to prevent simultaneous purchase and sale of electricity; , Using binary variables ensures that electricity purchase and sale cannot occur simultaneously. This indicates the power limit of the power grid transmission line.

[0056] (4) Gas purchase from oil and gas platforms:

[0057] Natural gas, as the system's primary energy input, is limited by the physical transport capacity of the pipelines.

[0058] ;

[0059] ;

[0060] In the formula, The mass of natural gas purchased from the oil and gas platform during the t-th time period; and These represent the quality of natural gas used by the combined cooling, heating and power unit and the gas turbine during the t-th time period; This is the maximum transport capacity limit for the pipeline.

[0061] (5) Electric-heat-cold energy storage system

[0062] The state evolution and operational constraints of batteries, thermal storage tanks, and cold storage tanks can be described using a general mathematical model:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] in, e, h, and c represent electricity, heat, and cold, respectively. , , Let be the maximum and minimum values ​​of the energy storage capacity and storage capacity of the energy storage device for energy type i during time period t; This represents the self-loss rate of the energy storage system; , These represent the charging and discharging power of the energy storage device during time period t, respectively. , These represent charge and discharge efficiencies, respectively. , These represent the charging and discharging states, and are binary variables. , These represent the upper limits of the charging and discharging power of the energy storage device, respectively.

[0069] (6) General model of energy conversion equipment:

[0070] Its operation must meet the constraints of conversion efficiency, capacity limit, and ramp rate:

[0071] ;

[0072] ;

[0073] ;

[0074] in, , , These represent the output power, maximum value, and minimum value of the i-th energy source of the energy conversion device during the t-th time period; This represents the input power of the i'th energy source in the t-th time period of the energy conversion device; This represents the energy conversion efficiency of the equipment; , The lower and upper limits of ramp-up constraints for energy conversion equipment.

[0075] (7) Electrolytic cell:

[0076] As an energy conversion interface connecting the power system and the hydrogen energy industry chain, it utilizes electrochemical principles to convert excess renewable energy electricity into hydrogen. Its operating model is described as follows:

[0077] ;

[0078] ;

[0079] In the formula, The energy consumption coefficient (kWh / kg) for producing hydrogen is given. and These are the upper and lower limits of the electrolytic cell power, respectively. The electrical energy consumed in producing hydrogen. To determine the quality of hydrogen produced.

[0080] (8) Gas-fired mixed hydrogen cooling-heating-power cogeneration unit model (CCHP):

[0081] Construct a combined cooling, heating and power system integrating a gas turbine, a waste heat boiler, and an absorption chiller. This system allows for the mixing and combustion of a certain proportion of green hydrogen into natural gas.

[0082] ;

[0083] ;

[0084] ;

[0085] In the formula, The ratio of fuel gas to hydrogen. and Let be the mass of hydrogen and the mass of methane input to the unit during the t-th time period, respectively. , , These are the lower heating values ​​of hydrogen, methane, and the mixed gas, respectively.

[0086] The mixed fuels are burned in a gas turbine to generate electricity, and the waste heat from the high-temperature flue gas produced by the gas turbine is recovered for heating and cooling.

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, This represents the total output power of the CCHP. , and These represent the electrical, thermal, and cooling power output by the CCHP during time period t, respectively. , and These represent the electrical, thermal, and cooling power outputs of the CCHP, respectively. The exhaust waste heat of GT during time period t; This is the heat dissipation loss coefficient.

[0093] The output of a CCHP must meet capacity constraints, and its power regulation rate must be limited by the ramp rate.

[0094] ;

[0095] In the formula, and They are respectively The upper and lower limits; and These represent the upper and lower limits of the CCHP ramp rate.

[0096] (9) Hydrogen fuel cell (HFC)

[0097] Hydrogen fuel cells, as a novel energy conversion device, can efficiently integrate the power grid, heating network, and hydrogen network. The model is as follows:

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] In the formula, The hydrogen power input into the HFC during time period t; , These represent the electrical and thermal power output of the hydrogen fuel cell during time period t, respectively. The energy conversion efficiency of hydrogen fuel cells; , These are the upper and lower limits of the adjustable range of the electrical and thermal ratio in hydrogen fuel cells, respectively. , These represent the upper and lower limits of the output power of hydrogen fuel cells; , These are the upper and lower limits of the ramp rate for hydrogen fuel cells; Because of the low calorific value of hydrogen, The mass of hydrogen consumed by the fuel cell.

[0104] (10) Methanol synthesis system:

[0105] The system utilizes hydrogen and captured carbon dioxide to synthesize methanol. The chemical reaction equations involved in this process are as follows:

[0106] ;

[0107] The mass of carbon dioxide produced by combined cooling, heating and power units and gas-fired boilers burning natural gas is as follows:

[0108] ;

[0109] In the formula, Carbon emission factor of natural gas; The carbon dioxide produced by burning natural gas in a CCHP (Combined Cooling, Heating and Power) plant. The carbon dioxide produced by burning natural gas in GB (gas-fired boilers) For the quality of natural gas burned by CCHP, For the quality of natural gas burned in GB, The total carbon emissions generated from burning natural gas.

[0110] The aforementioned carbon emissions are captured using a carbon capture system.

[0111] ;

[0112] ;

[0113] In the formula, and These represent the mass of carbon dioxide obtained by the carbon capture device and the direct carbon capture, respectively, during the time period t. It refers to CCUS carbon capture efficiency.

[0114] The amount of hydrogen and carbon dioxide required for methanol synthesis is linearly related to the methanol yield.

[0115] ;

[0116] In the formula, and These are the mass conversion coefficients for hydrogen and carbon dioxide required to produce methanol, respectively. , and These are the mass conversion coefficients of methanol produced during time period t and the mass of hydrogen and carbon dioxide required to produce methanol, respectively.

[0117] Accordingly, the energy consumption model for each device is expressed as follows:

[0118] ;

[0119] ;

[0120] ;

[0121] In the formula, , , These are the unit energy consumption coefficients (kWh / kg) for carbon capture, direct carbon capture, and methanol synthesis processes, respectively. , and These represent the electrical energy required for the carbon capture device, direct carbon capture of carbon dioxide, and methanol production during time period t.

[0122] (11) Ammonia synthesis system:

[0123] The system utilizes hydrogen produced by water electrolysis and nitrogen obtained from air separation to synthesize ammonia. The chemical reaction equation for this process is as follows: ;

[0124] Based on the law of conservation of mass in chemical reactions, the amount of hydrogen and nitrogen required to synthesize one unit mass of ammonia is calculated as follows:

[0125] ;

[0126] In the formula, and These are the mass conversion coefficients of hydrogen and nitrogen required to produce ammonia during time period t, respectively. , and These represent the mass of ammonia produced during time period t and the masses of hydrogen and nitrogen used in producing ammonia, respectively.

[0127] The energy consumption model for the ammonia synthesis process is constructed as follows: ;

[0128] In the formula, and These are the unit energy consumption coefficients for ammonia synthesis and nitrogen production processes, respectively. and These represent the electrical energy required for the carbon capture device and the direct carbon capture of carbon dioxide during time period t, respectively. This indicates the mass of nitrogen required to produce ammonia.

[0129] (12) Supporting storage systems for hydrogen-based industries:

[0130] Its state changes are based on mass balance, and the constraints include upper and lower capacity limits and the restriction that charging and discharging cannot occur simultaneously:

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] in, H represents hydrogen, OH represents methanol, and NH3 represents ammonia.

[0137] , , These represent the storage capacity and maximum and minimum values ​​of the storage equipment for substance j in time period t. This represents the self-loss rate of the energy storage system; , These represent the charging and discharging power of the energy storage device during time period t, respectively. , These represent charge and discharge efficiencies, respectively. , These represent the charging and discharging states, and are binary variables. , These represent the upper limits of the charging and discharging capacity of energy storage devices, respectively.

[0138] (13) Power balance of energy network:

[0139] The system must meet the real-time power balance of multiple energy flows, including electricity, cooling, and heating, within any scheduling period t.

[0140] ① Power balance:

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] in, , These are wind power and photovoltaic power generation, respectively. , , , , , These are CCHP power generation, HFC power generation, electricity purchased from the grid, electricity load after demand response, electricity consumption for ice storage air conditioning, and electricity sold to the grid. , These refer to charging and discharging the battery, respectively.

[0146] ②Cold power balance: ;

[0147] in, , , These are CCHP refrigeration, ice storage air conditioning refrigeration, and cooling load after demand response; , These are respectively used to charge and release cold energy into the cold storage device.

[0148] ③ Thermal power balance:

[0149] ;

[0150] ;

[0151] in, , , These are the heat generated by CCHP, the heat generated by GB, and the heat load after demand response, respectively. , These are respectively for charging and releasing heat energy into the thermal storage device.

[0152] (14) Energy network quality balance:

[0153] Within any time period t, the total supply of all substances in the system must be strictly equal to the total demand.

[0154] Hydrogen mass balance: ;

[0155] in, , , , , , These are the total hydrogen production, hydrogen load, hydrogen mass required for methanol production, hydrogen mass required for ammonia production, hydrogen mass used in CCHP, and hydrogen mass used in HFC. , These refer to the mass of hydrogen being charged and discharged from the hydrogen storage device, respectively.

[0156] Methanol mass balance: ;

[0157] in, , These are the total methanol production volume and methanol fuel load, respectively. , The figures represent the mass of methanol charged and discharged from the methanol storage unit, respectively.

[0158] Ammonia mass balance: ;

[0159] in, , These are the total ammonia production and ammonia load, respectively. , These refer to the ammonia charging and discharging mass of the ammonia storage device.

[0160] Carbon dioxide mass balance: ;

[0161] in, , , These represent the carbon dioxide captured by the carbon capture device after the combustion of natural gas, the carbon dioxide captured by the direct carbon capture device from the air, and the mass of carbon dioxide required to produce methanol, respectively. , These represent the mass of carbon dioxide added and released, respectively.

[0162] This embodiment constructs a deeply coupled model integrating the entire industrial chain of electricity, heat, cooling, gas, carbon, hydrogen, ammonia, and alcohol, incorporating the resource recycling path of the hydrogen-based industry, thus expanding the consumption boundary of marine new energy sources and improving the robustness of the system. By differentially representing power flow and mass flow, the accuracy of the physical representation of scheduling decisions is significantly improved.

[0163] S102: Using the island virtual power plant system model, calculate the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions from electricity purchases by multi-energy system operators, and then obtain the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchases by operators.

[0164] To guide various stakeholders in effectively reducing carbon emissions, this embodiment constructs a multi-dimensional carbon guidance framework that includes carbon quota trading for energy producers, carbon capture incentives for energy producers, and carbon penalties for electricity purchases by multi-energy system operators; the details are as follows:

[0165] (1) Carbon allowance trading volume for energy producers:

[0166] Regulatory authorities first determine the carbon emission allowances of energy producers based on their production capacity levels.

[0167] Energy producers' carbon allowances depend on the actual amount of electricity and heat they generate, calculated as follows:

[0168] ;

[0169] in, and These are the carbon quota coefficients for the output of a unit of electrical energy and thermal energy, respectively. The heat energy generated by the gas-fired boiler.

[0170] The total amount of carbon dioxide actually produced by the system burning natural gas is:

[0171] ;

[0172] This allows us to determine the net tiered carbon trading volume for energy producers in conventional production processes. for:

[0173] .

[0174] (2) Carbon capture by energy producers:

[0175] To incentivize energy producers to invest in carbon capture technology, this embodiment directly treats the captured carbon dioxide as a negative emissions asset and incorporates it into the carbon trading system for incentive purposes. The final approved trading amount equals the total captured volume:

[0176] ;

[0177] in, This represents the total amount of carbon dioxide captured. , These are carbon capture devices that capture carbon dioxide produced after the combustion of natural gas, and direct carbon capture devices that capture carbon dioxide from the air.

[0178] (3) Carbon emissions from electricity purchases by multi-energy system operators:

[0179] To encourage multi-energy system operators to reduce their reliance on high-carbon upstream power grids, the model imposes a full penalty on carbon emissions generated from purchasing electricity from the grid. The resulting carbon emissions are expressed as:

[0180] ;

[0181] in, Carbon emissions from purchasing electricity from the grid, As a carbon emission factor of the power grid, Electricity purchased from the power grid.

[0182] Net carbon emissions based on the above calculations The corresponding carbon trading costs or benefits The following is a description using a piecewise function:

[0183] ;

[0184] in, This refers to the carbon trading costs for energy producers, the carbon capture revenue for energy producers, or the carbon penalty costs for electricity purchases by operators. This refers to carbon allowances for energy producers, carbon capture by energy producers, or carbon emissions from electricity purchases by multi-energy system operators. This indicates the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, or the carbon emissions from electricity purchases by multi-energy system operators. This indicates the corresponding carbon trading benchmark price; This indicates the width of the stepped interval in the corresponding model; This is the price growth rate in the positive carbon zone, used to penalize high carbon emissions; it is a set value, for example, it can be set to 0.25. This is the compensation increase rate in the negative carbon range, used to incentivize deep emission reduction behavior. It is a set value, for example, it can be set to 0.25.

[0185] This embodiment of the bilateral symmetric tiered carbon guidance model employs a symmetrical tiered structure, which not only imposes progressively increasing penalties on emissions exceeding quotas but also progressively increasing rewards on emissions reductions below quotas. This symmetry enhances the incentive for system participants to pursue maximum emissions reductions, helps stimulate deeper emissions reduction efforts within the system, optimizes source-load configuration, and makes emissions reduction activities more economically viable.

[0186] This embodiment's bilateral symmetric tiered carbon guidance model breaks away from the traditional one-way carbon management model by assigning carbon emission responsibility to both the energy supply and demand sides. This "source-load shared responsibility" mechanism can effectively guide end-users to proactively adjust their energy consumption habits, forcing energy structure optimization from the consumption side and achieving a full-chain emission reduction response.

[0187] S103: Based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle, define carbon emission level factors for electricity, heat, and cooling respectively. Based on the carbon emission level factors, calculate the total reward and penalty amount within time period t after the load-side bidirectional carbon response and the total reward and penalty amount within time period t on the source side respectively.

[0188] To address the limitations of traditional carbon trading strategies in reflecting real-time environmental costs and guiding the real-time response of system flexibility resources, this embodiment proposes a precise low-carbon control method based on carbon emission level factors. This method follows a closed-loop logic of "quantification-assessment-guidance": first, real-time carbon emissions are quantified based on the multi-energy flow coupling characteristics; then, level factors characterizing the system's emission status are defined; and finally, a two-way response mechanism for end users and an incentive mechanism for energy producers are constructed.

[0189] Specifically, in integrated energy systems, the production processes of heterogeneous energy sources such as electricity, heat, and cooling exhibit strong coupling characteristics. To accurately track carbon footprints, this embodiment introduces a carbon emission allocation factor. This is used to achieve precise decoupling and quantification of carbon emission responsibility in multi-energy coupling links, and to rationally allocate total carbon emissions among different energy products.

[0190] Real-time carbon emissions of electricity, heat, and cold at time t , , Specifically, it is expressed as follows:

[0191] ;

[0192] ;

[0193] in, , , These represent the proportions of carbon emissions from CCHP (CCHP) power generation, heat production, and cooling to the total carbon dioxide emissions from natural gas combustion. , These represent the proportion of carbon emissions generated from purchasing electricity from the grid to meet the system's electricity and cooling needs, respectively. Carbon emission factors from thermal power plants; The carbon dioxide produced by burning natural gas in a CCHP (Combined Cooling, Heating and Power) plant. The carbon dioxide produced by burning natural gas in GB (gas-fired boilers) This indicates that multi-energy system operators purchase electricity from external power grids.

[0194] To effectively analyze the changes in carbon emissions and clarify the benchmarks for carbon penalties and incentives, this embodiment defines a carbon emission level factor. i = e, h, or c. This indicator is calculated by measuring real-time carbon emissions relative to the average carbon emissions during the scheduling period. The deviation is used to quantify the current emission level of the system. Characterizing high carbon emission levels, Characterizing low carbon emission levels:

[0195] ;

[0196] in, , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the real-time carbon emissions for electricity, heat, and cooling, respectively. , and These represent the average carbon emissions for electricity, heat, and cooling during the scheduling cycle.

[0197] Based on this level factor, multi-energy system operators have developed differentiated source-load dual-side precise low-carbon control methods to achieve synergistic optimization of economy and low carbon emissions.

[0198] Regarding the bidirectional carbon response on the load side:

[0199] To fully tap the potential of demand-side response, multi-energy system operators have introduced dynamic unit power incentive and penalty adjustments. This mechanism exhibits bidirectional adjustment characteristics: during periods of high emissions, the unit power penalty is increased to suppress unnecessary loads, while during periods of low emissions, the unit power incentive is provided to guide load shifting.

[0200] ;

[0201] In the formula, The load-side response coefficient is defined, and the interval constraint on the right side of the formula ensures that the reward / penalty per unit power is within the acceptable range for the user. Based on this, the final reward / penalty amount for the user side within time period t is determined. Updated to:

[0202] ;

[0203] Energy storage operators' final reward and penalty amount within time period t Updated to:

[0204] ;

[0205] Therefore, the total amount of rewards and penalties within time period t after the bidirectional carbon response on the load side (user side) for:

[0206] ;

[0207] Therefore, the total amount of rewards and penalties for energy storage operators within time period t after the two-way carbon response. for:

[0208] ;

[0209] in, and These represent the total amount of rewards and penalties for both the user side and the energy storage operator within time period t after the two-way carbon response. , , These represent the reward / penalty amounts for electricity, heat, and cooling within time period t on the user's side. , , These represent the reward and penalty amounts for electricity, heat, and cooling for energy storage operators within time period t, respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the response coefficients for electricity, heat, and cold on the load side, respectively. , , These are the unit power rewards and penalties for electricity, heat, and cooling on the load side, respectively. , These represent the upper and lower limits of the reward / penalty amount per unit power of electricity. , These represent the upper and lower limits of the reward / penalty amount per unit power of heat. , These are the upper and lower limits of the reward / penalty amount per unit power for cold applications; , and These are the electrical load, thermal load, and cooling load after demand response; To charge the energy storage, For thermal energy storage and heat storage, It is used for cold energy storage.

[0210] Regarding source-side unidirectional carbon excitation:

[0211] For energy producers, multi-energy system operators implement a one-way asymmetric incentive mechanism based on positive carbon emission levels. The core objective of this mechanism is to specifically incentivize energy producers to increase unit output during periods of high carbon emissions, thereby diluting the overall carbon emissions of the system. Firstly, the incentive per unit power of source-side capacity... The design is as follows:

[0212] ;

[0213] In the formula, the model introduces The operator acts as a one-way filter, only when the system is in a high emission level region. The stimulus is triggered at the time.

[0214] Accordingly, the total incentive amount per unit power for energy producers during time period t. Represented as:

[0215] ;

[0216] Therefore, the total incentive amount for energy producers during period t for:

[0217] ;

[0218] Correspondingly, the total amount of unit power incentive during time period t Represented as:

[0219] ;

[0220] Therefore, the total incentive amount for energy storage operators during period t for:

[0221] ;

[0222] in, and These represent the total incentive amount within time period t after the one-way incentives are given to energy producers and energy storage operators, respectively. , , These represent the incentive amounts for electricity, heat, and cooling provided by energy producers during time period t. , , These represent the incentive amounts for electricity, heat, and cooling for the energy storage operator during time period t, respectively. , , The unit power excitation quantity of source-side electrical, heat, and cooling capacity. , , These are the excitation coefficients for the source side (electric, thermal, and cold), respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the upper limits of the unit power incentive for source-side electrical, thermal, and cooling production capacity, respectively.

[0223] This embodiment introduces a carbon emission level factor, which calculates the difference between "real-time carbon emissions" and "cycle-average carbon emissions," transforming the abstract emission reduction target into a physical indicator with time-scale characteristics.

[0224] Carbon emissions are typically invisible to users. The carbon emission level factor directly maps carbon emissions to the total reward and penalty amount, transforming complex carbon reduction targets into intuitive reward and penalty signals, thus reducing the complexity of system management. It can constrain the supply side and guide the demand side to achieve overall system optimization. When the overall carbon emissions of the system are high, the carbon emission level factor increases, the energy cost for users rises, and load shifting occurs. At the same time, it incentivizes production units to increase output, reducing the system's purchase of external electricity and achieving system carbon reduction. Furthermore, the hydrogen-ammonia-methanol value chain unique to this embodiment is extremely sensitive to the carbon emission level factor. When CELF is high, it will also prioritize hydrogen fuel cells, a zero-carbon or low-carbon emission production method.

[0225] This mechanism ensures that prices remain at the benchmark during periods of low emissions, thereby effectively maintaining the dynamic equilibrium of interests among the various participants within the game framework while guaranteeing reasonable profit margins for multi-energy system operators. For example, if multi-energy system operators increase the cost of purchasing electricity, heat, and cooling energy, it incentivizes energy producers to increase output during peak load periods, reducing the cost of purchasing electricity from the grid to offset this additional expenditure and protect their profits. Because the unilateral increase in energy purchase costs increases the revenue of energy producers, it ensures the dynamic equilibrium of interests among the participants and ultimately controls the fluctuation of economic benefits of each follower to within 5%.

[0226] S104: With multi-energy system operators as leaders, energy producers, energy storage operators and end users as followers, and with the goal of maximizing their own profits or minimizing their energy costs, construct a multi-entity collaborative optimization objective function.

[0227] In this embodiment, a two-layer optimization scheduling framework based on Stackelberg game theory is constructed to address the differences in the interests of multiple stakeholders in the island integrated energy system. This architecture decomposes the global low-carbon optimization goal of the system into the autonomous decision-making process of each participating stakeholder through a "price-guided-response feedback" mechanism.

[0228] The leader is a multi-energy system operator who acts as the dispatch hub for "energy and carbon signals." Based on the real-time unit output plans of energy producers and the real-time carbon emission status of the entire system, the operator issues prices for purchasing energy from energy producers and energy storage operators (energy release) and for selling energy to users and energy storage operators (energy charging).

[0229] Followers include energy producers, energy storage operators, and end users. Each entity, based on energy trading prices and considering the benefits or costs of carbon rewards and penalties, pursues the maximization of its own interests by optimizing production plans, energy storage scheduling, and load shifting.

[0230] The objective function for multi-agent collaborative optimization includes:

[0231] (1) Objective function of multi-energy system operator:

[0232] As the leader at the top of the game, it guides lower-level energy producers, energy storage operators, and end users to adjust their behavior by issuing trading prices for various energy sources, thereby maximizing its own profits.

[0233] ;

[0234] in, This indicates the revenue that operators receive from selling energy to users:

[0235] ;

[0236] In the formula, , These represent hydrogen, methanol, and ammonia, respectively. Prices for electricity, heat, and cooling energy transactions. For the electricity, heat, and cooling loads after demand response, The trading prices for hydrogen, methanol, and ammonia. For hydrogen, methanol and ammonia loads, The scheduling interval.

[0237] This represents the revenue generated by operators from selling energy to energy storage operators:

[0238] ;

[0239] In the formula: , These represent the charging of the energy storage device;

[0240] This indicates the cost of purchasing energy from energy producers:

[0241] ;

[0242] In the formula, , These represent the energy prices purchased from energy producers, respectively. P represents electricity, heat, and cold energy, with the unit being power. m represents hydrogen, methanol, and ammonia, with the unit being kg.

[0243] This represents the cost of interaction between the operator and the power grid:

[0244] ;

[0245] in, and These are binary variables used to ensure that the amount of electricity purchased and sold differs. The price at which it is purchased from the power grid. Electricity purchased by the operator from the grid. The price at which it is sold to the power grid. Electricity sold to the grid by operators.

[0246] Compensation for peak shaving by operators:

[0247] ;

[0248] In the formula, and Adjust the unit price of power per unit. , , These represent the transfer amounts of electricity, heat, and cooling loads, respectively.

[0249] This indicates the carbon penalty cost for operators purchasing electricity. and These represent the total amount of rewards and penalties for both the user side and the energy storage operator during time period t after the two-way carbon response; and These represent the total incentive amount within time period t after one-way incentives are given to energy producers and energy storage operators, respectively.

[0250] Cost of leasing energy storage capacity: ;

[0251] In the formula, The proportion of leasing costs borne by multi-energy system operators. and The rental price is per unit capacity. and It is an adjustable capacity, which is 80% of the rated capacity.

[0252] (2) Objective function of energy producers:

[0253] As followers in the game, energy producers respond to energy trading price signals released by multi-energy system operators and pursue the maximization of their own operating profits by flexibly adjusting their power output plans.

[0254] ;

[0255] in, This indicates the revenue generated by energy producers from the sale of energy:

[0256] ;

[0257] Indicates the cost of purchasing gas: ;

[0258] Indicates operation and maintenance costs: ;

[0259] Indicates the cost of leasing energy storage capacity:

[0260] ;

[0261] ;

[0262] , These are the carbon trading costs and carbon capture revenues for energy producers, respectively.

[0263] This represents the total incentive amount for energy producers within time period t after a one-way incentive.

[0264] In the formula, To purchase the unit price of natural gas, To purchase natural gas quality, For operation and maintenance cost coefficient, The electrical, heating, and cooling power produced by energy producers. This is the operation and maintenance cost coefficient. The quality of hydrogen ammonia produced by energy producers. This refers to the unit price of natural gas. For natural gas purchase volume, and The respective proportions of energy storage leasing fees borne by operators and energy producers.

[0265] (3) Objective function of energy storage operators:

[0266] Energy storage operators obtain fixed revenue by leasing energy storage capacity to multi-energy system operators and energy producers, and maximize profits by performing charge-discharge operations to achieve peak-valley arbitrage.

[0267] The objective function for energy storage operators is as follows:

[0268] ;

[0269] in, , These represent the revenue from energy storage operations and the cost of energy storage recharge, respectively:

[0270] ;

[0271] ;

[0272] Indicates operation and maintenance costs:

[0273] ;

[0274] Revenue from capacity leasing: ;

[0275] in, Indicates the power output. Indicates the mass released from the storage device. Indicates charging power. Indicates the mass of the storage device filled; and The operation and maintenance cost coefficient for each energy storage device; and The rental price is per unit capacity. and It has an adjustable capacity.

[0276] (4) Objective function for end users:

[0277] End users participate in demand response by adjusting their electricity, heat, and cooling load consumption behavior based on real-time energy price signals released by multi-energy system operators, in order to minimize energy costs.

[0278] ;

[0279] Indicating user comfort level: ;

[0280] Indicates energy cost: ;

[0281] Indicates peak-shaving compensation: ;

[0282] in, , These are the comfort function coefficients, For the load after demand response, and These are the unit price per unit of adjustable power; Indicates the loading of hydrogen, ammonia, and alcohol. ; , , These represent the load transfer amounts for electricity, heat, and cooling loads, respectively.

[0283] S105: Solve the objective function to obtain the optimal energy production plan, energy storage scheduling, and load transfer strategy.

[0284] Among them, the optimal energy production plan, taking electricity as an example, is how much electricity to generate and how to use it; this part of the result determines the output of each power generation and conversion equipment in the system at every moment within 24 hours.

[0285] The optimal energy storage scheduling strategy determines when to charge and when to discharge, which in turn determines the charging and discharging states of the electrical, thermal, cold, hydrogen, ammonia, alcohol, and carbon energy storage media in the system.

[0286] The optimal load shifting strategy refers to how users adjust their energy consumption behavior based on price and carbon reward / penalty signals to smooth out peak loads and fill valleys. This part is a key result of the response carbon emission level factor, reflecting the demand response behavior on the user side, in order to reshape the load curve.

[0287] In this embodiment, the leader-follower game problem is deconstructed into two mutually feedback optimization loops. The upper layer performs strategy search, implemented by a differential evolution algorithm, responsible for selecting the optimal energy trading price scheme. Through mutation, crossover, and selection operations, a multi-dimensional trading price vector that maximizes the profits of the multi-energy system operators is searched globally. The inner layer, precise scheduling, is completed by the Gurobi solver, responsible for executing physical response scheduling. For each set of price signals, the inner layer model is transformed into a MILP problem, precisely solving for the output plans of each entity that satisfy energy balance constraints.

[0288] The upper-level leader aims to maximize profits and uses a differential evolution algorithm to determine the trading prices of electricity, heat, and cooling energy. The lower-level followers, based on these energy trading prices, call the Gurobi solver to find the optimal output plan for each entity. The output plan is then fed back to the upper-level leader to calculate their own revenue. The electricity, heat, and cooling energy price trading strategy is mutated and cross-operated, and through continuous iterative optimization, the optimal energy production plan, energy storage scheduling, and load transfer strategy are obtained.

[0289] For each set of trading price signals, the lower-level model is transformed into a MILP (Mixed Integer Linear Programming) problem to accurately solve the output plans of each entity that satisfy the energy balance constraints.

[0290] Combination Figure 3 The specific solution process is as follows:

[0291] (1) Initialization: Input source-load data (including new energy output forecast and power, heat, cold, hydrogen, ammonia and alcohol load forecast data), set the trading price game boundary, differential evolution algorithm population size and maximum number of iterations;

[0292] (2) Initial feedback: The initial energy trading price is selected by random sampling method. The lower-level followers call Gurobi to solve for the response and feed it back to the multi-energy system operator to calculate the initial revenue. ;

[0293] (3) Evolutionary operation: Perform differential mutation and crossover on the trading price population to generate new trading price schemes. ;

[0294] (4) Competitive selection: targeting Repeat the inner solution process; if the multi-energy system operator benefits under the new scheme... If so, then a greedy replacement is performed to update the optimal population;

[0295] (5) Equilibrium judgment: When the equilibrium is reached When the game equilibrium is reached, the algorithm stops and outputs the trading scheme and scheduling strategy that achieves the Stackelberg equilibrium.

[0296] This embodiment couples multiple heterogeneous energy sources and adopts a multi-entity collaborative scheduling strategy based on master-slave game theory, which can ensure stable power supply to the basic load of electricity, heat and cold under various fault conditions.

[0297] Because islands are far from the upper-level power grid, their transmission power is limited, and power transmission may be interrupted under extreme weather conditions. The system and method in this embodiment can ensure normal system operation even when the power grid supply is interrupted. When the grid stops purchasing electricity, the system can supplement the power gap by purchasing more natural gas resources or using hydrogen energy. The specific effects of balancing electricity, heat, and cooling power are as follows: Figure 4 , Figure 5 and Figure 6 As shown.

[0298] Based on this, when the oil and gas platform interrupts natural gas supply due to special reasons, the system, since it does not burn natural gas and therefore does not produce carbon dioxide, will cease selling methanol fuel externally and only sell ammonia fuel. Internal hydrogen supply will remain unaffected. The system relies on hydrogen fuel cells and energy storage to fill the energy gap. The specific effects of balancing electricity, heat, and cooling power are as follows: Figure 7 , Figure 8 and Figure 9 As shown.

[0299] Furthermore, considering the extreme weather conditions faced by offshore energy islands, including wind and solar power outages, the disappearance of new energy sources under these circumstances means that the hydrogen-based industry needs to temporarily halt. During the adjustment, energy storage is no longer constrained by equal capacity at 0 and 24 hours. The electrothermal-cooled hydrogen energy storage equipment releases a large amount of energy to effectively cope with the most extreme energy shortage conditions. The system and method proposed in this embodiment can maintain a consistent power supply throughout the day for the system's electrical-thermal-cooling load, even under the most extreme operating conditions.

[0300] In summary, the proposed model exhibits excellent robustness. Through multi-energy coupling and cooperative scheduling under a unified framework, the system's ability to cope with faults was considered from the initial design stage, resulting in extremely high robustness.

[0301] This embodiment constructs an integrated island energy system model encompassing the entire industrial chain of electricity, heat, cooling, gas, carbon, hydrogen, ammonia, and alcohol. It achieves precise physical characterization of multiple energy flows, effectively ensuring system robustness. A master-slave game framework is constructed, with the multi-energy system operator as the leader and energy producers, energy storage providers, and end-users as followers, coordinating the interests of each entity through an information exchange mechanism. Combining an improved tiered carbon guidance mechanism with a source-load dual-sided carbon reward and punishment model based on carbon emission level factors, this embodiment can perceive carbon emission fluctuations in real time and convert them into real-time reward and punishment signals, guiding internal system resources to achieve deep emission reduction and maximize the utilization of new energy sources. Finally, the model is efficiently solved using the DE-Gurobi hybrid algorithm system, ensuring rapid convergence of the game equilibrium point under complex constraints.

[0302] Example 2

[0303] In one or more embodiments, a multi-agent collaborative optimization scheduling system for an island energy system is disclosed, specifically including:

[0304] The virtual power plant model building module is used to consider the power balance and quality balance of the energy network and establish an integrated island virtual power plant system model that integrates the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol.

[0305] The carbon trading guidance module is used to calculate the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions of electricity purchased by multi-energy system operators using the island virtual power plant system model, and then obtain the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchased by operators.

[0306] The carbon response module is used to define carbon emission level factors for electricity, heat, and cold based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle. Based on the carbon emission level factors, it calculates the total reward and penalty amount within time period t after the load-side bidirectional carbon response and the total unidirectional incentive amount within time period t on the source side.

[0307] The objective function construction module is used to construct a multi-entity collaborative optimization objective function with multi-energy system operators as leaders, energy producers, energy storage operators and end users as followers, and the goal of maximizing their own profits or minimizing energy costs.

[0308] The objective solution module is used to solve the objective function to obtain the optimal energy production plan, energy storage scheduling and load transfer strategy.

[0309] The specific implementation methods of each module are exactly the same as those in Example 1, and will not be described in detail again.

[0310] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-entity collaborative optimization scheduling method for an island energy system, characterized in that, include: Considering the power balance and quality balance of the energy network, a virtual power plant system model for an island integrating the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol is established. Using the island virtual power plant system model, the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions from electricity purchases by multi-energy system operators are calculated respectively, and then the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchases by operators are obtained respectively. Based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle, carbon emission level factors for electricity, heat, and cooling are defined respectively. Based on the carbon emission level factors, the total amount of rewards and penalties in time period t after the load-side bidirectional carbon response and the total amount of unidirectional incentives in time period t on the source side are calculated respectively. With multi-energy system operators as leaders, energy producers, energy storage operators, and end users as followers, and with the goal of maximizing their own profits or minimizing their energy costs, a multi-entity collaborative optimization objective function is constructed. Solving the objective function yields the optimal energy production plan, energy storage scheduling, and load shifting strategy. The aforementioned carbon trading cost model for energy producers, carbon capture revenue model for energy producers, and carbon penalty cost model for electricity purchasers are specifically implemented through bilateral symmetrical tiered carbon trading. ； in, This refers to the carbon trading costs for energy producers, the carbon capture revenue for energy producers, or the carbon penalty costs for electricity purchases by operators. This refers to carbon allowances for energy producers, carbon capture by energy producers, or carbon emissions from electricity purchases by multi-energy system operators. This indicates the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, or the carbon emissions from electricity purchases by multi-energy system operators. This indicates the corresponding carbon trading benchmark price; This indicates the width of the stepped interval in the corresponding model; The price growth rate in the positive carbon zone is used to penalize high carbon emissions. The rate of increase in the negative carbon range is used to incentivize deep emission reduction efforts. The total reward and penalty amount within time period t after the load-side two-way carbon response is calculated based on the aforementioned carbon emission level factor, specifically as follows: ； ； ； ； ； in, and These represent the total amount of rewards and penalties for both the user side and the energy storage operator within time period t after the two-way carbon response. , , These represent the reward / penalty amounts for electricity, heat, and cooling within time period t on the user's side. , , These represent the reward and penalty amounts for electricity, heat, and cooling for energy storage operators within time period t, respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the response coefficients for electricity, heat, and cold on the load side, respectively. , , These are the unit power rewards and penalties for electricity, heat, and cooling on the load side, respectively. , These represent the upper and lower limits of the reward / penalty amount per unit power of electricity. , These represent the upper and lower limits of the reward / penalty amount per unit power of heat. , These are the upper and lower limits of the reward / penalty amount per unit power for cold applications; , and These are the electrical load, thermal load, and cooling load after demand response; To charge the energy storage, For thermal energy storage and heat storage, For cold energy storage; The total amount of one-way incentives on the source side during time period t is calculated based on the aforementioned carbon emission level factor, specifically as follows: ； ； ； ； ； in, and These represent the total incentive amount within time period t after the one-way incentives are given to energy producers and energy storage operators, respectively. , , These represent the incentive amounts for electricity, heat, and cooling provided by energy producers during time period t. , , These represent the incentive amounts for electricity, heat, and cooling for the energy storage operator during time period t, respectively. , , The unit power excitation quantity of source-side electrical, heat, and cooling capacity. , , These are the excitation coefficients for the source side (electric, thermal, and cold), respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the upper limits of the unit power incentive for source-side electrical, thermal, and cooling production capacity, respectively.

2. The multi-entity collaborative optimization scheduling method for an island energy system as described in claim 1, characterized in that, Establish a virtual power plant system model for an island that integrates the entire industrial chain of electricity, heat, cooling, gas, hydrocarbons, hydrogen, ammonia, and alcohols. Specifically, this includes: Energy producers dominate island wind and solar power generation, electrolytic hydrogen production, combined cooling, heating and power (CCHP), ice storage air conditioning, and methanol and ammonia synthesis systems coupled with CCUS technology; energy storage operators configure batteries, thermal storage devices, cold storage devices, and hydrogen-based industrial storage facilities; the user side includes electricity, heat, and cooling loads, as well as energy demands for hydrogen-powered vehicles and marine navigation.

3. The multi-entity collaborative optimization scheduling method for an island energy system as described in claim 1, characterized in that, The carbon allowance trading volume of energy producers, carbon capture volume of energy producers, and carbon emissions from electricity purchases by multi-energy system operators are calculated separately as follows: The carbon quota trading volume of the energy producer is the difference between the carbon quota obtained by the energy producer from the actual electricity and heat generated and the total amount of carbon dioxide produced by the system from the actual combustion of natural gas. The carbon capture amount of the energy producer is the amount of carbon dioxide actually captured by the energy producer. The carbon emissions from electricity purchases by the multi-energy system operator refer to the carbon emissions generated from purchasing electricity from the grid.

4. The multi-entity collaborative optimization scheduling method for an island energy system as described in claim 1, characterized in that, The specific carbon emission level factors for electricity, heat, and cooling are as follows: ； in, , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the real-time carbon emissions for electricity, heat, and cooling, respectively. , and These represent the average carbon emissions for electricity, heat, and cooling during the scheduling cycle.

5. The multi-entity collaborative optimization scheduling method for an island energy system as described in claim 1, characterized in that, Constructing a multi-agent collaborative optimization objective function, specifically including: Objective function for multi-energy system operators: ； Objective function for energy producers: ； Objective function for energy storage operators: ； End-user objective function: ； in, This refers to the revenue that operators receive from selling energy to users. This refers to the revenue generated by operators from selling energy to energy storage operators. This indicates the cost of purchasing energy from energy producers. This represents the cost of interaction between the operator and the power grid. To compensate operators for peak shaving. To represent the carbon penalty cost for operators purchasing electricity, and These represent the total amount of rewards and penalties for both the user side and the energy storage operator during time period t after the two-way carbon response; and These represent the total incentive amount within time period t after the one-way incentives are given to energy producers and energy storage operators, respectively. For multi-energy system operators, the cost of leasing energy storage capacity; This indicates the revenue generated by energy producers from the sale of energy. Indicates the cost of purchasing gas. Indicates operation and maintenance costs. This indicates the cost of leasing energy storage capacity for energy producers. This indicates the carbon trading costs for energy producers. This indicates the carbon capture revenue for energy producers; , These represent the revenue from releasing energy and the cost of charging energy for energy storage operators, respectively. Indicates operation and maintenance costs. This indicates revenue from capacity leasing; Indicates user comfort. Indicates energy cost, This indicates peak-shaving compensation for users; , , These represent the total revenue of multi-energy system operators, energy producers, and energy storage operators, respectively. It indicates the cost and comfort of energy consumption for users.

6. The multi-entity collaborative optimization scheduling method for an island energy system as described in claim 1, characterized in that, The objective function is solved as follows: The upper-level leaders aim to maximize profits and set the purchase and sale prices of energy for power generation, heat, cooling, and hydrogen-based industries. The lower-level followers, based on these energy prices, call a solver to find the optimal output plan for each entity. The output plan is then fed back to the upper-level leaders to calculate their own profits. By varying and cross-manipulating energy purchase and sale prices, and through continuous iterative optimization, the optimal energy production plan, energy storage scheduling, and load transfer strategy can be obtained.

7. A multi-entity collaborative optimization scheduling system for an island energy system, characterized in that, include: The virtual power plant model building module is used to consider the power balance and quality balance of the energy network and establish an integrated island virtual power plant system model that integrates the entire industrial chain of electricity-heat-cooling-gas-carbon-hydrogen-ammonia-alcohol. The carbon trading guidance module is used to calculate the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, and the carbon emissions of electricity purchased by multi-energy system operators using the island virtual power plant system model, and then obtain the corresponding carbon trading cost model, carbon capture revenue model, and carbon penalty cost model for electricity purchased by operators. The carbon response module is used to define carbon emission level factors for electricity, heat, and cold based on the deviation of real-time carbon emissions from the average carbon emissions during the scheduling cycle. Based on the carbon emission level factors, it calculates the total reward and penalty amount within time period t after the load-side bidirectional carbon response and the total unidirectional incentive amount within time period t on the source side. The objective function construction module is used to construct a multi-entity collaborative optimization objective function with multi-energy system operators as leaders, energy producers, energy storage operators and end users as followers, and the goal of maximizing their own profits or minimizing energy costs. The objective solving module is used to solve the objective function to obtain the optimal energy production plan, energy storage scheduling and load transfer strategy; The aforementioned carbon trading cost model for energy producers, carbon capture revenue model for energy producers, and carbon penalty cost model for electricity purchasers are specifically implemented through bilateral symmetrical tiered carbon trading. ； in, This refers to the carbon trading costs for energy producers, the carbon capture revenue for energy producers, or the carbon penalty costs for electricity purchases by operators. This refers to carbon allowances for energy producers, carbon capture by energy producers, or carbon emissions from electricity purchases by multi-energy system operators. This indicates the carbon quota trading volume of energy producers, the carbon capture volume of energy producers, or the carbon emissions from electricity purchases by multi-energy system operators. This indicates the corresponding carbon trading benchmark price; This indicates the width of the stepped interval in the corresponding model; The price growth rate in the positive carbon zone is used to penalize high carbon emissions. The rate of increase in the negative carbon range is used to incentivize deep emission reduction efforts. The total reward and penalty amount within time period t after the load-side two-way carbon response is calculated based on the aforementioned carbon emission level factor, specifically as follows: ； ； ； ； ； in, and These represent the total amount of rewards and penalties for both the user side and the energy storage operator within time period t after the two-way carbon response. , , These represent the reward / penalty amounts for electricity, heat, and cooling within time period t on the user's side. , , These represent the reward and penalty amounts for electricity, heat, and cooling for energy storage operators within time period t, respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the response coefficients for electricity, heat, and cold on the load side, respectively. , , These are the unit power rewards and penalties for electricity, heat, and cooling on the load side, respectively. , These represent the upper and lower limits of the reward / penalty amount per unit power of electricity. , These represent the upper and lower limits of the reward / penalty amount per unit power of heat. , These are the upper and lower limits of the reward / penalty amount per unit power for cold applications; , and These are the electrical load, thermal load, and cooling load after demand response; To charge the energy storage, For thermal energy storage and heat storage, For cold energy storage; The total amount of one-way incentives on the source side during time period t is calculated based on the aforementioned carbon emission level factor, specifically as follows: ； ； ； ； ； in, and These represent the total incentive amount within time period t after the one-way incentives are given to energy producers and energy storage operators, respectively. , , These represent the incentive amounts for electricity, heat, and cooling provided by energy producers during time period t. , , These represent the incentive amounts for electricity, heat, and cooling for the energy storage operator during time period t, respectively. , , The unit power excitation quantity of source-side electrical, heat, and cooling capacity. , , These are the excitation coefficients for the source side (electric, thermal, and cold), respectively. , , These are carbon emission level factors for electricity, heat, and cooling, respectively. , , These are the upper limits of the unit power incentive for source-side electrical, thermal, and cooling production capacity, respectively.

Images