Multi-objective planning method for regional distributed multi-energy systems considering carbon emissions

Abstract

The present invention discloses a multi-objective planning method for a regional distributed multi-energy system taking carbon emissions into consideration. From the perspective of promoting "carbon reduction" of the distributed multi-energy system, in addition to considering the investment, construction and operation costs in the distributed multi-energy system planning stage, the carbon emissions of the system are also considered. A multi-objective planning method for the distributed multi-energy system is established, and the model is converted into a mixed integer linear programming model for solution. Under the condition of ensuring the safe operation of the system, the investment, construction and operation costs of the project planning cycle are minimized. Under the condition of meeting the load requirements of end users, the carbon emissions of the system are also minimized. Finally, the optimal configuration strategy for the equipment capacity in the distributed system energy station is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of regional distributed multi-energy system planning and optimization, and relates to a multi-objective planning method for a regional distributed multi-energy system considering carbon emissions. Background Art

[0002] With environmental pollution, fossil energy shortages, and the greenhouse effect becoming increasingly prominent, building a clean, low-carbon, safe, and efficient modern energy system has become a major development direction and goal for the electric power industry. Regional distributed multi-energy systems, as regional energy systems serving end-users, achieve the interconnection and conversion of different energy forms—electricity, gas, cooling, and heat. Through full competition and interactive complementarity among these energy forms, they achieve clean, low-carbon, and efficient energy supply, meeting the needs of end-users. Compared with traditional power, natural gas, and thermal systems, regional distributed multi-energy systems actively utilize energy conversion and storage equipment to synergistically optimize different energy forms and actively incorporate renewable clean energy sources such as wind power and photovoltaics, thereby reducing CO2 emissions. Therefore, to ensure the safe and stable operation of urban regional distributed multi-energy systems and the rational allocation of energy conversion and storage equipment within the system, research on planning methods, determining optimal capacity allocation strategies, and improving renewable energy penetration and overall system efficiency has important theoretical and engineering significance. This research also provides technical insights for the implementation of regional distributed multi-energy systems.

[0003] Numerous studies have been conducted domestically and internationally on the planning of regional multi-energy systems. Key approaches include: 1) establishing a planning model that minimizes the total economic cost of system operation to meet user energy demands, using the energy hub within a building as the target, and using an improved dynamic Kriging model to more efficiently obtain the optimal planning solution; 2) establishing a two-layer integrated energy system planning model, with the upper layer being a planning model, solved using a particle swarm optimization algorithm, and the lower layer being a scheduling model, solved using an interior point method; and 3) establishing a dual-objective planning model for integrated energy systems based on energy hub units, based on economic and system environmental indicators. Weighting factors are assigned to different objectives, transforming the problem into a single-objective solution. However, the approach in 1) is more suitable for studying building-level energy system operation issues; 2) the swarm intelligence algorithm employed in this approach is prone to falling into local optimal solutions; and 3) this approach does not intuitively reflect the impact of environmental factors on the system during planning and operation. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-objective planning method for regional distributed multi-energy systems that takes carbon emissions into consideration, which can measure the equipment capacity configuration in the distributed energy station and the CO2 emissions of the system throughout the project cycle, thereby determining the optimal planning strategy.

[0005] The technical solution adopted by the present invention is a multi-objective planning method for a regional distributed multi-energy system taking into account carbon emissions. The distributed multi-energy system includes an electric power system, a natural gas system, and a thermal system. The method is specifically implemented in the following steps:

[0006] Step 1: Determine the load types within the distributed multi-energy system project construction planning period;

[0007] Step 2: Establish a multi-objective capacity configuration planning model for distributed multi-energy systems to achieve optimal capacity configuration;

[0008] Step 3: Establishing constraints to ensure the safe operation of the distributed multi-energy system established by the present invention;

[0009] Step 4: Under the constraints of ensuring the safe operation of the distributed multi-energy system, the urban area to be planned with carbon emissions taken into consideration is solved according to the distributed multi-energy system multi-objective capacity configuration planning model to achieve the distributed multi-energy system multi-objective planning.

[0010] The present invention is also characterized in that:

[0011] The load types in step 1 include electrical load, heating load, and cooling load.

[0012] Step 2 includes establishing objective function I and objective function II, which is specifically implemented as follows:

[0013] Step 2.1: Establish the investment and simulation operation function during the entire project planning cycle as the objective function I;

[0014] The objective function I is shown in formula (1):

[0015] minF ECO =F inv +F ope -F rv (1)

[0016] In formula (1), F inv represents the annual construction investment cost of the distributed integrated energy station, F ope represents the total annual operating cost of the distributed integrated energy station, F rv Plan for equipment residual value at the end of the project;

[0017] Step 2.2: Establish a function to represent the system carbon emissions during the entire project planning cycle to minimize carbon emissions as objective function II;

[0018] The sources of carbon emissions within energy stations in distributed multi-energy systems include the following: CO2 emissions converted from unit gas power purchased from the upstream natural gas network, CO2 emissions converted from unit electric power purchased from the upstream distribution network, and CO2 emissions per unit output power of CHP and GB in distributed energy stations. Objective function II is shown as follows:

[0019]

[0020] In formula (10), They represent the gas power input by CHP and GB equipment in distributed energy station i at time t, They represent the electric power and gas power input from the distribution network and natural gas network to the distributed energy station i at time t, respectively, gas , α elec 、 They respectively represent the emission coefficients of harmful gases during the use of natural gas, electricity and thermal systems.

[0021] The annual construction investment cost calculation formula of distributed integrated energy stations is as follows:

[0022]

[0023]

[0024] In formulas (2)-(3), EH represents the set of distributed energy stations, M represents the type of candidate planning equipment in the distributed energy station, represents the unit investment cost of the candidate equipment of type m, represents the installed capacity of the m-type planning candidate equipment in distributed energy station i, R m Indicates investment cost F inv The equal annual value coefficient, r represents the discount rate of the equipment, and N represents the planning period of the project.

[0025] The total annual operating cost of a distributed integrated energy station is calculated as follows:

[0026]

[0027]

[0028] In formula (4), D k represents the number of days of the kth typical scenario day, represents the annual operating cost of the system on day k in a typical scenario;

[0029] In formula (5), represents the system operation and maintenance cost of distributed energy station i at time t under the typical scenario day k, and the calculation formula is as follows:

[0030]

[0031] In formula (6), Represent the unit operation and maintenance costs of CHP, P2G, GB, DG, AC, and EC respectively; represents the unit penalty cost of distributed energy curtailment; They represent the natural gas inputs of the cogeneration unit and gas boiler in the distributed energy station i at time t respectively; They represent the active power and reduction amount output by distributed energy to energy station i at time t, The heat absorbed by the AC in distributed energy station i from the CHP unit at time t in the table; They represent the charging and discharging power of the energy storage unit EES in energy station i at time t respectively; represents the active power input by the P2G device in distributed energy station i at time t;

[0032] In formula (5), The natural gas purchase cost of distributed energy station i at time t under the typical scenario day k is calculated as follows:

[0033]

[0034] In formula (7), represents the unit price of natural gas supplied by the natural gas network to distributed energy stations, represents the gas purchase volume of distributed energy station i in the natural gas network at time t;

[0035] In formula (5), It represents the energy interaction cost of distributed energy station i at time t under the typical scenario day k, and the calculation formula is as follows:

[0036]

[0037] In formula (8), They represent the unit electricity price of distributed energy station i buying electricity from the grid and selling electricity to users at time t. They represent the power input from the power grid to the distributed energy station i and the power input from the distributed energy station i to the power user at time t respectively.

[0038]

[0039] In formula (9), α represents the equipment residual value coefficient, which is set to 0.05.

[0040] The constraints in step 3 include multi-energy balance constraints in different forms of electricity, gas, heat, and cooling, as well as operation constraints of equipment within the station.

[0041] The operating constraints of the equipment are as follows:

[0042] CHP unit safe operation constraints:

[0043]

[0044]

[0045]

[0046] In formulas (11) and (12), It represents the natural gas power flowing in and out of the CHP unit in the distributed energy station j at time t. They represent the power generation power and heat generation power of the CHP unit in the distributed energy station j at time t respectively. Respectively represent the efficiency of power generation and heat production of the CHP unit;

[0047] GB safe operation constraints:

[0048]

[0049]

[0050] In formulas (14) and (15), represents the gas power consumed by GB in distributed energy station j at time t, represents the thermal efficiency of GB, Indicates the maximum capacity of GB configured by the distributed energy station;

[0051] Constraints on safe operation of power-to-gas equipment:

[0052]

[0053]

[0054] In formulas (16) and (17), represents the input power of the P2G device in the distributed energy station j at time t, Indicates the efficiency of P2G equipment in converting electricity into natural gas. Indicates the amount of natural gas produced by the corresponding P2G device. Indicates the maximum capacity of the P2G device configured during the planning phase;

[0055] Safe operation constraints of absorption refrigeration units:

[0056]

[0057]

[0058] In formulas (18) and (19), represents the thermal power absorbed by the AC device in the distributed energy station j at time t, Indicates the efficiency of the AC unit's absorption cooling, Indicates the cooling power output by the corresponding AC device. Indicates the maximum capacity of the absorption cooling unit installed in the distributed energy station during the planning stage;

[0059] Safe operation constraints of electric refrigeration units EC:

[0060]

[0061]

[0062] In formulas (20) and (21), represents the electric power consumed by the EC device in the distributed energy station j at time t, Indicates the refrigeration conversion efficiency of the electric refrigeration device, Indicates the cooling power output by the corresponding EC device. Indicates the maximum capacity of the electric cooling device installed in the distributed energy station during the planning stage;

[0063] Wind turbine safe operation constraints:

[0064]

[0065]

[0066] In formulas (22) and (23), represents the output of wind turbines at distributed energy station j at time t, represents the wind power factor at time t. Indicates the unit capacity of a single fan. Indicates the number of wind turbines installed at the energy station. Indicates the maximum number of fans installed in the energy station.

[0067] The multi-energy balance constraints are as follows:

[0068] Electric power balance within distributed energy stations:

[0069]

[0070] In formula (24), represents the electric power input from the distribution network by distributed energy station j at time t;

[0071] Gas power balance within distributed energy stations:

[0072]

[0073] In formula (25), represents the gas power received by distributed energy station j from the gas distribution network, represents the natural gas generated by the power-to-energy equipment in distributed energy station j, Respectively represent the gas power consumed by the load, cogeneration unit and gas boiler;

[0074] Thermal power balance within distributed energy stations:

[0075]

[0076] In formula (26), represents the load consumed by distributed energy station j at time t;

[0077] Cooling power balance in distributed energy stations:

[0078]

[0079] In formula (27), It represents the cooling load consumed by distributed energy station j at time t.

[0080] Specifically, step 4 sets priorities for objective function I and objective function II, with investment and operating costs during the entire project planning cycle as the main priority and system carbon emissions during the entire project planning cycle as the secondary priority, that is, objective function I has a higher priority than objective function II; while ensuring the safe operation of the system, the investment and operating costs during the entire project planning cycle are minimized, and while meeting the load requirements of end users, the system carbon emissions are also minimized, thereby obtaining the optimal configuration of equipment capacity in the distributed system energy station and realizing multi-objective planning of distributed multi-energy systems.

[0081] The beneficial effects of the present invention are:

[0082] The present invention proposes a multi-objective planning method for regional distributed multi-energy systems taking carbon emissions into consideration. From the perspective of promoting "carbon reduction" of distributed multi-energy systems, in addition to considering the investment, construction and operation costs in the distributed multi-energy system planning stage, the carbon emissions of the system are also considered. A multi-objective planning method for distributed multi-energy systems is established, and the model is converted into a mixed integer linear programming model for solution. Under the condition of ensuring the safe operation of the system, the investment, construction and operation costs of the project planning cycle are minimized. Under the condition of meeting the load requirements of end users, the carbon emissions of the system are also minimized. Finally, the optimal configuration strategy for the equipment capacity in the distributed system energy station is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1is a flow chart of a multi-objective planning method for a regional distributed multi-energy system considering carbon emissions according to the present invention;

[0084] Figure 2 This is a schematic diagram of an energy station in an urban area’s distributed multi-energy system;

[0085] Figure 3 This is a bar chart of equipment planning strategies in energy stations under different scenarios, among which: Figure 3 (a) is a bar chart of equipment planning strategy in energy station in scenario 1. Figure 3 (b) is a bar chart of equipment planning strategy in energy station in scenario 2. Figure 3 (c) is a bar chart of equipment planning strategies in the energy station in scenario 3. DETAILED DESCRIPTION

[0086] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0087] The present invention considers the multi-objective planning method of regional distributed multi-energy system with carbon emissions. The distributed multi-energy system includes power system, natural gas system and thermal system. The energy conversion and energy storage units to be planned include: gas turbine, cogeneration unit, electric gas source heat pump, photovoltaic power generation, lithium ion battery and thermal storage tank, such as Figure 1 As shown, please follow the steps below:

[0088] Step 1: Determine the load types within the distributed multi-energy system project construction planning period. Load types include electrical load, heating load, and cooling load.

[0089] Step 2: Establish a multi-objective capacity configuration planning model for a distributed multi-energy system. This model is a mixed integer linear programming model, including objective function I and objective function II, with the goal of achieving optimal capacity configuration.

[0090] Step 2.1: Establish the investment and simulation operation function during the entire project planning cycle as the objective function I;

[0091] The objective function I is shown in formula (1):

[0092] minF ECO =F inv +F ope -F rv (1)

[0093] In formula (1), F inv represents the annual construction investment cost of the distributed integrated energy station, F ope represents the total annual operating cost of the distributed integrated energy station, F rv Plan for equipment residual value at the end of the project;

[0094] The annual construction investment cost calculation formula of distributed integrated energy stations is as follows:

[0095]

[0096]

[0097] In formulas (2)-(3), EH represents the set of distributed energy stations, M represents the type of candidate planning equipment in the distributed energy station, represents the unit investment cost of the candidate equipment of type m, represents the installed capacity of the m-type planning candidate equipment in distributed energy station i, R m Indicates investment cost F inv The equal annual value coefficient, r represents the discount rate of the equipment, and N represents the planning period of the project.

[0098] The total annual operating cost of a distributed integrated energy station is calculated as follows:

[0099]

[0100]

[0101] In formula (4), D k represents the number of days of the kth typical scenario day, represents the annual operating cost of the system on day k in a typical scenario;

[0102] In formula (5), represents the system operation and maintenance cost of distributed energy station i at time t under the typical scenario day k, and the calculation formula is as follows:

[0103]

[0104] In formula (6), Represent the unit operation and maintenance costs of CHP, P2G, GB, DG, AC, and EC respectively; represents the unit penalty cost of distributed energy curtailment; They represent the natural gas inputs of the cogeneration unit and gas boiler in the distributed energy station i at time t respectively; They represent the active power and reduction amount output by distributed energy to energy station i at time t, The heat absorbed by the AC in distributed energy station i from the CHP unit at time t in the table; They represent the charging and discharging power of the energy storage unit EES in energy station i at time t respectively; represents the active power input by the P2G device in distributed energy station i at time t;

[0105] In formula (5), The natural gas purchase cost of distributed energy station i at time t under the typical scenario day k is calculated as follows:

[0106]

[0107] In formula (7), represents the unit price of natural gas supplied by the natural gas network to distributed energy stations, represents the gas purchase volume of distributed energy station i in the natural gas network at time t;

[0108] In formula (5), It represents the energy interaction cost of distributed energy station i at time t under the typical scenario day k, and the calculation formula is as follows:

[0109]

[0110] In formula (8), They represent the unit electricity price of distributed energy station i buying electricity from the grid and selling electricity to users at time t. They represent the power input from the power grid to the distributed energy station i and the power input from the distributed energy station i to the power user at time t respectively.

[0111]

[0112] In formula (9), α represents the equipment residual value coefficient, which is set to 0.05.

[0113] Step 2.2: Establish a function to represent the system carbon emissions during the entire project planning cycle to minimize carbon emissions as objective function II;

[0114] The sources of carbon emissions within energy stations in distributed multi-energy systems include the following: CO2 emissions converted from unit gas power purchased from the upstream natural gas network, CO2 emissions converted from unit electric power purchased from the upstream distribution network, and CO2 emissions per unit output power of CHP and GB in distributed energy stations. Objective function II is shown as follows:

[0115]

[0116] In formula (10), They represent the gas power input by CHP and GB equipment in distributed energy station i at time t, They represent the electric power and gas power input from the distribution network and natural gas network to the distributed energy station i at time t, respectively, gas , α elec 、 They respectively represent the emission coefficients of harmful gases during the use of natural gas, electricity and thermal systems.

[0117] Step 3: Establish constraints to ensure the safe operation of the distributed multi-energy system established by the present invention. The constraints include multi-energy balance constraints in different forms of electricity, gas, heat, and cold, as well as operation constraints of equipment within the station.

[0118] The operating constraints of the equipment are as follows:

[0119] CHP unit safe operation constraints:

[0120]

[0121]

[0122]

[0123] In formulas (11) and (12), It represents the natural gas power flowing in and out of the CHP unit in the distributed energy station j at time t. They represent the power generation power and heat generation power of the CHP unit in the distributed energy station j at time t respectively. Respectively represent the efficiency of power generation and heat production of the CHP unit;

[0124] GB safe operation constraints:

[0125]

[0126]

[0127] In formulas (14) and (15), represents the gas power consumed by GB in distributed energy station j at time t, represents the thermal efficiency of GB, Indicates the maximum capacity of GB configured by the distributed energy station;

[0128] Constraints on safe operation of power-to-gas equipment:

[0129]

[0130]

[0131] In formulas (16) and (17), represents the input power of the P2G device in the distributed energy station j at time t, Indicates the efficiency of P2G equipment in converting electricity into natural gas. Indicates the amount of natural gas produced by the corresponding P2G device. Indicates the maximum capacity of the P2G device configured during the planning phase;

[0132] Safe operation constraints of absorption refrigeration units:

[0133]

[0134]

[0135] In formulas (18) and (19), represents the thermal power absorbed by the AC device in the distributed energy station j at time t, Indicates the efficiency of the AC unit's absorption cooling, Indicates the cooling power output by the corresponding AC device. Indicates the maximum capacity of the absorption cooling unit installed in the distributed energy station during the planning stage;

[0136] Safe operation constraints of electric refrigeration units EC:

[0137]

[0138]

[0139] In formulas (20) and (21), represents the electric power consumed by the EC device in the distributed energy station j at time t, Indicates the refrigeration conversion efficiency of the electric refrigeration device, Indicates the cooling power output by the corresponding EC device. Indicates the maximum capacity of the electric cooling device installed in the distributed energy station during the planning stage;

[0140] Wind turbine safe operation constraints:

[0141]

[0142]

[0143] In formulas (22) and (23), represents the output of wind turbines at distributed energy station j at time t, represents the wind power factor at time t. Indicates the unit capacity of a single fan. Indicates the number of wind turbines installed at the energy station. Indicates the maximum number of fans installed in the energy station.

[0144] The multi-energy balance constraints are as follows:

[0145] Electric power balance within distributed energy stations:

[0146]

[0147] In formula (24), represents the electric power input from the distribution network by distributed energy station j at time t;

[0148] Gas power balance within distributed energy stations:

[0149]

[0150] In formula (25), represents the gas power received by distributed energy station j from the gas distribution network, represents the natural gas generated by the power-to-energy equipment in distributed energy station j, Respectively represent the gas power consumed by the load, cogeneration unit and gas boiler;

[0151] Thermal power balance within distributed energy stations:

[0152]

[0153] In formula (26), represents the load consumed by distributed energy station j at time t;

[0154] Cooling power balance in distributed energy stations:

[0155]

[0156] In formula (27), It represents the cooling load consumed by distributed energy station j at time t.

[0157] Step 4: Under the constraints of ensuring the safe operation of the distributed multi-energy system, the urban area to be planned with carbon emissions taken into account is solved according to the distributed multi-energy system multi-objective capacity configuration planning model to achieve the distributed multi-energy system multi-objective planning;

[0158] Specifically, step 4 sets priorities for objective function I and objective function II, with investment and operating costs during the entire project planning cycle as the main priority and system carbon emissions during the entire project planning cycle as the secondary priority, that is, objective function I has a higher priority than objective function II; while ensuring the safe operation of the system, the investment and operating costs during the entire project planning cycle are minimized, and while meeting the load requirements of end users, the system carbon emissions are also minimized, thereby obtaining the optimal configuration of equipment capacity in the distributed system energy station and realizing multi-objective planning of distributed multi-energy systems.

[0159] In order to verify the performance of the multi-objective planning method of regional distributed multi-energy system considering carbon emissions in terms of system investment and operation economy and environmental performance, Figure 2The distributed multi-energy system energy station in the urban area shown in the figure is set up in three scenarios: independent operation planning of the electric and natural gas systems, coupled operation planning of the electric and natural gas systems, and coupled operation. These three scenarios are planned using the planning method of the present invention, as follows:

[0160] Scenario 1: Without considering the coupling between the power system and the natural gas system, an operation model with separate supply of electricity, gas, cooling, and heat is adopted. In this model, the electricity load and natural gas load are supplied by the upper-level power grid and gas grid, respectively, and the cooling load and heating load are supplied by electric refrigeration devices and gas boilers, respectively.

[0161] Scenario 2: Considering the coupling of the natural gas system and the power system, adopting the operation mode of combined power, gas, cooling and heat, and planning the equipment in the distributed energy station while considering only economic goals.

[0162] Scenario 3: Considering the coupling of the natural gas system and the power system, a combined operation mode of electricity, gas, cooling, and heat is adopted. In addition to considering economic objectives, the carbon emissions of system operation are also considered. The distributed multi-energy system multi-objective hierarchical planning method of this invention is used to plan the equipment within the distributed energy station.

[0163] Analysis of capacity configuration strategies for distributed energy stations in different scenarios:

[0164] The planned capacity of equipment in the distributed multi-energy system under three scenarios is shown in Table 1:

[0165] Table 1 Planning capacity experimental results (kW)

[0166]

[0167] Figure 3 (a) shows the planning of gas boilers and electric refrigeration equipment in scenario 1, when each system operates independently; Figure 3 (b) shows the equipment planning under scenario 2, when power-to-gas equipment is introduced and the system is coupled; Figure 3 (c) shows the equipment planning of distributed multi-energy systems under scenario 3, when a multi-objective hierarchical approach is adopted to consider the impact of carbon emissions.

[0168] In the three different scenarios, although Scenario 1 saves costs in the initial stage of investment and construction because it does not involve mutual coupling between electrical systems, it needs to purchase electricity and gas from the power grid and gas network to meet load demand during the operation phase, which increases the system operation cost; Scenario 2 considers coupling the electrical distributed multi-energy system with power-to-gas and gas turbines, adopting a cogeneration mode, and planning and commissioning the power-to-gas equipment at the initial stage of the project. Not only does it not increase the overall project planning and operation costs, but it also shows good economic efficiency in investment and operation costs compared to the mutual operation of each subsystem; Scenario 3 uses the distributed multi-energy system hierarchical planning method considering carbon emissions proposed in this article, considering the access of distributed energy, which not only shows good economic advantages compared to Scenario 2, but also helps to reduce CO2 emissions.

[0169] The economic costs under the three planning scenarios are shown in the following table:

[0170] Table 2 Total investment and operating costs under different scenarios

[0171]

[0172] As shown in Table 2, scenario 2 considering the distributed cogeneration operation model will save 64.86% of the investment and operation costs compared to scenario 1. Under the distributed cogeneration mode considering carbon emissions, the two-level planning model proposed in this paper will save 65.79% of the total investment and operation costs compared to scenario 1, and can save 1.99×10 6 In addition, because Scenario 3 takes the environmental impact of carbon emissions into account during planning, when economic efficiency is optimized, the distributed multi-energy system can achieve a minimum total carbon emission of 113,840 kg within one simulated planned operation year.

Claims

1. A multi-objective planning method for a regional distributed multi-energy system considering carbon emissions. The distributed multi-energy system includes an electric power system, a natural gas system, and a thermal system. The method is characterized by: Please follow the steps below to implement: Step 1: Determine the load types within the distributed multi-energy system project construction planning period; Step 2: Establish a multi-objective capacity configuration planning model for distributed multi-energy systems to achieve optimal capacity configuration; Step 2 includes establishing objective function I and objective function II, which is specifically implemented according to the following steps: Step 2.1: Establish the investment and simulation operation function during the entire project planning cycle as the objective function I; The objective function I is shown in formula (1): In formula (1), represents the annual construction investment cost of the distributed integrated energy station, Represents the total annual operating cost of the distributed integrated energy station, Plan for equipment residual value at the end of the project; Step 2.2: Establish a function to represent the system carbon emissions during the entire project planning cycle to minimize carbon emissions as objective function II; The sources of carbon emissions in energy stations in distributed multi-energy systems include the following: Emissions from the purchase of unit electricity from the upper distribution network Emissions and unit output power of CHP and GB in distributed energy stations Emissions, objective function II is as follows: In formula (10), 、 Respectively Moment Distributed Energy Station Gas power input to CHP and GB equipment, 、 Respectively Distribution grid and natural gas network to distributed energy stations Input electrical power and gas power, 、 、 Respectively represent the emission coefficients of harmful gases during the use of natural gas, electricity, and thermal systems; Step 3: Establishing constraints to ensure the safe operation of the distributed multi-energy system established by the present invention; Step 4: Under the constraints of ensuring the safe operation of the distributed multi-energy system, the urban area to be planned with carbon emissions taken into consideration is solved according to the distributed multi-energy system multi-objective capacity configuration planning model to achieve the distributed multi-energy system multi-objective planning.

2. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 1 is characterized in that: The load types in step 1 include electrical load, thermal load, and cooling load.

3. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 1 is characterized in that: The annual construction investment cost calculation formula of the distributed integrated energy station is as follows: In formulas (2) and (3), represents the set of distributed energy stations, Indicates the type of candidate planning equipment in the distributed energy station, express Unit investment cost of candidate equipment, Distributed Energy Station middle Model planning candidate equipment installation capacity, Indicates investment costs The equal annual value coefficient, represents the discount rate of the equipment, Represents the planning cycle of the project.

4. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 1 is characterized in that: The total annual operating cost of the distributed integrated energy station is calculated as follows: In formula (4), Indicates the The number of typical scenario days, Indicates a typical scenario day Annual operating costs of the system; In formula (5), Indicates a typical scenario day Distributed energy station exist The system operation and maintenance cost at the moment is calculated as follows: In formula (6), 、 、 、 、 、 Represent the unit operation and maintenance costs of CHP, P2G, GB, DG, AC, and EC respectively; represents the unit penalty cost of distributed energy curtailment; 、 Respectively Moment Distributed Energy Station The amount of natural gas input to the combined heat and power units and gas boilers; 、 Respectively Distributed energy to energy stations at all times Output active power and curtailment, Distributed energy stations in the table AC in The heat absorbed from the CHP unit at all times; 、 Respectively Moment Energy Station The charge and discharge power of the energy storage unit EES; Distributed Energy Station P2G equipment in Active power input at all times; In formula (5), Indicates a typical scenario day Distributed energy station exist The natural gas purchase cost at the time is calculated as follows: In formula (7), represents the unit price of natural gas supplied by the natural gas network to distributed energy stations, express Moment Distributed Energy Station Gas purchase volume from the natural gas network; In formula (5), Indicates a typical scenario day Distributed energy station exist The electric energy interaction cost at the moment is calculated as follows: In formula (8), 、 Respectively Moment Distributed Energy Station The unit price of electricity purchased from the grid and sold to users, 、 Respectively The grid is constantly sending energy to distributed energy stations Imported power and distributed energy stations Power input to electricity users, In formula (9), It represents the equipment residual value coefficient, and its value is 0.

05.

5. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 1 is characterized in that: The constraints in step 3 include multi-energy balance constraints in different forms of electricity, gas, heat, and cooling, as well as operation constraints of equipment within the station.

6. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 5 is characterized in that: The operational constraints of the equipment are as follows: CHP unit safe operation constraints: In formulas (11) and (12), Distributed Energy Station Medium CHP unit The natural gas power flowing in and out at all times, 、 Represents distributed energy stations Medium CHP unit The power generation and heat generation at all times, 、 Respectively represent the efficiency of power generation and heat production of the CHP unit; GB safe operation constraints: In formulas (14) and (15), express Moment Distributed Energy Station The gas power consumed by the internal GB, represents the thermal efficiency of GB, Indicates the maximum capacity of GB configured by the distributed energy station; Constraints on safe operation of power-to-gas equipment: In formulas (16) and (17), express Moment Distributed Energy Station Internal P2G device input power, Indicates the efficiency of P2G equipment in converting electricity into natural gas. Indicates the amount of natural gas produced by the corresponding P2G device, Indicates the maximum capacity of the P2G device configured during the planning phase; Safe operation constraints of absorption refrigeration units: In formulas (18) and (19), express Moment Distributed Energy Station The heat power absorbed by the internal AC unit, Indicates the efficiency of the AC unit's absorption cooling, Indicates the cooling power output by the corresponding AC device. Indicates the maximum capacity of the absorption cooling unit installed in the distributed energy station during the planning stage; Safe operation constraints of electric refrigeration units EC: In formulas (20) and (21), express Moment Distributed Energy Station The electrical power consumed by the internal EC device, Indicates the refrigeration conversion efficiency of the electric refrigeration device, Indicates the cooling power output by the corresponding EC device, Indicates the maximum capacity of the electric cooling device installed in the distributed energy station during the planning stage; Wind turbine safe operation constraints: In formulas (22) and (23), represents the output of wind turbines at distributed energy station j at time t, represents the wind power factor at time t, Indicates the unit capacity of a single fan. Indicates the number of wind turbines installed at the energy station, Indicates the maximum number of fans installed in the energy station.

7. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 5 is characterized in that: The multi-energy balance constraints are as follows: Electric power balance within distributed energy stations: In formula (24), represents the electric power input from the distribution network by distributed energy station j at time t; Gas power balance within distributed energy stations: In formula (25), represents the gas power received by distributed energy station j from the gas distribution network, represents the natural gas generated by the power-to-energy equipment in distributed energy station j, 、 、 Respectively represent the gas power consumed by the load, cogeneration unit and gas boiler; Thermal power balance within distributed energy stations: In formula (26), express Moment Distributed Energy Station the load consumed; Cooling power balance in distributed energy stations: In formula (27), express Moment Distributed Energy Station The cooling load consumed.

8. The multi-objective planning method for regional distributed multi-energy systems considering carbon emissions according to claim 1 is characterized in that: Specifically, step 4 is to set priorities for objective function I and objective function II, with investment and operating costs within the entire project planning cycle as the main priority and system carbon emissions within the entire project planning cycle as the secondary priority, that is, objective function I has a higher priority than objective function II; while ensuring the safe operation of the system, the investment and operating costs within the entire project planning cycle are minimized, and while meeting the load requirements of end users, the carbon emissions of the system are also minimized, thereby obtaining the optimal configuration of equipment capacity in the distributed system energy station and realizing multi-objective planning of the distributed multi-energy system.

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