Park low-carbon planning method based on robust optimization

Abstract

The invention relates to the technical field of power systems, and particularly discloses a park low-carbon planning method based on robust optimization, and the method comprises the steps: constructing a free carbon emission quota transaction model; constructing a deterministic low-carbon optimization planning model of the park integrated energy system by considering multi-energy complementation; quantizing the long-term uncertainty of the load growth rate by adopting a long-term uncertainty description method of the load growth rate based on an information gap decision theory; constructing a low-carbon robust planning model of the integrated energy system based on an information gap decision-making theory based on a free carbon emission quota transaction model and a park integrated energy system deterministic low-carbon optimization planning model constructed by considering multi-energy complementation; and solving the model to obtain a planning scheme. The method has the advantages that collaborative optimization of carbon quota distribution and multi-energy network topology is realized, carbon emission constraints of each year are constructed, and full-life-cycle carbon emission reduction is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a low-carbon planning method for a park based on robust optimization. Background Art

[0002] Existing research has formed a relatively complete technical system in the field of integrated energy system planning for parks, mainly focusing on two cores: multi-energy coupling optimization and uncertainty processing. In terms of the system planning architecture, a hierarchical progressive optimization framework is generally adopted, and an organic connection between investment decision-making and operation scheduling is achieved through a two-layer or multi-level collaborative model. Typical models include upper-layer equipment configuration optimization and lower-layer multi-energy flow collaborative scheduling. At the same time, electric-thermal-gas hybrid power flow constraints, energy storage device regulation, and energy cascade utilization technology are integrated, significantly improving the system economy and energy supply reliability. For uncertainty processing, the research presents a technical path of parallel probability optimization and robust optimization: the former constructs a typical scenario set of source and load through kernel density estimation, and the latter uses interval mathematics or two-stage robust models to cope with extreme fluctuations. In particular, a distributed optimization method integrating the resilience of multi-energy networks has been developed in disaster scenarios.

[0003] Existing research on park collaborative planning focuses on various types of load demands and optimization goals, and mostly uses typical scenario generation and robust optimization to handle short-term uncertainties of source and load. For the whole-life cycle planning of integrated energy systems in parks, the long-term uncertainty of load growth rate has a greater impact on the robustness of the system, and unreasonable redundancy of load growth rate will greatly increase the economic cost. However, the existing technology lacks a systematic modeling of the dynamic correlation mechanism between the long-term load growth trend and the carbon trading policy, and fails to effectively coordinate the coordination relationship between the free quota allocation mechanism and the multi-energy flow topology structure. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a low-carbon planning method for a park based on robust optimization.

[0005] The purpose of the present invention is achieved through the following technical solutions: A low-carbon planning method for a park based on robust optimization, the method includes:

[0006] Construct a free carbon emission quota trading model;

[0007] Construct a deterministic low-carbon optimization planning model for an integrated energy system in a park considering multi-energy complementarity; the constraint conditions of the deterministic low-carbon optimization planning model for the integrated energy system in the park considering multi-energy complementarity include electric bus power balance constraint, heat bus power balance constraint, cold bus power balance constraint, gas bus power balance constraint, hydrogen balance constraint, energy conversion equipment constraint, energy storage constraint, space constraint, electric vehicle charging station constraint, new energy power generation constraint, and low-carbon planning constraint;

[0008] Based on the information gap decision theory, the long-term uncertainty characterization method of load growth rate is used to quantify the long-term uncertainty of load growth rate.

[0009] Based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model of the park's integrated energy system considering multi-energy complementarity, a low-carbon robust planning model of the integrated energy system based on information gap decision theory is constructed; the planning scheme is obtained by solving the model.

[0010] Specifically, the construction of the free carbon emission quota trading model includes calculating the carbon emission quota of the park:

[0011]

[0012] Where, and Assignment of carbon emission quota per unit of heat and electricity to the region; P e,buy,t P is the purchased power; e,CHP,t 、P h,CHP,t are the electrical and thermal power output of CHP respectively; is the electricity-heat conversion coefficient; P h,GB,t The thermal power output of the gas boiler;

[0013] Calculate the actual carbon emissions of the system:

[0014]

[0015] Where K e and K h are the carbon emission intensity per unit of electricity and per unit of heat respectively;

[0016] Calculating the cost of carbon emissions:

[0017]

[0018]

[0019] Where, To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; It is the benchmark price for carbon trading.

[0020] Specifically, the objective function of the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity is:

[0021]

[0022] Where, 、 、 、 They are the investment cost, operation cost, maintenance cost, and carbon trading cost of the park integrated energy system respectively; is the revenue of the electrical energy storage; is the discount rate; is the set of planned equipment; is the unit investment cost of the i-th equipment; 、 and are the planned power and planned capacity of the i-th equipment respectively; is a Boolean variable, and its value of 1 indicates that the i-th equipment is an energy storage type equipment; is the conversion coefficient of the capacity investment cost and the power investment cost; 、 and are the unit electricity and gas purchase costs at time t respectively; 、 and are the unit operation costs of the i-th equipment; is the power of the i-th equipment; P e,buy,t is the electricity purchase power; is the traded carbon emission volume considering the carbon quota; is the carbon emission cost; is the carbon trading benchmark price, and N is the total number of years considered in the system planning; is the unit time interval; is the unit electricity purchase power at time t; 、 and are the discharge and charge powers of the energy storage at time t; 、

[0023] Specifically, the method for characterizing the long-term uncertainty of the load growth rate includes dividing the whole life cycle of the system into 3 stages , with different load growth rates in each stage, and the set of load growth rate benchmark values is expressed as:

[0024]

[0025] In the formula, is the set of electric, heat, and cooling load growth rates in the t-th stage, and

[0026] are the electric, heat, and cooling load growth rates respectively; There is an information gap between the actual growth rate and the nominal value. Let the uncertainty set of the load growth rate be:

[0027]

[0028] In the formula: is the uncertainty range, is the weight coefficient of the uncertainty range; is the weighting of the uncertainty range, reflecting the system's ability to avoid uncertainty risks, denoted as the system robustness coefficient.

[0029] Specifically, the integrated energy system low-carbon robust planning model based on the information gap decision theory is a two-layer robust optimization framework: the upper layer constructs a deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity, and the lower layer constructs an uncertainty set of the load growth rate, quantifies the extreme scenario risk boundary using the IGDT theory, and dynamically adjusts the trade-off relationship between economy and low-carbon goals through the robustness coefficient, finally forming a planning scheme with interval immunity characteristics.

[0030] The present invention has the following advantages:

[0031] The present invention realizes the collaborative optimization of carbon quota allocation and multi-energy network topology by constructing a free carbon emission quota trading model and constructing a carbon quota cost target, constructs carbon emission constraints for each year, and realizes the reduction of carbon emissions in the whole life cycle;

[0032] The present invention combines a robust planning model based on the information gap decision theory, and can realize the multi-time scale collaborative optimization of the long-term load growth trend and short-term fluctuations by combining typical fluctuation scenarios;

[0033] The present invention realizes the multi-energy complementarity of the park integrated energy system and improves the overall operation efficiency through a deterministic low-carbon optimization framework of electricity-thermal-gas-cooling-hydrogen multi-energy coupling and combining a multi-type equipment constraint system. Description of the Drawings

[0034] Figure 1 is a schematic diagram of the process flow of the planning method of the present invention. Detailed Embodiments

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and shown herein can be arranged and designed in various different configurations.

[0036] Therefore, the detailed description of the embodiments of the present invention provided in the drawings below is not intended to limit the scope of the claimed present invention, but only represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0037] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0038] The present invention will be further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0039] As Figure 1 shown, a low-carbon planning method for a park based on robust optimization, the method includes:

[0040] Construct an unpaid carbon emission quota trading model;

[0041] When calculating the carbon emission cost in the integrated energy system, it is necessary to consider the carbon quota and then trade the carbon emission volume. Unpaid allocation is adopted and the carbon emission quota is provided for the system based on the baseline method. The main carbon emission sources of the system are mainly three parts: external power purchase, gas turbine, and gas boiler. Then the carbon emission quota of the park can be expressed by the following formula:

[0042] (1)

[0043] In the formula, and are the carbon emission allocation amounts per unit heat and electricity in the region; P e,buy,t is the purchased power; P e,CHP,t , P h,CHP,t are the electric and heat powers output by the CHP respectively; is the electricity-heat conversion coefficient; P h,GB,t is the heat power output by the gas boiler;

[0044] The actual carbon emission volume model and carbon allocation model of the system are similar, and the factor method is used for calculation. The difference is that the carbon emission coefficient of thermal power is used to replace the carbon emission allocation amount per unit electricity in the region:

[0045] (2)

[0046] In the formula, K e and K h are the carbon emission intensities per unit electricity and per unit heat respectively;

[0047] The traded carbon emissions after considering carbon quotas are the difference between the actual carbon emissions and the carbon emission quotas. At the same time, the carbon emission cost is calculated at a fixed carbon trading price:

[0048] (3)

[0049] (4)

[0050] In the formula, is the traded carbon emissions after considering carbon quotas; is the carbon emission cost; is the benchmark carbon trading price.

[0051] Build a deterministic low-carbon optimization planning model for the integrated energy system in the park considering multi-energy complementarity; the constraint conditions of the deterministic low-carbon optimization planning model for the integrated energy system in the park considering multi-energy complementarity include power balance constraints of the electric busbar, power balance constraints of the heat busbar, power balance constraints of the cold busbar, power balance constraints of the gas busbar, hydrogen balance constraints, energy conversion equipment constraints, energy storage constraints, space constraints, electric vehicle charging station constraints, new energy power generation constraints and low-carbon planning constraints;

[0052] In order to take into account the coordinated planning of various types of energy storage and energy-consuming equipment in the park, taking the investment cost, operation and maintenance cost and carbon trading cost of the integrated energy system in the park as the comprehensive cost of the park, and considering the peak shaving and valley filling benefits of electric energy storage to construct the collaborative planning objective function of the park, the objective function of the low-carbon optimization planning model is:

[0053] (5)

[0054] In the formula, , , , are the investment cost, operation cost, maintenance cost and carbon trading cost of the integrated energy system in the park respectively; is the electric energy storage income; is the discount rate; is the set of planned equipment; is the unit investment cost of the i-th equipment; , are the planned power and planned capacity of the i-th equipment respectively; is a Boolean variable, which is 1 indicating that the i-th equipment is an energy storage type equipment; is the conversion coefficient of the capacity investment cost and the power investment cost; , are the unit power purchase and gas purchase costs at time t respectively; , are the unit costs of curtailed wind and curtailed solar power respectively; is the set of all devices in the park; is the unit operating cost of the i-th device; is the power of the i-th device; P e,buy,t is the power purchase; is the traded carbon emission after considering carbon quotas; is the carbon emission cost; is the carbon trading benchmark price, and N is the total number of years considered in the system planning; is the unit time interval; is the unit power purchase at time t; 、 are the discharge and charge powers of energy storage at time t; 、 are the curtailed wind and curtailed solar power respectively.

[0055] The power balance constraint of the electrical bus is:

[0056] (6)

[0057] (7)

[0058] In the formula, 、 are the wind and solar power outputs; is the power purchase; 、 、 、 are the electrical powers of the hydrogen fuel cell, gas turbine, electrolyzer, and electric chiller at time t respectively; represents the electrical load power in the t time period; and represent the discharge and charge powers of the electric vehicle respectively; and represent the discharge and charge powers of the electrical energy storage respectively; is the power purchase limit.

[0059] The power balance constraint of the thermal bus is:

[0060] (8)

[0061] In the formula, 、 、 、 are the thermal powers of the gas turbine, gas boiler, hydrogen fuel cell, and absorption chiller at time t respectively; represents the thermal load power in the t time period; and represent the heat release and heat charging powers of the thermal energy storage at time t respectively.

[0062] The cold bus power balance constraint is:

[0063] (9)

[0064] In the formula, 、 are the cooling powers of the electric chiller and the absorption chiller respectively; 、 represent the charging and discharging powers of water cold storage respectively; 、 represent the charging and discharging powers of ice cold storage respectively; represents the cooling load power.

[0065] The gas bus power balance constraint is:

[0066] (10)

[0067] (11)

[0068] In the formula, is the gas purchase power at time t; 、 、 represent the output or required gas powers of the methane reactor, gas boiler, and gas turbine; is the gas consumption load; and represent the charging and discharging gas powers respectively; is the gas purchase power limit.

[0069] The hydrogen balance constraint is:

[0070] (12)

[0071] In the formula, is the hydrogen production power of the electrolyzer; 、 are the hydrogen powers required by the methane reactor and the hydrogen fuel cell respectively; and represent the hydrogen storage and hydrogen charging powers respectively.

[0072] The energy conversion equipment constraint is:

[0073] The Park integrated energy system (PIES) constructs a stable energy supply system together with wind-solar generating units, the superior power grid, and the gas grid at the energy input end. The diesel engine (DE) is used to handle emergencies. During the energy conversion process, equipment such as the combined heat and power (CHP) unit, the electric refrigeration (ER) unit, and the gas boiler (GB) play crucial roles. In particular, the combined cooling, heating, and power (CCHP) system realizes the hierarchical utilization of energy through the collaborative operation of the CHP unit and the absorption chiller (AC), further optimizing the energy utilization effect. At the same time, P2G considers two-stage operation, namely, the two key stages of power-to-hydrogen and power-to-gas. The two-stage P2G technology can improve the efficiency and environmental friendliness of energy conversion and utilization, mainly including the electrolyzer (EL), the hydrogen fuel cell (HFC), and the methane reactor (MR). In the energy storage link, the park considers various energy storage devices, including the electric energy storage system (ES), the heat energy storage system (HS), the gas energy storage system (GS), the hydrogen energy storage system (H2S), the ice storage system (IS), and the water storage system (WS), to improve the flexibility and reliability of the overall energy management of the park. According to the relationship between the energy input and output among the PIES systems, the input power, output power, and conversion efficiency of each energy conversion device are denoted as and and respectively. Their output characteristics and constraints are shown as follows:

[0074] (13)

[0075] In the formula, represents the set of electrolyzer, methane reactor, hydrogen fuel cell, boiler, gas turbine, electric refrigeration, and absorption refrigeration equipment; represents the upper limit value of the output power of each energy conversion device, is the input power of the i-th energy conversion device, is the output power of the i-th energy conversion device; is the conversion efficiency of the i-th energy conversion device.

[0076] The energy storage constraint is:

[0077] (14)

[0078] In the formula, , is the set of electrical energy storage, thermal energy storage, gas energy storage, hydrogen energy storage, chilled water storage, and ice storage; , are the charging and discharging powers of the i-th energy storage device at time t, respectively; , are the maximum charging and discharging powers of the i-th energy storage device per single charge, respectively; , are the charging and discharging state parameters of the i-th energy storage device at time t, which are binary variables; , represent the charging and discharging efficiencies of the energy storage device, respectively; , represent the upper and lower limits of the capacity of the i-th energy storage device, respectively; is the capacity of the i-th energy storage device at time t, is the capacity of the i-th energy storage device at time t-1, is the unit time interval.

[0079] The space constraint is:

[0080] (15)

[0081] In the formula: , are the floor areas per unit power of ice storage and chilled water storage; , are the total power capacities of ice storage and chilled water storage; is the maximum space limit of the chilled storage device.

[0082] For the constraints of electric vehicle charging stations, the typical charging and discharging capabilities of electric vehicle clusters are considered in the constraints of electric vehicle charging piles, so as to improve the planning operation efficiency. From the above electric vehicle operation model, the models of all electric vehicle clusters in a certain area can be established and regarded as a single entity. The charging and discharging capacity models of electric vehicle clusters can be obtained by accumulating the charging and discharging capacity models of single electric vehicles. The operation constraints of electric vehicle charging stations mainly consider the constraints of their charging and discharging powers, and the constraints are as follows:

[0083] (16)

[0084] In the formula: is the limit of the discharging capacity ratio; is the limit of the charging capacity ratio; is the capacity of the electric vehicle charging station; and respectively represent the discharging and charging powers of the electric vehicle.

[0085] The new energy power generation constraint is:

[0086] (17)

[0087] In the formula: and respectively represent the upper limit values of the wind power and photovoltaic output powers; and are the wind and photovoltaic power outputs.

[0088] The low-carbon planning constraint is:

[0089] To ensure the low-carbon nature of the park planning, it is set that in each year after carbon peak, the traded carbon emissions need to decrease year by year at a fixed ratio of v%; the constraints are as follows:

[0090]

[0091]

[0092] In the formula, x represents that carbon peak is achieved in the xth year of the plan, taking the 6th year; y represents the years after carbon peak.

[0093] Based on the information gap decision theory, a long-term uncertainty characterization method of the load growth rate is used to quantify the long-term uncertainty of the load growth rate; due to the grid connection power limit, the uncertainty of the load growth rate is an important parameter affecting the robustness. The information gap decision theory (IGDT) is a non-probabilistic uncertainty modeling method, which shows unique advantages in dealing with the uncertainty of the load growth rate: it quantifies the potential deviation range of parameters through the "information gap", getting rid of the dependence on the probability distribution assumption, and is especially suitable for long-term load forecasting scenarios with multiple factors such as policy orientation and economic fluctuations; the robust IGDT takes the double-layer optimization framework as the core, while optimizing decision variables such as equipment capacity, obtaining the maximum allowable deviation threshold of the load growth rate, so as to avoid the risk of insufficient energy supply in the park caused by unexpected load growth; the whole life cycle of the system is divided into 3 stages , and the load growth rate is different in each stage. The set of load growth rate reference values is expressed as:

[0094] (20)

[0095] In the formula, is the growth rate set of electric heating and cooling loads in the tth stage, are the growth rates of electricity, heating and cooling loads respectively;

[0096] There is an information gap between the actual growth rate and the nominal value. Let the uncertainty set of the load growth rate be:

[0097]

[0098] Where: is the uncertainty range, is the uncertainty range weight coefficient; is the weighted uncertainty range, reflecting the system's ability to avoid uncertainty risks, and is recorded as the system robustness coefficient. is the uncertain load growth rate.

[0099] Based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model of the park's integrated energy system considering multi-energy complementarity, a low-carbon robust planning model of the integrated energy system based on information gap decision theory is constructed; the planning scheme is obtained by solving the model.

[0100] The low-carbon robust planning model for the integrated energy system based on information gap decision theory is a two-layer robust optimization framework: the upper layer considers multi-energy complementarity to construct a deterministic low-carbon optimization planning model for the integrated energy system of the park; the lower layer couples the uncertainty set of load growth rates, uses the IGDT theory to quantify the risk boundary of extreme scenarios, and dynamically adjusts the trade-off between economic efficiency and low-carbon goals through the robustness coefficient, ultimately forming a planning scheme with interval immunity characteristics. Its core decision model can be characterized as follows:

[0101]

[0102] Where, Costs calculated for a park planning model that considers long-term uncertainty; The minimum cost value calculated by the deterministic park planning model is: is the uncertainty load growth rate; is the acceptable cost increase factor; is the weight of the uncertainty range.

[0103] The above is only a preferred embodiment of the present invention and does not impose any formal limitations on the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content, or modify it into an equivalent embodiment with equivalent changes. Therefore, all content that does not depart from the technical solution of the present invention, any changes, modifications, equivalent changes and modifications made to the above embodiments according to the technology of the present invention, all fall within the protection scope of this technical solution.

Claims

1. A low-carbon planning method for industrial parks based on robust optimization, characterized in that: The method includes: Constructing a free carbon emission quota trading model; Constructing a deterministic low-carbon optimization planning model for a park integrated energy system considering multi-energy complementarity; the constraint conditions of the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity include power balance constraints for the electric busbar, power balance constraints for the heat busbar, power balance constraints for the cold busbar, power balance constraints for the gas busbar, hydrogen balance constraints, energy conversion equipment constraints, energy storage constraints, space constraints, electric vehicle charging station constraints, new energy power generation constraints, and low-carbon planning constraints; Based on the information-gap decision theory, using a long-term uncertainty characterization method for the load growth rate to quantify the long-term uncertainty of the load growth rate; Constructing a low-carbon robust planning model for the integrated energy system based on the information-gap decision theory based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity; solving the model to obtain a planning scheme.

2. The low-carbon planning method for a park based on robust optimization according to claim 1, characterized in that: The construction of the free carbon emission quota trading model includes calculating the carbon emission quota of the park: ； Wherein, and are the regional unit heat and electricity carbon emission allocation quotas; is the power purchase; , P h,CHP,t are the electricity and heat powers output by the CHP respectively; is the electricity-to-heat conversion coefficient; P h,GB,t is the heat power output by the gas boiler; Calculating the actual carbon emissions of the system: ； where K e and K h are the carbon emission intensities per unit of electricity and per unit of heat respectively; Calculating the carbon emission cost: ； ； In the formula, is the traded carbon emission after considering carbon quotas; is the carbon emission cost; is the carbon trading benchmark price.

3. The method for low-carbon planning of a park based on robust optimization according to claim 2, characterized in that: The objective function of the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity is: ； Wherein, , , , are respectively the investment cost, operation cost, maintenance cost and carbon trading cost of the park integrated energy system; is the revenue of the electrical energy storage; is the discount rate; is the set of planned equipment; is the unit investment cost of the i-th equipment; , are respectively the planned power and planned capacity of the i-th equipment; is a Boolean variable, and its value of 1 indicates that the i-th equipment is an energy storage type equipment; is the conversion coefficient of the capacity investment cost and the power investment cost; , are respectively the unit power purchase and gas purchase costs at time t; , are respectively the unit curtailment of wind and curtailment of light costs; is the set of all equipment in the park; is the unit operation cost of the i-th equipment; is the power of the i-th equipment; P e,buy,t is the power purchase; is the traded carbon emission volume considering the carbon quota; is the carbon emission cost; is the carbon trading benchmark price, and N is the total number of years considered in the system planning; is the unit time interval; is the unit power purchase at time t; , are respectively the discharging and charging powers of the energy storage at time t; , are respectively the curtailment of wind and curtailment of light powers.

4. A low-carbon planning method for a park based on robust optimization according to claim 1, characterized in that: The method for characterizing the long-term uncertainty of the load growth rate includes dividing the whole life cycle of the system into three stages , with different load growth rates in each stage. The set of load growth rate reference values is expressed as: ； Wherein, is the set of growth rates of electric, heat and cooling loads in the t-th stage, are the growth rates of electric, heat and cooling loads respectively; There is an information gap for the actual growth rate near the nominal value. Let the uncertainty set of the load growth rate be: ； In the formula, is the uncertainty range, is the weight coefficient of the uncertainty range; is the weighting of the uncertainty range, reflecting the system's ability to avoid uncertainty risks, denoted as the system robustness coefficient.

5. A low-carbon planning method for a park based on robust optimization according to claim 4, characterized in that: The low-carbon robust planning model for the integrated energy system based on the information-gap decision theory is a two-layer robust optimization framework: the upper layer is the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity, and the lower layer couples the uncertainty set of the load growth rate. Using the IGDT theory to quantify the extreme scenario risk boundary, dynamically adjusting the trade-off relationship between economy and low-carbon goals through the robustness coefficient, and finally forming a planning scheme with interval immunity characteristics.

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