Comprehensive energy system low-carbon economic dispatching method, system, equipment and medium

Abstract

The invention provides an integrated energy system low-carbon economic dispatching method, system and device and a medium, and belongs to the field of integrated energy system optimization dispatching. The method comprises the following steps: establishing an integrated energy microgrid model containing a carbon capture device; in order to cope with source-load multiple uncertainty challenges in the integrated energy microgrid, a hybrid two-stage robust optimization method based on multi-scene probability is provided on the basis of traditional two-stage robust optimization, and a Camp is generated by adopting nested column constraint; decomposing the hybrid two-stage robust optimization model into a main problem and a sub-problem by using a CG algorithm, and carrying out cyclic solution on the main problem and the sub-problem; the carbon transaction cost is considered in the scheduling model, the electric energy transaction of the system is limited to reduce indirect carbon emission, and excessive conservative of the scheduling scheme is avoided. Example results show that the method can give consideration to the robustness, economy and low-carbon property of the system, excessive robustness of the system is avoided through introduction of a carbon transaction mechanism, and low-carbon operation is maintained.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization scheduling, specifically involving a low-carbon economic scheduling method, system, computer equipment, and medium for integrated energy systems. Background Technology

[0002] Guided by the goals of "carbon peaking and carbon neutrality," countries around the world are actively promoting the replacement of traditional fossil fuels with new clean energy sources. Compared to the independent operation mode of a single energy system, the emergence of integrated energy (IEM) systems can optimize the conversion of various energy sources within a microgrid, improve energy utilization and the low-carbon operation level of the system by relying on storage units, help to absorb renewable energy, alleviate energy pressure, improve the ecological environment, and provide a reliable implementation platform for the transformation of the national energy structure towards green and low-carbon.

[0003] Due to the volatility of renewable energy output and load demand in integrated energy microgrids, current domestic and international research primarily employs two main approaches to address this issue: stochastic programming and robust optimization. For example, some studies have constructed multi-objective dynamic economic dispatch models based on chance-constrained programming and solved them using fuzzy theory; others have established stochastic programming models, analyzed the renewable resource market and a two-level stochastic programming framework, transformed them into equivalent single-level hybrid linear models, and then solved them using fuzzy theory. However, while stochastic programming can fundamentally address the uncertainty of renewable resources, the probability distribution of uncertainties in integrated energy microgrids is complex and difficult to describe precisely. Furthermore, it involves high computational costs, low efficiency, and limited modeling and solution accuracy, failing to ensure a balance between economic efficiency and low carbon emissions. Summary of the Invention

[0004] To address the imbalance between economic efficiency and low-carbon emissions in existing integrated energy microgrid systems, this invention incorporates carbon emissions into a two-stage robust optimization model to address source-load uncertainties. This effectively balances economic efficiency and environmental friendliness, reducing indirect carbon emissions from the robust optimization model. This invention provides a low-carbon economic dispatch method, system, equipment, and medium for integrated energy systems.

[0005] To achieve the above objectives, the present invention provides a low-carbon economic dispatch method for an integrated energy system, comprising: Based on the physical properties of energy conversion units, energy storage units, energy load units, low-carbon equipment, and interaction modules, constraints for the integrated energy microgrid system are established. Different operating scenarios for the integrated energy microgrid system are preset, and uncertainty sets are established for the system operating under different scenarios based on the constraints. The uncertainty set refers to the set of upper and lower limits of energy and load demand fluctuations and the maximum number of occurrences of the worst scenario within the integrated energy microgrid system.

[0006] Within the constraints, before all different operating scenarios occur, a basic low-carbon scheduling plan is formulated with the goal of minimizing the total system cost; the basic low-carbon scheduling plan satisfies the constraints of the integrated energy microgrid system and the energy conservation constraints; the most unfavorable scenario with the greatest impact on the total system operating cost is selected from the uncertainty set; within the constraints and uncertainty set of the integrated energy microgrid system, the basic low-carbon scheduling plan is adjusted with the goal of minimizing the total system operating cost under the most unfavorable scenario, and the constraints are updated at the same time.

[0007] The basic low-carbon scheduling plan is cyclically adjusted based on the updated constraints until the difference between the total cost of the basic low-carbon scheduling plan and the total cost of the worst-case scenario is less than a set threshold. This yields the optimal low-carbon scheduling scheme with the lowest cost for the integrated energy microgrid system.

[0008] Preferably, the system constraints include: Equipment capacity constraints: The combined heat and power unit is set to generate heat simultaneously during power generation and the power-to-heat output ratio is fixed, and the battery charging power does not exceed its own maximum capacity; Energy balance constraint: The total supply and demand of energy such as electricity and heat are set to be balanced at all times. The total supply includes production, purchase and conversion; the total demand includes consumption and energy storage. Interaction constraints: The energy purchased and sold between the integrated energy system and the external market shall not exceed the access capacity, and the same type of energy shall not be purchased and sold at the same time. Carbon capture constraints: The carbon capture capacity of the carbon capture equipment is set to not exceed the carbon emissions of the corresponding cogeneration unit, and the capture process consumes system power.

[0009] Preferably, the uncertainty set establishment process includes: Historical output records and future forecasts of renewable energy are obtained from the integrated energy system, as well as historical consumption records and future forecasts of electricity, heat, and cooling loads. The output refers to the actual power output. Based on the maximum physical limits of each piece of equipment in the integrated energy system, determine the upper and lower limits of fluctuations in renewable energy output and electricity, heat, and cooling load demand; By statistically analyzing historical data, we can calculate the deviation patterns between renewable energy output, various load demands, and corresponding forecast values, and identify the high-probability fluctuation ranges of renewable energy output and various load demands in reality. Within the upper and lower limits and ranges of these fluctuations, we can screen out the normal forecast scenarios where energy supply and demand are at normal levels, the optimal scenario of energy surplus, and the worst scenario of energy shortage. Set the maximum number of times the worst-case scenario of supply falling short of demand occurs within the scheduling cycle; An uncertainty set is generated by the upper and lower limits of energy fluctuations and the maximum number of occurrences of the worst-case scenario.

[0010] Preferably, the optimal low-carbon scheduling scheme that minimizes the cost of the integrated energy microgrid system is solved using the C&CG algorithm, specifically including: Step 1: The constraints of the integrated energy microgrid system are relaxed based on the equipment capacity constraints, energy balance constraints, and uncertainty set information. The relaxation involves temporarily ignoring additional constraints that may be generated in the most unfavorable scenario. The goal is to minimize the total cost of the integrated energy microgrid system, and the initial decision variables are solved. A basic low-carbon scheduling plan is output, which includes: the electricity / heat purchase and sale plan with the energy trading market, the energy storage equipment charging and discharging plan, and the initial total cost corresponding to the basic low-carbon scheduling plan. Step 2: Input basic low-carbon scheduling plan and uncertainty set Within the uncertain set, based on the basic low-carbon scheduling plan of the input, we seek the scenario that maximizes the system operating cost. For the most unfavorable scenario selected, within the constraints of equipment capacity and the upper and lower limits of the fluctuation of the uncertainty set, the scheduling strategy is adjusted, the minimum operating cost under the most unfavorable scenario is calculated, and the minimum operating cost under the most unfavorable scenario is output. Establish new constraints to ensure that the basic low-carbon scheduling plan meets the supplementary constraints under the most unfavorable scenario; Step 3: Input the initial total cost of the basic low-carbon scheduling plan, the cost of the worst-case scenario, and the new constraints; With the goal of minimizing the total system operating cost under the worst-case scenario, the basic low-carbon scheduling plan is adjusted, and the constraints are updated. The updated basic low-carbon scheduling plan is re-solved to obtain a new basic scheduling plan and its corresponding total cost. The updated total cost is compared with the cost of the worst-case scenario: if the difference between the two is less than a preset threshold, convergence is determined; otherwise, return to step 2, substitute the new basic scheduling plan into the subproblem, repeatedly filter the worst-case scenario and generate new constraints; when convergence occurs, the final optimal scheduling scheme is output.

[0011] Preferably, the energy conversion unit includes an electric boiler and an electric chiller; the energy storage unit includes a battery, energy equipment, and cold energy storage equipment; the energy load unit includes electrical load, heat load, and cold load; the low-carbon equipment includes a low-carbon capture device responsible for absorbing carbon dioxide emitted by CHP and includes energy storage; the interaction module enables the purchase and sale of electricity and heat with the external energy market.

[0012] Preferably, the optimal low-carbon scheduling scheme includes: requiring the basic low-carbon scheduling plan to cover all scenarios within the uncertainty set; and requiring the optimized low-carbon adjustment strategy for the most unfavorable scenario to ensure that the system has the minimum total operating cost.

[0013] Preferably, the integrated energy microgrid system is established by energy conversion unit, energy storage unit, energy load unit, low-carbon equipment and interaction module.

[0014] This invention also provides a low-carbon economic dispatch system for integrated energy systems, comprising: The basic data establishment module is used to establish constraints for the integrated energy microgrid system based on the physical properties of energy conversion units, energy storage units, energy load units, low-carbon equipment, and interaction modules; it presets different operating scenarios for the integrated energy microgrid system and establishes uncertainty sets for the system operating under different scenarios based on the constraints; the uncertainty set refers to the set of upper and lower limits of energy and load demand fluctuations and the maximum number of occurrences of the worst scenario within the integrated energy microgrid system.

[0015] A two-stage robust optimization model establishment module is used to formulate a basic low-carbon scheduling plan with the objective of minimizing the total system cost before all different operating scenarios occur within the constraints. The basic low-carbon scheduling plan satisfies the constraints of the integrated energy microgrid system and the energy conservation constraints. The module selects the most unfavorable scenarios that have the greatest impact on the total system operating cost from the uncertainty set. Within the constraints and uncertainty set of the integrated energy microgrid system, the module adjusts the basic low-carbon scheduling plan with the objective of minimizing the total system operating cost under the most unfavorable scenario, while updating the constraints.

[0016] The scheduling scheme solution module is used to iteratively adjust the basic low-carbon scheduling plan based on the updated constraints until the difference between the total cost of the basic low-carbon scheduling plan and the total cost of the worst-case scenario is less than a set threshold, thus obtaining the optimal low-carbon scheduling scheme with the minimum cost of the integrated energy microgrid system.

[0017] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the integrated energy system low-carbon economic dispatch method.

[0018] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the integrated energy system low-carbon economic dispatch method.

[0019] The low-carbon economic dispatch method for integrated energy systems provided by this invention has the following beneficial effects: This invention proposes a low-carbon economic dispatch scheme for integrated energy systems. It constructs an integrated energy microgrid model containing carbon capture equipment, formulates a basic low-carbon dispatch plan based on multi-scenario probabilities, selects the most unfavorable scenario, calculates carbon trading costs, and avoids overly conservative dispatch. When the difference between the total cost of the basic low-carbon dispatch plan and the total cost of the most unfavorable scenario is less than a set threshold, the optimal low-carbon dispatch scheme with the lowest cost for the integrated energy microgrid system is obtained. This achieves system robustness, reduces computational load, balances economic efficiency and low-carbon development, and ensures that the carbon trading mechanism remains operational in a low-carbon manner. Attached Figure Description

[0020] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a low-carbon economic dispatch method for an integrated energy system according to an embodiment of the present invention; Figure 2 This invention relates to different operating scenarios and time-of-use electricity prices for the integrated energy system in this embodiment. Figure 2 (a) is a photovoltaic forecasting scenario; Figure 2 (b) is the wind power forecasting scenario; Figure 2 (c) is the electrical load forecasting scenario; Figure 2 (d) represents the carbon emission prediction scenario; Figure 2 (e) represents the heat load prediction scenario; Figure 2 (f) is the time-of-use electricity price of the integrated energy system; Figure 3 This is a power balance diagram of different devices in different scenarios of the integrated energy system according to an embodiment of the present invention; Figure 3 (a) is for optimizing carbon capture devices in deterministic scenarios; Figure 3 (b) is a carbon capture device optimized for deterministic scenarios; Figure 3 (c) uses a carbon capture device for robust scenario optimization; Figure 3 (d) refers to robust scenario optimization and carbon trading mechanism; Figure 4 These are heat load power balance diagrams for different scenarios in embodiments of the present invention; Figure 4 (a) represents scenario one; Figure 4 (b) represents scenario two; Figure 4 (c) represents scenario three; Figure 4 (d) represents scenario four; Figure 5 This is a schematic diagram illustrating the impact of carbon trading prices on system scheduling according to an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the impact of carbon emission quotas on low-carbon scheduling results according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the integrated energy microgrid system structure according to an embodiment of the present invention; Figure 8 This is a flowchart illustrating the solution process of the hybrid two-stage robust optimization model according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0023] This invention provides a low-carbon economic dispatch method for integrated energy systems, specifically as follows: Figure 1 As shown, it includes: The integrated energy microgrid system studied in this invention embodiment comprises five parts: an external energy supply section, an internal energy supply section, an energy conversion device, an energy storage section, and an energy consumption section. The external energy supply section consists of an external power grid and fuel input; the internal energy supply section consists of wind turbines (WT) and photovoltaic (PV); the energy conversion device consists of combined heat and power (CHP) units, electric boilers (EB), and electric coolers (EC); the energy storage section consists of batteries (BT), heating storage (HS), and cold storage equipment; and the energy consumption section consists of electrical load (EL), heating load (HL), and cooling load (CL). It is also equipped with carbon capture equipment to absorb and store the CO2 generated by the system. Furthermore, the system interacts with the energy trading market for electrical and thermal energy. The specific energy system model framework is as follows: Figure 7 As shown.

[0024] The combined heat and power (CHP) units in the integrated energy system adopt traditional back-pressure units, whose electrical and thermal output power have a linear relationship, while satisfying the following constraints: ; (1) In equation (1), and These represent the electrical and thermal output power of the combined heat and power unit during time period t, respectively. and These represent the minimum electrical and thermal output power of the combined heat and power unit, respectively. and These represent the maximum electrical and thermal output power of the combined heat and power unit, respectively. , , For electrothermal conversion efficiency; This represents the amount of natural gas consumed by the combined heat and power unit during time period t; The calorific value of natural gas; The thermal energy conversion rate of natural gas combustion.

[0025] Carbon capture and storage (CCS) equipment is a widely used carbon capture and storage technology in integrated energy microgrid systems. It directly captures and stores carbon dioxide from the flue gas emitted by combined heat and power units in the integrated energy microgrid, thereby achieving low-carbon operation of the integrated energy system. It can also capture carbon emissions from electricity trading.

[0026] (2) In equation (2), This represents the carbon dioxide emissions of the combined heat and power unit during the time period t. The carbon dioxide capture rate of the carbon capture equipment; The amount of carbon dioxide absorbed by the carbon capture device during time period t; The amount of carbon dioxide that the carbon capture device failed to absorb during time period t; The electrical energy consumed by the carbon capture device for the amount of carbon dioxide absorbed during time period t, including the electrical energy consumed for carbon dioxide sequestration. ; This indicates the amount of carbon dioxide captured by the carbon capture device.

[0027] The energy storage devices in the integrated energy microgrid include thermal and electrical energy storage, and realize bidirectional energy conversion through charging and discharging, which improves the flexibility and time-shift adjustability of the integrated energy microgrid system.

[0028] ; (3) In equation (3), The electrical / thermal power absorbed by the electric / thermal energy storage device during time period t; , The upper and lower limits of the electrical / thermal power absorbed by the electric / thermal energy storage device within time period t; , Electric and thermal energy storage devices t Upper and lower limits of the electrical / thermal power released within a time period; For 0-1 variables, Energy absorption efficiency for electric and thermal energy storage devices; Energy release efficiency for electric and thermal energy storage devices; The initial period represents the capacity of electrical and thermal energy storage; cold energy storage is similar to this invention and will not be described in detail here.

[0029] Power conversion constraints of electric boilers: ; (4) In equation (4), These represent the maximum and minimum power outputs of the electric boiler, respectively.

[0030] Electrical cooling conversion constraints: ; (5) In equation (5), They represent t The electrical power consumed and the cooling power output during a given period of time; The power consumption coefficient for electric refrigeration; This indicates the start / stop status of the electric cooling system.

[0031] An integrated energy system is a two-way energy trading hub. When the integrated energy system is short of energy, it can purchase the energy it needs from the energy trading market to meet its supply and demand balance. When the integrated energy system has a surplus of energy, it can sell the excess energy to the energy market to generate revenue.

[0032] ; (6) In equation (6), and These respectively represent the system in t Purchase and sell electricity in the energy trading market during specific time periods; and These respectively represent the system in t Upper and lower limits for electricity purchases within a given time period; and They represent t The upper and lower limits of electricity sales within a given time period; These are 0-1 variables, representing the energy purchase and sale status of the system; similarly... , These respectively represent the system in t During certain periods, heat energy is purchased and sold through the energy trading market. and These represent the upper and lower limits of the system's heat energy purchases within the time period t, respectively. and They represent t The upper and lower limits of what can be sold at high prices within a given time period;

[0033] The integrated energy carbon trading mechanism model approximates the carbon emission quota of the integrated energy system as the output of the combined heat and power unit multiplied by the quota factor. The carbon emission quota model is shown in the equation.

[0034] ; (7) In equation (7), This represents the carbon emission allowance of the system at time t. The carbon quota coefficient for combined heat and power (CHP) units is 0.72 t / MWh.

[0035] The carbon emissions of the integrated energy microgrid system constructed in this invention mainly include the direct carbon emissions generated by the combustion of natural gas by the cogeneration unit and the indirect carbon emissions from the purchase of electricity from the energy trading market. The actual carbon emissions of the cogeneration unit are the carbon dioxide emitted into the atmosphere that is not captured by carbon capture technology. Therefore, the actual carbon emissions of the system are shown in the following formula.

[0036] ; (8) In equation (8), This represents the actual carbon emissions of the integrated energy system during time period t. This represents the average carbon emission factor of the power grid supply within the integrated energy park, with a value of 0.69 t / MWh.

[0037] Integrated Energy System Carbon Trading Cost Calculation Model The carbon trading strategy for an integrated energy system is as follows: when the integrated energy system's carbon emissions exceed its carbon emission allowance during time period t, the system must pay a carbon tax to the carbon trading market; conversely, the system can sell carbon emission allowances to the market to generate revenue. Therefore, the carbon trading cost calculation model is as follows:

[0038] ; (9) In equation (9), The carbon trading cost of the system during period t; This indicates the carbon trading price, valued at 300 yuan / ton.

[0039] This invention addresses the uncertainty of renewable energy output and load demand by constructing a hybrid two-stage robust model. The model consists of an outer-layer min problem and an inner-layer max-min problem, with the optimization objective being to minimize the day-ahead scheduling cost of the system. The first stage of the model is the outer-layer min problem, which involves interacting with the energy trading market to formulate electricity, heat, and energy storage charging / discharging plans before the uncertainty occurs, thus satisfying system operation under any set of scenarios. The second stage, the inner-layer max-min problem, seeks the scenario with the minimum system operating cost within the constructed uncertainty set, given the outer-layer min decision in the first stage. The first and second stages are continuously optimized alternately until the first-stage decision satisfies the requirements of all scenarios within the uncertainty set, while also ensuring that the system minimizes the day-ahead scheduling cost under the worst-case scenario.

[0040] The economic efficiency and low-carbon performance of integrated energy system optimization dispatch are primarily related to the inputs of the robust second-stage scenario. When the actual output of renewable energy and loads deviates from the predicted values, the integrated energy system will formulate effective robust solutions to mitigate the uncertainty risks caused by severe scenarios. Generally speaking, the more conservative the robust optimization scheme, the more the integrated energy system will plan for increased unit output and electricity purchases, thereby increasing dispatch costs and compromising the system's low-carbon benefits.

[0041] Construction of an Uncertainty-Based Optimization Scheduling Model. Source-Load Uncertainty Description: This invention describes the uncertainty of renewable energy output and load demand within an integrated energy microgrid by constructing a source-load polyhedral uncertainty set.

[0042] (10) ; (11) In equations (10) and (11), Integrated energy system i exist t Actual power, predicted power, maximum permissible power fluctuation, and minimum permissible power fluctuation within the time period; 、 These represent integrated energy microgrids. i exist t State variables representing the maximum and minimum fluctuations of renewable energy within a given time period; This represents half the width of the fluctuation range; and These are the uncertainty budgets for renewable energy output and load demand within the integrated energy microgrid. These values ​​are integers; the larger the value, the stronger the system's robustness and the more periods of uncertainty the system experiences.

[0043] Traditional two-stage robust optimization models construct solutions to the impact of uncertain changes in renewable energy and load demand power within an integrated energy microgrid on system operation. The resulting solution represents the decision output under the worst-case scenario, often exhibiting excessive conservatism. However, uncertainty does not entirely bring negative consequences; scenarios that optimize system operation also exist. Therefore, this invention considers predictive scenarios on top of the two-stage robust optimization, overcoming the drawback of excessive conservatism. It also obtains the source-load scheduling results and constructs a hybrid two-stage robust optimization model through a multi-scenario driven approach.

[0044] The predicted scenario model for the operation of the integrated energy microgrid is as follows: (12) In Equation (12), the prediction scenario model in the integrated energy microgrid is a scheduling cycle prediction model with the goal of minimizing the total operating cost of the system, and the distribution values ​​of renewable energy and load demand within the prediction scenario are obtained. .

[0045] The worst-case scenario model for the operation of the integrated energy microgrid is as follows: (13) The severe scenarios within an integrated energy microgrid system constitute a traditional two-order, three-layer robust optimization problem. The first stage, the outer-layer min problem, involves interacting with the energy trading market to formulate electricity, heat, and energy storage charging / discharging plans to meet the system's operational needs under any set of scenarios. The second stage, the inner-layer min-max problem, involves finding the scenario that maximizes the minimum operating cost of the system within the constructed uncertain set after solving the outer-layer min problem's decisions; this is the worst-case scenario. The first and second stages are continuously alternated in optimization until the first-stage decisions satisfy the requirements of all scenarios within the uncertain set. Simultaneously, the uncertain scenarios constructed in the inner layer are fed back to the outer-layer min stage for readjustment and adaptation to the new scenarios; this is called the rescheduling stage.

[0046] Traditional two-stage robustness models, when dealing with uncertainties in integrated energy systems, typically solve for the worst-case two-stage robustness model to obtain the source-load distribution values ​​of the integrated energy system. The traditional two-stage robust optimization model can be described in a compact form as follows: ;(14) In equation (14), x,z , indicates that at a certain point in time x,z ,Down y The feasible region; the constraints in the first row represent the constraints related to variables x and z in the inner min stage; the constraints in the second row represent the constraints related to variables in the outer min-max stage. x, y, zand related constraints, a、b This is the constant coefficient column vector for constructing the model; a,s,f,h,j This is the constant coefficient matrix in the constraint; d,g The constant coefficient column vector is used as a constraint. Since two-stage robust optimization is an algorithm for dealing with nonlinear problems, it cannot be solved directly by a solver. Usually, the C&CG algorithm is used to decompose the two-stage robust problem into an outer problem and an inner problem. At the same time, the inner problem is a two-layer problem. Generally, the KKT conditions are used to transform the two-layer problem into a single-layer max optimization problem.

[0047] The objective function for the hybrid two-stage robust system is the sum of the operating costs, energy trading costs, and carbon-related costs of the integrated energy unit.

[0048] (15) In equation (15), For the day-ahead dispatch cost of the integrated energy system, The operating costs of units in a comprehensive energy system, To integrate the electricity purchase and sale costs of the energy system and the energy trading market, This refers to the costs of carbon trading, carbon capture, and carbon storage within an integrated energy system.

[0049] (16) In equation (16), To measure the operating power of each unit within the integrated energy microgrid, and This represents the cost coefficient for the operation of each unit within the integrated energy microgrid; These are the electricity price, heat price, and natural gas price in the energy trading market during time period t, respectively. , These are the cost coefficients for carbon capture and carbon sequestration of carbon capture equipment within an integrated energy microgrid; The carbon trading cost of the integrated energy microgrid system during time period t.

[0050] During operation, the integrated energy microgrid system must not only meet the constraints of system modeling and carbon trading mechanism, but also the energy conservation constraints within the integrated energy system. (17) In equation (17), , These represent the worst-case wind and solar power outputs within the integrated energy microgrid, respectively. , These represent the predicted power of the electrical load and thermal load within the integrated energy microgrid, respectively.

[0051] The distribution values ​​obtained from each scenario are used as a set to construct a hybrid two-stage robust model based on historical data. Furthermore, the probabilities of scenarios in the constructed hybrid two-stage robust model are uncertain. This invention uses a comprehensive norm to constrain the deviation between the actual probability distribution and the empirical probability distribution for each scenario. The uncertainty set of the scenario probability distribution is as follows:

[0052] (18) In equation (18), S This represents the total number of typical scenarios; and These represent the actual probability distribution and the empirical probability distribution for each scenario; and These are the allowable deviations (probabilistic uncertainty budgets) for the 1-norm and ∞-norm, respectively.

[0053] (19) The first-order decision variables and second-order solution process of the aforementioned hybrid two-stage robust mathematical model are the same. In the second-stage solution process, the hybrid two-stage robust model uses scenario probability distributions to find the minimum operating cost of the system, i.e., the worst-case scenario. Furthermore, the advantage of the hybrid two-stage robust solution is that the outer-layer decision variable `max-min` is not affected by the inner-layer `min` decision, and the inner-layer `min` problem can be decomposed into multiple typical scenario sub-problems to optimize the probability distribution of the worst-case scenario. Meanwhile, the C&CG algorithm is an algorithm for solving the hybrid two-stage robust model. First, the constraints of the main problem are relaxed to a certain extent. Then, the variable values ​​obtained from the main problem are substituted into the sub-problems. The sub-problems then continuously generate scenarios to add variables and constraints to the main problem to generate cutting planes for compensation. This process is repeated until the solutions to the main problem and the sub-problems converge to be equal, thus obtaining the solution to the hybrid two-stage robust optimization model. Furthermore, to avoid the large number of 0-1 variables introduced during the bilinear transformation from a two-level to a single-level model using KKT or duality theory, this effectively improves the accuracy and efficiency of the hybrid two-stage robust solution. The specific flowchart for solving the hybrid two-stage robust optimization model is shown below. Figure 8 As shown.

[0054] This stage verifies the effectiveness of the proposed hybrid two-stage robust optimization model, and uses... Figure 1Taking the integrated energy microgrid system shown as an example, this invention verifies that the proposed model and algorithm optimize the economy, robustness, and low-carbon operation of the microgrid. In the box-like set constructed within the integrated energy microgrid, the fluctuation deviation of renewable energy output is ±15% of the predicted output, with uncertainties budgeted at 12 and 6 for wind power and solar power, respectively; the fluctuation deviation of load demand power is ±10% of the predicted power, with an uncertainty conservatism of 12. In the scenario probability uncertainty set, the empirical probability distributions of the predicted scenario, the optimal scenario, and the worst-case scenario are {0.5, 0.2, 0.3}, and the scheduling cycle selected in this invention is 24 hours.

[0055] To highlight the superiority of the hybrid two-stage robust scheduling plan proposed in this invention, this invention first analyzes the distribution of renewable energy output and load demand power in multiple scenarios (predicted scenario, optimal scenario, worst-case scenario) of microgrids.

[0056] In the worst-case scenario of the integrated energy microgrid, wind power output falls at the extremes of the predicted scenario's fluctuation range during the time periods of 01:00-6:00, 10:00-12:00, and 21:00-23:00. Clearly, the actual wind power output within the integrated energy microgrid system is lower than the predicted output, consistent with the results of traditional two-stage robust optimization of wind power output in the worst-case scenario within the integrated energy microgrid. Conversely, in the optimal scenario of the integrated energy microgrid, wind power output falls at the extremes of the predicted scenario's fluctuation range during the time periods of 01:00-4:00 and 18:00-21:00. The actual wind power output within the integrated energy microgrid system is higher than the predicted output. In the worst-case scenario of the integrated energy microgrid, photovoltaic output falls at the extremes of the predicted scenario's fluctuation range during the time period of 07:00-12:00, while in the optimal scenario, it falls at the extremes of the predicted scenario's fluctuation range during the time period of 07:00-14:00. The above analysis shows that in the worst-case scenario of the traditional two-stage robust system, wind and solar power output is distributed at the extreme point of the fluctuation range of the predicted scenario during the uncertainty period. Actual wind and solar power output is lower than predicted output. The integrated energy system needs to purchase additional electricity from the external energy trading market or increase the output of its internal units to make up for the difference between actual and predicted output. Therefore, this scenario has the strongest robustness, but it also increases the system's carbon emissions, requiring the system to purchase additional carbon emission allowances, thus causing losses in the system's economic efficiency and low-carbon performance. Furthermore, considering that the probability of uncertainty periods in actual microgrid systems fluctuating entirely within the lower range is extremely low, while actual output can also be distributed within the upper range of fluctuation, the optimal two-stage robust system scenario shows that output is distributed at the extreme point of the upper range of fluctuation during the uncertainty period. Therefore, the existence of an optimal scenario needs to be considered.

[0057] Analysis of the load side of the integrated energy microgrid system shows that the worst-case scenario's electrical load power is distributed at the extreme point of the predicted scenario's fluctuation range during the 07:00-18:00 time period. Clearly, the actual electrical power within the integrated energy microgrid is higher than the predicted power, which is the opposite of the wind and solar power distribution results. The integrated energy system needs to purchase additional electricity from the external energy trading market or increase the output of its internal units to make up for the difference between the actual and predicted electrical power. The optimal scenario is distributed at the extreme point of the lower fluctuation range during the 08:00-20:00 time period. The worst-case scenario's thermal load power is distributed at the extreme point of the upper fluctuation range during the 01:00-07:00 and 11:00-13:00 time periods, which is the opposite of the optimal scenario. The conclusions for cooling load power and electric / thermal load power are similar for each scenario. Comprehensive analysis shows that in the worst-case scenario of an integrated energy microgrid system, the actual power consumption during the fluctuation period is higher than the predicted power. The system will increase its operating costs to mitigate the impact of uncertainty. However, considering that the load power in the worst-case scenario is distributed at the extreme point of the lower fluctuation range, it is also difficult to occur in actual microgrids. In contrast, there is also an optimal scenario in which the energy consumption rate is distributed in the upper fluctuation range during the uncertainty period.

[0058] To verify the effectiveness of robust optimization of the integrated energy microgrid system considering carbon trading mechanisms and carbon capture equipment, this invention designs robust optimization and deterministic optimization based on scheduling results of typical scenarios for comparison. It also compares the system with and without carbon trading mechanisms and carbon capture equipment to verify its low-carbon performance.

[0059] The differences in cogeneration unit output, system electricity purchase, and carbon emissions were compared across four scenarios. Table 1 shows the differences in cogeneration unit output, electricity purchase, and carbon emissions across the four optimization scenarios, as detailed below.

[0060] Scenario 1: Without considering system robustness optimization, carbon trading mechanisms, and carbon capture equipment; Scenario 2: Without considering system robustness optimization or carbon trading mechanisms, carbon capture equipment is used; Scenario 3: Considering robust system optimization, without considering carbon trading mechanisms, carbon capture equipment is used; Scenario 4: Consider robust optimization of the system, including carbon trading mechanisms and the use of carbon capture equipment.

[0061] Table 1 Comparative Analysis of the Impact of Different Scenarios on Integrated Energy Microgrid Systems Scenario 1 did not consider robust optimization of the system, nor did it take into account carbon trading mechanisms and carbon capture equipment. Although the combined heat and power (CHP) units and electricity purchases in Scenario 1 were the fewest among the four scenarios, the system's carbon emissions were the highest. Scenario 2, with the addition of carbon capture equipment compared to Scenario 1, reduced carbon emissions by 39.902%. Scenario 3, with robust optimization compared to Scenario 2, saw an increase in CHP units and electricity purchases. This is because it considered the worst-case scenario predicted for all uncertain parameters in the day-ahead period, thus requiring higher costs to compensate for the worst-case scenario, and the system's carbon emissions also increased by 7.79%. The system robust optimization model compensates for system volatility and improves system robustness by increasing the planned output of CHP units and the amount of electricity purchased in the day-ahead period. However, emitting excessive carbon dioxide to improve system robustness is clearly inconsistent with the low-carbon operation requirements proposed in this invention.

[0062] In response to the above situation, Scenario 4 considers a carbon trading mechanism to further constrain the system's carbon emissions. In Scenario 4, the output of each unit increased by 6.71% compared to Scenario 3, while the purchased electricity volume decreased by 49.88%, and the system's carbon emissions decreased by 33.78%.

[0063] While the optimization method in Scenario 4 sacrifices a slight degree of robustness, it achieves significant low-carbon benefits. This demonstrates that the introduction of a carbon trading mechanism can effectively limit electricity purchases to reduce indirect carbon emissions from electricity generation, avoiding overly conservative system practices while maintaining low-carbon operation, thus achieving a balanced optimization between robustness and low-carbon performance.

[0064] Further analysis was conducted on the optimized scheduling results for various scenarios. For example... Figure 3 As shown, the integrated energy microgrid can absorb most of the renewable energy, with the load-side energy demand mainly coming from wind and solar power output. During peak wind power output periods, the system generates excess electricity, which is then sold through the energy market or stored as electricity and heat. During off-peak wind power output periods, the electricity load also falls below its peak, leading to a higher demand for heat energy on the load side. The integrated energy microgrid's electric boilers convert electricity into heat energy to supply the thermal energy system. Figure 3 In scenario (a), since deterministic scenario optimization is adopted and no carbon capture device is used, the system is more inclined to sell electricity rather than store it during peak wind power output, thus enabling the system to obtain better economic benefits. Figure 3 (b) Scenario 2 is based on Scenario 1 and is equipped with a carbon capture device. Since the cogeneration unit inside the system supplies power all day, its carbon capture device is turned on all day to absorb the carbon dioxide emitted by the cogeneration unit. Figure 3(c) Scenario 3 adopts robust optimization, which addresses the uncertainty risks brought about by wind and solar power fluctuations by planning more cogeneration unit output and grid power purchases in the day-ahead and maintaining the battery charging and discharging time in the system at a high level. Figure 3 (d) Scenario 4 adds a carbon trading mechanism to Scenario 3. The integrated energy system is constrained by this mechanism, which reduces the amount of electricity the system purchases in exchange for higher low-carbon benefits.

[0065] In integrated energy microgrids, the main source of heat output is combined heat and power (CHP) units. For example... Figure 4 As shown, during the period from 8:00 to 14:00, the invention constructed... Figure 4 In the four scenarios (a)-(d), the heating power of the cogeneration unit is limited, resulting in a decrease in the system's heating capacity. Simultaneously, the load demand is low and renewable energy is abundant during this period, promoting renewable energy consumption and reducing the system's carbon emissions. Between 18:00 and 21:00, the system's heat load demand is high while renewable energy output is low. Figure 4 (c) Scenario 3 and Figure 4 Scenario (d) employs robust optimization, whereby the system increases the thermal output of the combined heat and power (CHP) units to raise the upper limit of thermal power, thereby mitigating the risks posed by renewable energy output within the system. During this period, the integrated energy system meets the heat load by purchasing thermal energy and releasing a large amount of thermal energy stored in thermal storage devices. Carbon trading prices and carbon emission allowances constitute a significant proportion of low-carbon operation within the integrated energy system, and changes in their parameters will affect the system's operating costs. The carbon trading price and carbon emission allowance coefficients are 300 yuan / ton and 0.7 t / MWh, respectively.

[0066] like Figure 5 As shown, with the increase in carbon trading prices, the integrated energy system's carbon emissions decrease while its total operating costs increase, but the system's carbon emissions are also limited. When the carbon trading price is set at 450 yuan / ton, the integrated energy system's carbon emissions drop sharply. Afterward, the integrated energy system becomes less sensitive to fluctuations in energy trading prices, and carbon emissions continue to decline. At this point, the system is operating in a low-carbon state, and its robustness has reached its optimal level. Increasing the system's purchasing power and unit output cannot further improve its robustness.

[0067] like Figure 6As shown, in an integrated energy system, as the carbon allowance coefficient increases, the corresponding carbon emission allowances obtained by the system also increase, leading to an increase in the system's carbon emissions and a decrease in operating costs. The increase in the trading price of carbon allowances leads to an increase in system operating costs. With increased carbon trading allowances, the integrated energy system can reduce carbon emissions by decreasing overall carbon emissions, increasing the output of more low-carbon equipment, and selling excess carbon emission allowances, thereby reducing the system's carbon emission costs and allowing the system to profit. When the system's carbon emission allowances reach 0.7 t / MWh or higher, the integrated energy system enters a low-carbon operation state, with continuously decreasing operating costs. This indicates that the integrated energy system has entered a period of sufficient carbon emission allowances, where selling allowances generates economic benefits, rather than increasing unit output and purchasing electricity to improve robustness.

[0068] The hybrid two-stage robustness constructed in this invention finds the optimal result of the worst probability distribution within the uncertainty set by constructing a comprehensive norm, and can be adjusted by adjusting the degree of certainty budget. and This reflects the risk preferences of comprehensive energy decision-makers for various typical scenarios, achieving a good trade-off between economy, low carbon emissions, and conservatism. The optimization results of probability uncertainty budgets for different comprehensive norms are shown in Table 2.

[0069] Table 2 Optimization results of probability uncertainty budgets for different comprehensive norms Table 2 shows that the 1-norm and sum -The probability uncertainty of the norm follows and As the probability of a scenario increases, the volatility of that scenario also increases. This is because the proportion of worst-case scenarios increases, while the proportion of predicted and optimal scenarios decreases, thus enhancing the robustness of the integrated energy microgrid system. and When the value is 0, regardless of the value of the probability uncertainty budget for the other norm, the scenario probability distribution is transformed into an empirical probability distribution. In this case, the hybrid two-stage robust optimization is transformed into a two-stage stochastic optimization. Since this result does not consider the deviation between the actual scenario probability and the empirical scenario probability, it has good economic efficiency but poor robustness. = When the probability is set to 1, the scenario probabilities are {0.2, 0, 0.8}, where the predicted scenario probability is 0.2, the optimal scenario probability is 0, and the worst-case scenario probability is 0.8. In this case, the system robustness is close to the result of traditional two-stage robust optimization. In summary, dispatchers of integrated energy microgrid systems can estimate the probability uncertainty based on their risk preference. and Adjustments were made to the conservatism of the scheduling model.

[0070] This invention focuses on energy trading within integrated energy microgrids under uncertain carbon-source-load scenarios. It proposes a hybrid two-stage robust optimization model to address the uncertainty of carbon-source-load power within integrated energy systems. A trading method within the integrated energy system is established based on master-slave game theory, and the solution is obtained using the C&CG algorithm. The main conclusions are as follows: The hybrid two-stage robust optimization method proposed in this invention, considering multiple uncertainties in the carbon-source-load of integrated energy systems, can effectively resist fluctuations in multiple uncertain variables within the integrated energy system. It plays a positive role in both low-carbon and economic benefits, and can better leverage the synergistic carbon reduction advantages of integrated energy systems. By comparing trading schemes within integrated energy systems, the effectiveness of the proposed master-slave game model considering integrated energy system operators and users is verified. This model ensures the interests of both upper-level integrated energy system operators and lower-level users. The hybrid two-stage robust game optimization method with multi-scenario input proposed in this invention can ensure robustness while avoiding the overconservative problem of traditional two-stage robust optimization. Furthermore, by selecting the probability of an appropriate comprehensive norm for scheduling adjustment, it can effectively balance the economy and conservatism of the integrated energy system.

[0071] In summary, carbon trading mechanisms within integrated energy systems facilitate a balanced optimization of system robustness, economic efficiency, and low-carbon performance. Firstly, from an economic perspective, when carbon trading prices rise, the system maintains low-carbon performance by reducing carbon emissions, but at the cost of some economic benefits. Conversely, when carbon emission allowances increase, the system can not only maintain low-carbon performance but also sell excess allowances to generate economic benefits. Secondly, regarding robustness, regardless of whether carbon trading prices or carbon emission allowances rise, the system will choose to reduce the conservatism of its scheduling schemes to achieve higher low-carbon benefits. Finally, the system considers low-carbon performance while ensuring economic efficiency and robustness; these three aspects are mutually constrained, guaranteeing a balanced and optimized scheduling of robustness, economy, and low carbon performance.

[0072] The robust model of the integrated energy system in this invention involves planning additional day-ahead electricity purchases and additional generator output to mitigate the fluctuation risks caused by wind and solar power output during real-time operation. To verify that the system can achieve robustness, economy, and low carbon emissions, this paper selects three day-ahead optimization scheduling methods for comparison, analyzing the differences in the actual total operating cost, pre-scheduling cost, and rescheduling cost of the integrated energy system under different scheduling methods. The comparisons are made between a hybrid two-stage robust optimization method, two-stage robust optimization, and two-stage stochastic optimization.

[0073] Depend on Figure 2 (a)- Figure 2(e) It can be seen that within this integrated energy system, the two-stage robust optimization only considers the worst-case scenario for power output optimization. However, in reality, the probability of the worst-case scenario occurring is extremely small, so the result of this model is overly conservative, leading to poor economic efficiency. The two-stage stochastic optimization, on the other hand, optimizes based on a given wind and solar power output scenario, without considering the probabilistic error between predicted and actual values. While the resulting result is more economical, it does not consider harsh environments and may differ significantly from reality. The hybrid two-stage robust optimization method, however, has a cost between that of two-stage robust optimization and two-stage stochastic optimization, while maintaining, to some extent, the system stability improvement provided by robust optimization and the system economic efficiency improvement provided by stochastic optimization. Figure 2 (a) is the photovoltaic prediction scenario. As time goes by, the optimal scenario occurs around 12 o'clock. Figure 2 (b) is the wind power prediction scenario, with the optimal scenario occurring around 12:00. Figure 2 (c) is the electricity load prediction scenario, with the optimal scenario occurring around 18:00; Figure 2 (d) represents the carbon emission prediction scenario, with the optimal scenario occurring around 4 a.m.; Figure 2 (e) represents the heat load prediction scenario, with the optimal scenario occurring between 10:00 AM and 12:00 PM. Figure 2 (f) is the time-of-use electricity price of the integrated energy system, with the highest electricity load occurring between 9:00-13:00 in the morning and 18:00-22:00 in the evening.

[0074] In-depth analysis reveals that, within the integrated energy system, wind power output under the worst-case scenario occurs during the periods of 01:00-6:00, 10:00-12:00, and 21:00-23:00, all falling at the extreme points of the predicted fluctuation range. Clearly, in this situation, the actual wind power output within the integrated energy system is lower than the predicted output. In contrast, Figure 2(b) In the optimal scenario of the integrated energy system, the wind power output falls at the extremes of the predicted scenario's fluctuation range during the periods of 01:00-04:00 and 18:00-21:00. At this time, the actual wind power output within the integrated energy system shows an increasing trend relative to the predicted output. In the worst-case scenario, the photovoltaic output of the integrated energy system falls above the extremes of the predicted scenario's fluctuation range during the period of 07:00-12:00, while in the optimal scenario, it falls at the extremes of the predicted scenario's fluctuation range during the period of 12:00-14:00. Under these circumstances, the integrated energy system needs to purchase additional electricity from the external energy trading market or increase the output of its internal units to compensate for the difference between the actual and predicted output. Therefore, although this scenario has the strongest robustness, it also leads to an increase in the system's carbon emissions, requiring the system to purchase additional carbon emission allowances, thus causing some loss to the system's economic efficiency and low-carbon performance.

[0075] Based on the same inventive concept, this invention also provides a low-carbon economic dispatch system for integrated energy systems, comprising: The basic data establishment module is used to establish the constraints of the integrated energy microgrid system based on the physical properties of energy conversion units, energy storage units, energy load units, low-carbon equipment and interaction modules; it presets different operating scenarios of the integrated energy microgrid system and establishes uncertainty sets for the system operating under different operating scenarios based on the constraints.

[0076] A two-stage robust optimization model building module is used to construct a two-stage robust optimization model with the objective of minimizing the total system cost over the entire lifecycle within the constraints. The first stage of the model includes: formulating a basic low-carbon scheduling plan before all different operating scenarios occur; the basic low-carbon scheduling plan satisfies the constraints of the integrated energy microgrid system and the energy conservation constraints. The second stage of the model includes: selecting the most unfavorable scenarios that have the greatest impact on the system operating cost from the uncertainty set; for the unfavorable scenarios, adjusting the basic scheduling strategy with the aim of minimizing the system's total lifecycle operating cost under the unfavorable scenarios within the range of fluctuations in the constraints and uncertainty set of the integrated energy microgrid system, while updating the constraints; and feeding back the updated constraints to the first stage of the model to update and optimize the basic low-carbon scheduling plan.

[0077] The scheduling scheme solution module is used for iterative iteration until the difference between the total cost of the basic low-carbon scheduling plan in the first stage of the model and the total cost of the worst scenario in the second stage of the model is less than a set threshold. At this point, the optimal low-carbon scheduling scheme with the lowest cost for the integrated energy microgrid system is obtained.

[0078] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the aforementioned integrated energy system low-carbon economic dispatch method.

[0079] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the integrated energy system low-carbon economic dispatch method provided above.

[0080] Specific limitations on the computational system for the integrated energy system's low-carbon economic dispatch method can be found in the limitations outlined above, and will not be repeated here. Each module in the aforementioned integrated energy system's low-carbon economic dispatch system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, allowing the processor to call and execute the corresponding operations of each module.

[0081] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, the above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A low-carbon economic dispatching method for an integrated energy system, characterized in that, The system comprises: According to the physical properties of the energy conversion unit, the energy storage unit, the energy consumption load unit, the low-carbon equipment and the interaction module, the constraint conditions of the comprehensive energy micro-grid system are established; Different operation scenarios of the comprehensive energy micro-grid system are preset, and an uncertain set of the system running under different operation scenarios is established based on the constraint conditions; the uncertain set refers to the set of the upper and lower limits of the fluctuation of energy and load demand in the comprehensive energy micro-grid system and the maximum number of occurrence of the worst scenario; Within the range of the constraint conditions, before all different operation scenarios occur, a basic low-carbon scheduling plan is made with the minimum total cost of the system as the target; The basic low-carbon scheduling plan meets the constraint conditions and the energy conservation constraint conditions of the comprehensive energy micro-grid system; the most adverse scenario that has the greatest impact on the total cost of the system is selected from the uncertain set; within the constraint conditions and the range of the uncertain set of the comprehensive energy micro-grid system, the basic low-carbon scheduling plan is adjusted with the minimum total cost of the system under the most adverse scenario as the target, and the constraint conditions are updated; The basic low-carbon scheduling plan is adjusted based on the updated constraint conditions until the difference between the total cost of the basic low-carbon scheduling plan and the total cost of the most adverse scenario is less than a set threshold, and the optimal low-carbon scheduling scheme with the minimum cost of the comprehensive energy micro-grid system is obtained. 2.The low-carbon economic dispatching method of the integrated energy system according to claim 1, characterized in that, The system constraint conditions include: Device capacity constraint: set the heat and power output ratio of the combined heat and power unit to be fixed when generating electricity, and the charging power of the battery to be less than the maximum capacity; Energy balance constraint: set the total supply and demand of energy such as electricity and heat at each time to be balanced, and the total supply includes production, purchase and conversion; the total demand includes consumption and energy storage; Interaction constraint: set the energy purchase and sale of the comprehensive energy system with the external market to be less than the access capacity, and the same kind of energy is not sold and purchased at the same time; Carbon capture constraint: set the carbon capture device to absorb less than the carbon emission of the corresponding combined heat and power unit, and the capture process consumes system electricity. 3.The low-carbon economic dispatching method of the integrated energy system according to claim 1, characterized in that, The uncertain set establishment process includes: Obtain the historical output record and future forecast value of renewable energy from the comprehensive energy system, and obtain the historical consumption record and future forecast value of electricity, heat and cold load, the output refers to the actual output power; Determine the upper and lower limits of the fluctuation of renewable energy output and electricity, heat and cold load demand according to the maximum physical limit of each device in the comprehensive energy system; By statistical data, the deviation law of renewable energy output, various load demands and corresponding forecast values is calculated, and the probable fluctuation interval of renewable energy output and various load demands in actual operation is determined; within the upper and lower limits and the interval, the conventional forecast scenario with conventional energy supply and demand, the optimal scenario with energy surplus and the worst scenario with energy shortage are selected; Set the maximum number of occurrence of the worst scenario with supply less than demand in the scheduling period; Generate the uncertain set from the upper and lower limits of energy fluctuation and the maximum number of occurrence of the worst scenario. 4.The low-carbon economic dispatching method of the integrated energy system according to claim 1, wherein, The optimal low-carbon scheduling scheme with the minimum cost of the comprehensive energy micro-grid system is solved by C&CG algorithm, which specifically includes: Step 1: According to the device capacity constraint, energy balance constraint and uncertainty set information of the integrated energy micro-grid system, constraints of the integrated energy micro-grid system are relaxed, that is, temporarily not considering additional constraints generated by the most unfavorable scenario, and initial decision variables are solved with the minimum total cost of the integrated energy micro-grid system as the target; a basic low-carbon scheduling plan is output, which includes: electricity / heat purchase and sale plan of the energy trading market, energy storage device charging and discharging plan, and initial total cost corresponding to the basic low-carbon scheduling plan; Step 2: Input the basic low-carbon scheduling plan and the uncertainty set Based on the input basic low-carbon scheduling plan, find the scenario that makes the system operation cost the highest in the uncertainty set; For the most unfavorable scenario screened out, adjust the scheduling strategy within the upper and lower limits of the device capacity constraint and the uncertainty set, calculate the minimum operation cost under the most unfavorable scenario, and output the minimum operation cost under the most unfavorable scenario; Establish a new constraint condition to ensure that the basic low-carbon scheduling plan meets the additional constraints under the most unfavorable scenario; Step 3: Input the initial total cost of the basic low-carbon scheduling plan, the most unfavorable scenario cost and the new constraint condition; With the minimum total cost of the system under the most unfavorable scenario as the target, adjust the basic low-carbon scheduling plan and update the constraint condition; re-solve the updated basic low-carbon scheduling plan to obtain a new basic scheduling plan and the corresponding total cost; compare the updated total cost with the most unfavorable scenario cost: if the difference between the two is less than a preset threshold, it is determined to converge; otherwise, return to step 2, substitute the new basic scheduling plan into the sub-problem, and repeat the selection of the most unfavorable scenario and the generation of the new constraint; when convergence is achieved, the final optimal scheduling scheme is output.

5. The low-carbon economic dispatch method for an integrated energy system according to claim 1, wherein, The energy conversion unit includes an electric boiler and an electric refrigerator; the energy storage unit includes a battery, an energy device and a cold energy storage device; the energy consumption load unit includes an electric load, a heat load and a cold load; the low-carbon device includes a low-carbon capture device responsible for absorbing carbon dioxide emitted by the CHP, including storage energy consumption; the interaction module realizes the purchase and sale transaction of electric energy and heat energy with the external energy market.

6. The low-carbon economic dispatching method of the integrated energy system according to claim 1, characterized in that, The optimal low-carbon scheduling scheme includes: requiring the basic low-carbon scheduling plan to cover all scenarios in the uncertainty set; the optimal low-carbon adjustment strategy for the most unfavorable scenario needs to ensure that the system has the minimum total operation cost.

7. The low-carbon economic dispatching method of the integrated energy system according to claim 1, characterized in that, The integrated energy micro-grid system is established by the energy conversion unit, the energy storage unit, the energy consumption load unit, the low-carbon device and the interaction module.

8. A low-carbon economic dispatch system for an integrated energy system, characterized in that, It includes: A basic data establishment module for establishing constraint conditions of the integrated energy micro-grid system according to physical properties of the energy conversion unit, the energy storage unit, the energy consumption load unit, the low-carbon device and the interaction module; Different running scenarios of the integrated energy micro-grid system are preset, and an uncertainty set is established for the system running under different running scenarios based on the constraint conditions; the uncertainty set refers to a set of upper and lower limits of fluctuations of energy and load demand in the integrated energy micro-grid system and the maximum number of most unfavorable scenarios; The two-stage robust optimization model establishment module is configured to formulate a basic low-carbon scheduling plan with the minimum total cost of the system as the target within the range of the constraint conditions before all different operation scenarios occur; the basic low-carbon scheduling plan meets the constraint conditions and the energy conservation constraint conditions of the integrated energy micro-grid system; the most adverse scenario that has the greatest impact on the total cost of system operation is selected from the uncertainty set; the basic low-carbon scheduling plan is adjusted and the constraint conditions are updated within the range of the constraint conditions and the uncertainty set of the integrated energy micro-grid system, with the minimum total cost of the system under the most adverse scenario as the target; The scheduling scheme solving module is configured to cyclically adjust the basic low-carbon scheduling plan based on the updated constraint conditions until the difference between the total cost of the basic low-carbon scheduling plan and the total cost of the most adverse scenario is less than a set threshold, and an optimal low-carbon scheduling scheme with the minimum cost of the integrated energy micro-grid system is obtained.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program can implement the steps of the method of any one of claims 1 to 7 when loaded by the processor. The computer program can implement the steps of the method of any one of claims 1 to 7 when loaded by the processor.

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