Multi-intelligent micro-grid joint optimization operation method and system considering carbon capture

Abstract

The invention discloses a multi-intelligent micro-grid joint optimization operation method and system considering carbon capture, and the method comprises the steps: considering the difference of intelligent micro-grids in different regions and different characteristics of multi-element loads in the intelligent micro-grids, and enabling a carbon dioxide direct air capture operation mode in a low-electricity-price period and a new energy large-power generation period through employing a carbon capture technology; constructing a multi-intelligent micro-grid joint optimization operation model by taking the minimum comprehensive cost as a target; and solving the multi-intelligent micro-grid joint optimization operation model to obtain a multi-intelligent micro-grid joint optimization operation strategy. According to the method, joint operation optimization is carried out on the intelligent micro-grid connected to the power distribution network in multiple regions and multiple types, the space flexibility of a novel source load is fully utilized, the influence of uncertain factors on various devices in the intelligent micro-grid is considered, the application of a novel carbon capture technology is considered, safe and stable operation of the micro-grid is guaranteed, and meanwhile the energy consumption of the micro-grid is reduced. And the operation cost of the system is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent microgrid optimization, and particularly relates to a method and system for jointly optimizing the operation of multiple intelligent microgrids considering carbon capture. Background Art

[0002] Nowadays, climate change and ecological environment protection have become common challenges faced globally. In order to address the global warming problem caused by greenhouse gas emissions, governments and international organizations have successively set emission reduction targets to promote the development of renewable energy and the improvement of energy utilization efficiency. However, with the growth of the global population, the acceleration of urbanization, and the improvement of industrialization level, the demand for energy by humans continues to increase, and the diversity of energy consumption is also constantly expanding. The contradiction between energy demand and environmental protection has prompted society to explore more efficient and innovative emission reduction paths that can achieve sustainable development goals while meeting energy demand.

[0003] Direct air capture (DAC) directly captures carbon dioxide from the air through chemical or physical adsorption technology, which has the advantages of strong adaptability and flexible deployment, and is particularly suitable for areas where traditional emission reduction is difficult to achieve. However, the implementation of this technology requires a large amount of electricity to maintain the processes of carbon dioxide adsorption, separation, and storage, bringing a reverse pressure on energy consumption and carbon emissions. Therefore, the development of DAC technology needs to be combined with a low-carbon energy supply system to ensure the net benefit of carbon capture, improve the efficiency and economy of carbon capture technology, and thus address the sustainability challenges in the context of energy-intensive background. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for jointly optimizing the operation of multiple intelligent microgrids considering carbon capture in view of the above problems existing in the prior art.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention proposes a method for jointly optimizing the operation of multiple intelligent microgrids considering carbon capture, including:

[0007] S1. Considering the differences of intelligent microgrids in different regions and the different characteristics of multiple loads in the intelligent microgrid, applying carbon capture technology to enable the operation mode of direct air capture of carbon dioxide during low electricity price periods and high renewable energy generation periods, and constructing a joint optimization operation model of multiple intelligent microgrids with the goal of minimizing the comprehensive cost;

[0008] S2. Solving the joint optimization operation model of multiple intelligent microgrids to obtain a joint optimization operation strategy of multiple intelligent microgrids.

[0009] In the above S1, the objective function of the joint optimization operation model of multiple intelligent microgrids includes:

[0010] min C E +C F -IC;

[0011]

[0012] C F =C P +C V +C B +C T ;

[0013]

[0014] In the above formula, C E is the power purchase cost for the smart microgrid to purchase electricity from the superior power grid, C F is the system operation cost, I C is the carbon trading revenue obtained by selling carbon sinks through carbon trading, d is the number of typical days in a year, is the active power transmitted from the superior power grid to the smart microgrid i during period t, is the fixed active load of the smart microgrid i during period t, i R and i I are the residential smart microgrid and the industrial and commercial smart microgrid respectively, is the residential electricity price during period t, is the industrial and commercial electricity price during period t, C P is the penalty cost for reasonably reducing the performance of adjustable load, C V is the subsidy cost for the electric vehicle to perform reverse discharging in the V2G mode, C B is the cycle loss cost brought by the charge and discharge of the energy storage system, C T is the transformer loss cost when the distribution transformer in the substation area is operating at a high load rate, is the carbon sink obtained by the smart microgrid i through direct air capture of carbon dioxide during period t, PR C is the unit price of carbon trading, is the original load of the adjustable load in the smart microgrid i during period t, is the actual load of the adjustable load in the smart microgrid i after optimized adjustment during period t, T U is the unit time, PR P is the unit penalty cost for adjusting the load, is the active power of the electric vehicle in the smart microgrid i performing reverse discharging in the V2G mode during period t, PR DI is the unit subsidy price for the reverse discharging of the electric vehicle, are the charging energy and discharging energy of the energy storage system in the smart microgrid i during period t respectively, PR CY is the unit cycle loss cost of the energy storage system, is a binary variable for the operating state of the distribution transformer in the intelligent microgrid i during period t, PR OL is the unit loss cost when the intelligent microgrid transformer is in a high load rate operating state.

[0015] In the above S1, the constraint conditions of the multi-intelligent microgrid joint optimal operation model include carbon capture system constraints and multi-source load constraints of the intelligent microgrid;

[0016] The carbon capture system constraints include:

[0017]

[0018]

[0019] In the above formula, is the carbon sink obtained by the intelligent microgrid i through direct air capture of carbon dioxide during period t, is the operating power of the carbon capture system in the intelligent microgrid i during period t, is the amount of carbon dioxide that can be captured per unit of energy consumed during period t, is the power upper limit of the carbon capture system in the intelligent microgrid i;

[0020] The multi-source load constraints of the intelligent microgrid include:

[0021]

[0022] In the above formula, is the actual load of the adjustable load in the intelligent microgrid i after optimized adjustment during period t, is the original load of the adjustable load in the intelligent microgrid i during period t, is a binary variable indicating whether the adjustable load power can be adjusted, δ M is a large M constant, κ m is the lower limit of the adjustment ratio of the adjustable load in the intelligent microgrid, T U is the unit time, is the upper limit of the energy that the adjustable load in the intelligent microgrid i is allowed to adjust within a typical day, is the actual shiftable load of the intelligent microgrid i during period t, is the energy demand of the shiftable load in the intelligent microgrid i within a typical day, is the active power of the electric vehicle charging forward in the intelligent microgrid i during period t, is the number of intelligent charging piles in the intelligent microgrid i, P SC is the rated active power of the AC slow charging pile charging forward, α i is a binary variable of the electric vehicle charging pile type, P FCis the rated active power for forward charging of the DC fast charging pile, is the active power of the electric vehicle discharging reversely in the intelligent microgrid i within the time period t, P SD is the rated active power of the AC slow charging pile discharging reversely, P FD is the rated active power of the DC fast charging pile discharging reversely, is the binary variable of the charging and discharging state of the electric vehicle, is the number of electric vehicles with charging demand in the intelligent microgrid i within the time period t, E U is the energy demand for a single charge of the electric vehicle, is the number of electric vehicles initially charged in the intelligent microgrid i within the time period t, is the number of electric vehicles transferred between the intelligent microgrid i and the intelligent microgrid j within the time period t, is the maximum proportion of the number of electric vehicles that can be transferred in the intelligent microgrid i within the time period t.

[0023] In the above S1, the constraint conditions of the multi - intelligent - microgrid joint optimization operation model also include the distributed photovoltaic system output constraint and the energy storage system constraint;

[0024] The distributed photovoltaic system output constraint is:

[0025]

[0026] The energy storage system constraint includes:

[0027]

[0028] In the above formula, is the actual output of the distributed photovoltaic in the intelligent microgrid i within the time period t, is the installed capacity of the distributed photovoltaic in the intelligent microgrid i, is the output coefficient of the distributed photovoltaic within the time period t, P MB is the maximum charge - discharge power of a single energy storage, is the number of energy storage systems in the intelligent microgrid i, is the power of the energy storage system in the intelligent microgrid i within the time period t, are the lower limit and upper limit of the energy state of the energy storage system respectively, BC ES is the capacity of a single energy storage system, BC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last time period within a typical day, are the charging energy and discharging energy of the energy storage system in the intelligent microgrid i within the time period t respectively, is the binary variable of the energy storage system operation state, A t，iis an auxiliary variable.

[0029] In S1, the constraint conditions of the multi - intelligent microgrid joint optimization operation model also include the constraints within the intelligent microgrid sub - area and the operation constraints of mobile energy storage;

[0030] The constraints within the intelligent microgrid sub - area include:

[0031]

[0032] In the above formula, is the active power transmitted from the superior power grid to the intelligent microgrid i within period t, are the charging energy and discharging energy of the mobile energy storage system in the intelligent microgrid i within period t respectively, T U is the unit time, is the fixed active load of the intelligent microgrid i within period t, is the actual load of the adjustable load in the intelligent microgrid i after optimization adjustment within period t, is the active power of the electric vehicle charging forward in the intelligent microgrid i within period t, is the active power of the electric vehicle discharging backward using V2G in the intelligent microgrid i within period t, is the power of the energy storage system in the intelligent microgrid i within period t, is the actual output of the distributed photovoltaic in the intelligent microgrid i within period t, The operating power of the carbon capture system in the intelligent microgrid i within period t, is the capacity of the distribution transformer in the intelligent microgrid i sub - area, φ L is the minimum load rate of the distribution transformer, φ H is the maximum load rate of the distribution transformer, δ M is a large M constant, is the binary variable of the operating state of the distribution transformer in the intelligent microgrid i within period t, δ S is relative to is a very small constant, is the actual load rate of the distribution transformer in the intelligent microgrid i within period t, φ O is the rated load rate of the distribution transformer;

[0033] The operation constraints of mobile energy storage include:

[0034]

[0035]

[0036] In the above formula, is the energy stored in the mobile energy storage system of the intelligent microgrid i within period t, is the energy transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j within the time period t. is the total capacity in the mobile energy storage system of the intelligent microgrid i within the time period t. are respectively the lower limit and the upper limit of the energy state of the energy storage system. is the total capacity transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j within the time period t. is the maximum capacity of the mobile energy storage system that can be connected in the intelligent microgrid i. is the binary variable of the working state of the mobile energy storage system in the intelligent microgrid i within the time period t.

[0037] In the second aspect, the present invention proposes a multi - intelligent microgrid joint optimal operation system considering carbon capture, including a joint optimal operation model construction module and a joint optimal operation model solution module.

[0038] The joint optimal operation model construction module is used to consider the differences of intelligent microgrids in different regions and the different characteristics of multiple loads in the intelligent microgrid, apply carbon capture technology to enable the operation mode of direct air capture of carbon dioxide during low - electricity - price periods and high - new - energy - generation periods, and construct a multi - intelligent microgrid joint optimal operation model with the goal of minimizing the comprehensive cost.

[0039] The joint optimal operation model solution module is used to solve the multi - intelligent microgrid joint optimal operation model and obtain the multi - intelligent microgrid joint optimal operation strategy.

[0040] The joint optimal operation model construction module includes an objective function construction unit.

[0041] The objective function construction unit is used to construct the objective function of the following multi - intelligent microgrid joint optimal operation model:

[0042] min C E +C F -I C ;

[0043]

[0044] C F =C P +C V +C B +C T ;

[0045]

[0046] In the above formula, C E is the power purchase cost for the intelligent microgrid to purchase electricity from the superior power grid, C F is the system operation cost, I C$R$ is the carbon trading revenue obtained from selling carbon sinks through carbon trading, and $d$ is the number of typical days in a year. $P_{i,t}^{grid}$ is the active power transmitted from the superior power grid to the intelligent microgrid $i$ during the time period $t$. $P_{i,t}^{load}$ is the fixed active load of the intelligent microgrid $i$ during the time period $t$. R $i$ I $i_{res}$ and $i_{ind}$ are the residential area intelligent microgrid and the industrial and commercial area intelligent microgrid respectively. $p_{t}^{res}$ is the residential area electricity price during the time period $t$. $p_{t}^{ind}$ is the industrial and commercial area electricity price during the time period $t$. P $C_{penalty}$ is the penalty cost for reasonably reducing the adjustable load performance. V $C_{V2G}$ is the subsidy cost for the reverse discharge of electric vehicles using the V2G mode. B $C_{cycle}$ is the cycle loss cost brought by the charge and discharge of the energy storage system. T $C_{transformer}$ is the transformer loss cost when the distribution transformer in the substation area is operating at a high load rate. $PR_{i,t}$ is the carbon sink obtained by the intelligent microgrid $i$ through direct air capture of carbon dioxide during the time period $t$. C $p_{carbon}$ is the unit price of carbon trading. $P_{i,t}^{original}$ is the original load of the adjustable load in the intelligent microgrid $i$ during the time period $t$. $P_{i,t}^{adjusted}$ is the actual load of the adjustable load in the intelligent microgrid $i$ after optimization and adjustment during the time period $t$. U $PR$ is the unit time. P $C_{penalty\_unit}$ is the unit penalty cost for adjusting the load. $P_{i,t}^{V2G}$ is the active power of the electric vehicle in the intelligent microgrid $i$ using V2G for reverse discharge during the time period $t$. DI $p_{V2G}$ is the unit subsidy price for the reverse discharge of electric vehicles. $PR_{i,t}^{charge}$ and $PR_{i,t}^{discharge}$ are the charging energy and discharging energy of the energy storage system in the intelligent microgrid $i$ during the time period $t$ respectively. CY $C_{cycle\_unit}$ is the unit cycle loss cost of the energy storage system. $PR_{i,t}^{transformer}$ is the binary variable of the operation state of the distribution transformer in the intelligent microgrid $i$ during the time period $t$. OL $C_{transformer\_unit}$ is the unit loss cost when the intelligent microgrid transformer is operating at a high load rate.

[0047] The joint optimization operation model construction module further includes a carbon capture system constraint construction unit and a multi - load constraint construction unit for the intelligent microgrid.

[0048] The carbon capture system constraint construction unit is used to construct the following carbon capture system constraints:

[0049]

[0050] In the above formula, $PR_{i,t}$ is the carbon sink obtained by the intelligent microgrid $i$ through direct air capture of carbon dioxide during the time period $t$. is the operating power of the carbon capture system in the intelligent microgrid i during period t, is the amount of carbon dioxide that can be captured per unit of energy consumed during period t, is the power upper limit of the carbon capture system in the intelligent microgrid i;

[0051] The multi - load constraint construction unit of the intelligent microgrid is used to construct the following multi - load constraints of the intelligent microgrid:

[0052]

[0053]

[0054] In the above formula, is the actual load of the adjustable load in the intelligent microgrid i after optimized adjustment during period t, is the original load of the adjustable load in the intelligent microgrid i during period t, is the binary variable indicating whether the adjustable load power can be adjusted, δ M is the large M constant, κ m is the lower limit of the adjustment ratio of the adjustable load in the intelligent microgrid, T U is the unit time, is the upper limit of the energy that the adjustable load in the intelligent microgrid i is allowed to adjust within a typical day, is the actual shiftable load of the intelligent microgrid i during period t, is the energy demand of the shiftable load in the intelligent microgrid i within a typical day, is the active power of the electric vehicle for forward charging in the intelligent microgrid i during period t, is the number of intelligent charging piles in the intelligent microgrid i, P SC is the rated active power of the AC slow charging pile for forward charging, α i is the binary variable of the electric vehicle charging pile type, P FC is the rated active power of the DC fast charging pile for forward charging, is the active power of the electric vehicle for reverse discharging in the intelligent microgrid i during period t, P SD is the rated active power of the AC slow charging pile for reverse discharging, P FD is the rated active power of the DC fast charging pile for reverse discharging, is the binary variable of the electric vehicle charge - discharge state, is the number of electric vehicles with charging demand in the intelligent microgrid i during period t, E U is the energy demand for a single charge of the electric vehicle, is the number of electric vehicles with initial charging in the intelligent microgrid i during period t, is the number of electric vehicles transferred between the intelligent microgrid i and the intelligent microgrid j within the time period t. is the maximum proportion of the number of electric vehicles that can be transferred in the intelligent microgrid i within the time period t.

[0055] The joint optimization model construction module further includes a distributed photovoltaic system output constraint construction unit and a energy storage system constraint construction unit;

[0056] The distributed photovoltaic system output constraint construction unit is used to construct the following distributed photovoltaic system output constraints:

[0057]

[0058] The energy storage system constraint construction unit is used to construct the following energy storage system constraints:

[0059]

[0060] In the above formula, is the actual output of the distributed photovoltaic in the intelligent microgrid i within the time period t, is the installed capacity of the distributed photovoltaic in the intelligent microgrid i, is the output coefficient of the distributed photovoltaic within the time period t, P MB is the maximum charge and discharge power of a single energy storage, is the number of energy storage systems in the intelligent microgrid i, is the power of the energy storage system in the intelligent microgrid i within the time period t, are the lower and upper limits of the energy state of the energy storage system respectively, BC ES is the capacity of a single energy storage system, BC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last time period within a typical day, are the charging energy and discharging energy of the energy storage system in the intelligent microgrid i within the time period t respectively, is the binary variable of the energy storage system operation state, A t，i is the auxiliary variable.

[0061] The joint optimization operation model construction module further includes an intelligent microgrid substation area constraint construction module and a mobile energy storage operation constraint construction module;

[0062] The intelligent microgrid substation area constraint construction unit is used to construct the following intelligent microgrid substation area constraints:

[0063]

[0064] In the above formula, is the active power transmitted from the superior power grid to the intelligent microgrid i within the time period t, are the charging energy and discharging energy of the mobile energy storage system in the smart microgrid i in time period t, T U is the unit time, is the fixed active load of smart microgrid i in time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, is the power of the energy storage system in the smart microgrid i during time period t, is the actual output of distributed photovoltaic in smart microgrid i in time period t, The operating power of the carbon capture system in the smart microgrid i during time period t, is the capacity of the distribution transformer in the smart microgrid i, φ L is the minimum load factor of the distribution transformer, φH is the maximum load factor of the distribution transformer, δ M is a large M constant, is the binary variable of the operating status of the distribution transformer in the smart microgrid i in time period t, δ S for relative to The smallest constant, is the actual load rate of the distribution transformer in the smart microgrid i during time period t, φ O is the rated load rate of the distribution transformer;

[0065] The mobile energy storage operation constraint construction unit is used to construct the following mobile energy storage operation constraints:

[0066]

[0067] In the above formula, is the energy stored in the mobile energy storage system of smart microgrid i in time period t, is the energy transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the total capacity of the mobile energy storage system of smart microgrid i in time period t, are the lower and upper limits of the energy state of the energy storage system, respectively. is the total capacity transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the maximum capacity of the mobile energy storage system that can be connected to the smart microgrid i, is the binary variable of the working state of the mobile energy storage system in the smart microgrid i during time period t

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention provides a method and system for jointly optimizing the operation of multiple intelligent microgrids considering carbon capture. This method takes into account the differences between intelligent microgrids in different regions and the different characteristics of diverse loads in the intelligent microgrid. It applies carbon capture technology to enable the operation mode of direct air capture of carbon dioxide during low electricity price periods and high output periods of new energy. With the goal of minimizing the comprehensive cost, a joint optimization operation model of multiple intelligent microgrids is constructed; and by solving the joint optimization operation model of multiple intelligent microgrids, a joint optimization operation strategy of multiple intelligent microgrids is obtained. On the one hand, this method considers the differential characteristics of intelligent microgrids in different regions of the distribution network and the different characteristics of diverse loads connected to the intelligent microgrid, conducts joint operation optimization for intelligent microgrids with multiple regions and multiple types connected to the distribution network, fully utilizes the spatial flexibility of new energy sources and loads, and takes into account the impact of uncertain factors on various devices in the intelligent microgrid to ensure the safe and stable operation of the microgrid; on the other hand, this method considers the application of new carbon capture technology, uses direct air capture facilities for carbon dioxide to capture free carbon dioxide in the air during reasonable periods, and sells the carbon sinks generated by capturing carbon dioxide through carbon trading, reducing the operating cost of the system while ensuring the safe and stable operation of the microgrid. Description of the Drawings

[0070] Figure 1 It is the topological structure diagram of the example in Example 1.

[0071] Figure 2 It is the time-of-use electricity price diagram of the example in Example 1.

[0072] Figure 3 It is the output characteristic diagram of distributed photovoltaic power generation of the example in Example 1.

[0073] Figure 4 It is the characteristic curve diagram of the carbon capture coefficient of the example in Example 1.

[0074] Figure 5 It is the chronological output curve diagram of the carbon capture facility of the example in Example 1.

[0075] Figure 6 It is the overall flowchart of the method of the present invention.

[0076] Figure 7 It is the structure diagram of multiple intelligent microgrids of the method in Example 1.

[0077] Figure 8 It is the structure diagram of the system of the present invention. Detailed Embodiments

[0078] The present invention will be further described in detail below in combination with the detailed embodiments and the drawings.

[0079] The present invention proposes a method and system for the combined optimal operation of multi-intelligent microgrids considering carbon capture, taking into account the different characteristics of shiftable loads, adjustable loads, bidirectional loads, and fixed loads in intelligent microgrids, as well as the differential characteristics of commercial and residential area intelligent microgrids in the distribution network. It conducts combined operation optimization for intelligent microgrids with multiple types and multiple regions accessing the distribution network, adopts an operation mode of enabling direct air capture of carbon dioxide during low electricity price periods and high new energy output periods for carbon capture, makes full use of the spatial flexibility of new sources and loads, considers the spatial transfer ability of electric vehicle charging loads between different microgrids, the flexible invocation of mobile energy storage between different time periods and different types of intelligent microgrids, the impact of the load rate of distribution transformers on service life, the impact of industrial and commercial and residential electricity prices on the electricity costs of different types of intelligent microgrids, the impact of lighting conditions at different time periods on the output of distributed photovoltaics in the intelligent microgrid, and the changes in carbon dioxide content in the air in different regions at different time periods. While ensuring the safe and stable operation of the microgrid, it reduces the operating cost of the system.

[0080] Embodiment 1:

[0081] This embodiment takes a 14-node distribution network example as the research object, and its topological structure is as Figure 1 shown. Among them, distribution network nodes P3, P7, P8, P11, and P12 are commercial area nodes, and the remaining distribution network nodes are residential area nodes. The time-of-use electricity price adopted in the example is as Figure 2 shown, the output characteristics of distributed photovoltaics are as Figure 3 shown, the carbon dioxide capture coefficient characteristic curve is as Figure 4 shown, and the chronological output curve of the carbon capture facility is as Figure 5 shown. The parameter settings adopted in the example are as follows: the number of typical days in a year is 365 days, the unit price of carbon trading is 200 yuan per ton, the unit penalty cost of the adjustable load is 0.5 yuan per kilowatt-hour, the unit subsidy price for reverse discharge is 0.5 yuan per kilowatt-hour, the unit time is 1 hour, the unit cycle loss cost of the energy storage system is 1 yuan per kilowatt-hour, the rated active power of a single electric vehicle for forward charging and reverse discharge is 60 kilowatts, the maximum charge and discharge power of a single energy storage is 100 kilowatts, the initial capacity of a single energy storage system is 40 kilowatt-hours, the lower and upper limits of the energy state of the energy storage system are 0.1 and 0.9 respectively, the carbon dioxide capture coefficient is 0.01 ton per kilowatt-hour, the maximum load rate of the distribution transformer is 0.8, the energy demand for a single electric vehicle charging is 40 kilowatt-hours, the minimum load rate of the distribution transformer is 0.2, the rated voltage of the distribution network node is 35 kV, and the lower and upper limits of the distribution network node voltage are 31.5 kV and 38.5 kV respectively.

[0082] As Figure 6 shown, a method for the combined optimal operation of multi-intelligent microgrids considering carbon capture is carried out in sequence according to the following steps:

[0083] 1. Considering the differences of intelligent microgrids in different regions and the different characteristics of diversified loads in intelligent microgrids, the operation mode of directly capturing carbon dioxide from the air is enabled during low electricity price periods and periods of large-scale new energy generation by applying carbon capture technology. A joint optimal operation model of multiple intelligent microgrids is constructed with the goal of minimizing the comprehensive cost.

[0084] The structure of the multiple intelligent microgrids is as Figure 7 shown, including three types of intelligent loads, namely adjustable loads, shiftable loads, and bidirectional loads, as well as fixed loads, a high proportion of distributed photovoltaic systems, electrochemical energy storage facilities, a carbon capture system, and mobile energy storage devices. Among them, adjustable loads refer to loads whose usage intensity is appropriately optimized without significantly affecting the user experience, such as the loads generated by temperature control facilities like air conditioners; shiftable loads refer to loads with the potential to shift the usage time period, such as the loads generated by facilities like intelligent washing machines and disinfection cabinets; bidirectional loads refer to loads with a certain reverse discharge function, such as electric vehicle loads that can charge forward and discharge in reverse through V2G; fixed loads refer to traditional loads without spatio-temporal flexibility and adjustability. In terms of the electricity purchase cost, since the electricity purchase cost of fixed loads is a constant, the cost brought by fixed loads is not considered in the objective of the optimization model.

[0085] Considering the impact of industrial and commercial electricity prices and residential electricity prices on the electricity consumption costs of different types of intelligent microgrids, the objective function of the joint optimal operation model of multiple intelligent microgrids includes:

[0086] min C E +C F -I C ;

[0087]

[0088] C F =C P +C V +C B +C T ;

[0089]

[0090] In the above formula, C E is the electricity purchase cost of the intelligent microgrid purchasing electricity from the superior power grid, C F is the system operation cost, I C is the carbon trading revenue obtained by selling carbon sinks through carbon trading, d is the number of typical days in a year, is the active power transmitted from the superior power grid to the intelligent microgrid i during period t, is the fixed active load of the intelligent microgrid i during period t, i R 、i IThey are the residential area intelligent microgrid and the industrial and commercial area intelligent microgrid respectively. is the residential area electricity price during period t. is the industrial and commercial area electricity price during period t, C P is the penalty cost for reasonably reducing the adjustable load performance, C V is the subsidy cost for the electric vehicle to perform reverse power discharge in the V2G mode, C B is the cycle loss cost brought by the charge and discharge of the energy storage system, C T is the transformer loss cost when the distribution transformer in the substation area is operating at a high load rate. is the carbon sink obtained by the intelligent microgrid i through direct air capture of carbon dioxide during period t, PR C is the unit price of carbon trading. is the original load of the adjustable load in the intelligent microgrid i during period t. is the actual load of the adjustable load in the intelligent microgrid i after optimization and adjustment during period t, T U is the unit time, PR P is the unit penalty cost for adjusting the load. is the active power of the electric vehicle in the intelligent microgrid i performing reverse power discharge using V2G during period t, PR DI is the unit subsidy price for the reverse power discharge of the electric vehicle. are the charging energy and discharging energy of the energy storage system in the intelligent microgrid i during period t respectively. For any period t, one of the two must be 0 to ensure the unity of its working state, PR CY is the unit cycle loss cost of the energy storage system. is the binary variable of the operation state of the distribution transformer in the intelligent microgrid i during period t. When the transformer is in the normal operation state, and when the transformer is in the high load rate operation state, PR OL is the unit loss cost when the intelligent microgrid transformer is in the high load rate working state.

[0091] The constraint conditions of the multi-intelligent microgrid joint optimization operation model include the carbon capture system constraint, the multi-source load constraint of the intelligent microgrid, the output constraint of the distributed photovoltaic system, the energy storage system constraint, the constraint within the intelligent microgrid substation area, the mobile energy storage operation constraint, and the distribution network operation constraint.

[0092] Considering the changes in the carbon dioxide content in the air in different regions at different times, the output characteristics of direct air capture of carbon dioxide are optimized accordingly. The carbon capture system constraints include:

[0093]

[0094]

[0095] In the above formula, is the carbon sink obtained by the intelligent microgrid i through direct air capture of carbon dioxide during the time period t, is the operating power of the carbon capture system in the intelligent microgrid i during the time period t, is the carbon dioxide capture coefficient, that is, the amount of carbon dioxide that can be captured per unit of energy consumed during the time period t, which is related to the carbon dioxide content in the air, is the power upper limit of the carbon capture system in the intelligent microgrid i;

[0096] The multi-load constraints of the intelligent microgrid include adjustable load constraints, shiftable load constraints, and bidirectional load constraints:

[0097] The adjustable load constraints include:

[0098] The adjustable power constraint of the adjustable load restricts that the power after adjusting the adjustable load shall not exceed the power before adjustment:

[0099]

[0100] The adjustable time period constraint of the adjustable load restricts that the adjustable load can only be adjusted within specific permitted time periods:

[0101]

[0102] The adjustable ratio constraint of the adjustable load restricts that the adjustment ratio of the adjustable load in any intelligent microgrid cannot exceed the lower limit at any time period:

[0103]

[0104] The adjustable upper limit constraint of the adjustable load restricts that the adjustment amount of the adjustable load within a typical day cannot exceed the upper limit:

[0105]

[0106] The shiftable load constraint is:

[0107]

[0108] According to the usage characteristics of different types of charging piles, AC slow charging piles are applied in residential intelligent microgrids, and DC fast charging piles are applied in industrial and commercial intelligent microgrids, making full use of the spatial flexibility of new energy sources and loads, considering the spatial transfer ability of electric vehicle charging loads between different microgrids. The bidirectional load constraints of the intelligent microgrid include:

[0109] The active power constraint of the electric vehicle restricts that the active power of the electric vehicle for forward charging and reverse discharging cannot exceed the upper limit of the service capacity of the intelligent charging piles in the substation area:

[0110]

[0111] The operating state constraint of electric vehicles restricts that for any intelligent microgrid at any time period, only pure forward charging or reverse discharging of electric vehicles is allowed, and simultaneous charging and discharging are not allowed:

[0112]

[0113] The spatial flexibility constraint of the charging load of electric vehicles. Due to the spatial flexibility of the charging load of electric vehicles, electric vehicles can choose a charging location within a certain range:

[0114]

[0115] In the above formula, is the actual load of the adjustable load in the intelligent microgrid i after optimization and adjustment during time period t, is the original load of the adjustable load in the intelligent microgrid i during time period t, is a binary variable indicating whether the power of the adjustable load can be adjusted. When it is allowed to adjust the power of the adjustable load, and when it is not allowed to adjust the power of the adjustable load. δ M is a large M constant, κ m is the lower limit of the adjustment ratio of the adjustable load in the intelligent microgrid, T U is the unit time, is the upper limit of the energy allowed to be adjusted for the adjustable load in the intelligent microgrid i within a typical day, is the actual shiftable load of the intelligent microgrid i during time period t, is the energy demand of the shiftable load in the intelligent microgrid i within a typical day, is the active power of forward charging of electric vehicles in the intelligent microgrid i during time period t, is the number of intelligent charging piles in the intelligent microgrid i, P SC is the rated active power of forward charging of an AC slow charging pile, α i is a binary variable of the type of electric vehicle charging pile. When α i = 1, the electric vehicle charging pile is a DC fast charging pile, and when α i = 0, the electric vehicle charging pile is an AC slow charging station, P FC is the rated active power of forward charging of a DC fast charging pile, is the active power of reverse discharging of electric vehicles in the intelligent microgrid i during time period t, P SD is the rated active power of reverse discharging of an AC slow charging pile, P FD is the rated active power of reverse discharging of a DC fast charging pile, is a binary variable for the charging and discharging state of an electric vehicle. When , the electric vehicle is in the forward charging state. When , the electric vehicle is in the reverse discharging state. is the number of electric vehicles with charging demand in the intelligent microgrid i during period t. E U is the energy demand for a single charging of an electric vehicle. is the number of electric vehicles initially charged in the intelligent microgrid i during period t. is the number of electric vehicles transferred between the intelligent microgrid i and the intelligent microgrid j during period t. When , the electric vehicle with charging demand is transferred from the intelligent microgrid i to the intelligent microgrid j. When , the electric vehicle with charging demand is transferred from the intelligent microgrid j to the intelligent microgrid i. is the maximum proportion of the number of electric vehicles that can be transferred in the intelligent microgrid i during period t;

[0116] Considers the influence of different period lighting conditions on the distributed PV output in the intelligent microgrid. The distributed PV system output constraint is:

[0117]

[0118] In the above formula, is the actual output of the distributed PV in the intelligent microgrid i during period t. is the installed capacity of the distributed PV in the intelligent microgrid i. is the output coefficient of the distributed PV during period t;

[0119] The energy storage system constraints include:

[0120] The charging and discharging power constraint of the energy storage system:

[0121]

[0122] The charging and discharging energy constraint of the energy storage system:

[0123]

[0124] The additional constraint conditions of the McCormick envelope method. For the convenience of solving, the McCormick envelope method is used to relax the problem, and the auxiliary variable A is introduced t,i to replace the bilinear term

[0125]

[0126] In the above formula, P MB is the maximum charging and discharging power of a single energy storage. is the number of energy storage systems in smart microgrid i, is the input or output power of the energy storage system in the smart microgrid i during time period t. When the energy storage system discharges, When the energy storage system is charged, are the lower and upper limits of the energy state of the energy storage system, BC ES is the capacity of a single energy storage system, EC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last period of a typical day, are the charging energy and discharging energy of the energy storage system in the smart microgrid i in time period t, is a binary variable of the energy storage system operating status. When the energy storage system discharges, When the energy storage system is charged, A t，i It is the auxiliary variable used in McCormick envelope method;

[0127] Taking into account the impact of multiple uncertain factors on various equipment in the smart microgrid, the impact of transformer load rate on service life in the smart microgrid area is considered. The constraints in the smart microgrid area include:

[0128] Power balance constraints within the smart microgrid area:

[0129]

[0130] Transformer load factor constraints in smart microgrid areas:

[0131]

[0132] Transformer operating state constraints:

[0133]

[0134] In the above formula, is the active power transmitted from the upper power grid to the smart microgrid i in time period t, are the charging energy and discharging energy of the mobile energy storage system in the smart microgrid i in time period t, T U is the unit time, is the fixed active load of smart microgrid i in time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, is the power of the energy storage system in the intelligent microgrid i during period t. is the actual output of the distributed PV in the intelligent microgrid i during period t. The operating power of the carbon capture system in the intelligent microgrid i during period t. is the capacity of the distribution transformer in the substation area of the intelligent microgrid i, φ L is the minimum load rate of the distribution transformer, φH is the maximum load rate of the distribution transformer, δ M is the large M constant. is the binary variable of the operating state of the distribution transformer in the intelligent microgrid i during period t, δ S is relative to is the extremely small constant. is the actual load rate of the distribution transformer in the intelligent microgrid i during period t, φ O is the rated load rate of the distribution transformer;

[0135] Considering the flexible allocation of mobile energy storage among different types of intelligent microgrids at different times, the operation constraints of mobile energy storage include:

[0136] The energy balance constraint of the mobile energy storage system:

[0137]

[0138] The capacity balance constraint of the mobile energy storage system:

[0139]

[0140] The working state constraint of the mobile energy storage system:

[0141]

[0142] The energy and capacity constraints of the transferred mobile energy storage:

[0143]

[0144] In the above formula, is the energy stored in the mobile energy storage system of the intelligent microgrid i during period t. is the energy transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j during period t. is the total capacity in the mobile energy storage system of the intelligent microgrid i during period t. are the lower and upper limits of the energy state of the energy storage system respectively. is the total capacity transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j during period t. is the maximum capacity of the mobile energy storage system that can be connected in the intelligent microgrid i. is a binary variable representing the working state of the mobile energy storage system in the intelligent microgrid i during period t. When the mobile energy storage system is in the charging state, and when the mobile energy storage system is in the discharging state;

[0145] Multiple intelligent microgrid substations are operated jointly. The operation constraints of the distribution network include:

[0146] Power transmission constraints of the distribution network lines:

[0147]

[0148] Node voltage constraints of the distribution network:

[0149]

[0150] In the above formula, are the active power and reactive power on the distribution network line w during period t, respectively. μ w，i is the correlation coefficient between the distribution network line w and the intelligent microgrid i. When μ w，i = 1, the distribution network line w is connected to the intelligent microgrid i. When μ w，i = 0, the distribution network line w is not connected to the intelligent microgrid i. is the active power transmitted from the superior power grid to the intelligent microgrid i during period t, is the fixed reactive power load of the intelligent microgrid i during period t, LP w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w during period t, are the resistance and reactance of the distribution network line w, respectively. U N is the rated voltage of the distribution network node, U t，a 、U t，b are the node voltages of the distribution network nodes a and b during period t, respectively. The nodes a and b are the two endpoints of the distribution network line w. U t,i is the node voltage of the intelligent microgrid i during period t, U m 、U M are the lower limit and upper limit of the distribution network node voltage, respectively.

[0151] 2. Perform simulation operations based on the MATLAB / CPLEX platform to solve the multi-intelligent microgrid joint optimization operation model and obtain the multi-intelligent microgrid joint optimization operation strategy;

[0152] The hardware device parameters of the simulation platform are set as: Intel Core i7-9750H, 32G RAM, 2.6GHz.

[0153] To verify the effectiveness of this method, the traditional microgrid optimal operation strategy is introduced as Strategy 2, and the strategy described in this method is used as Strategy 1. Both are applied to the 14-node distribution network example for comparison. Among them, Strategy 2 does not adopt a carbon capture system and does not optimize adjustable loads and shiftable loads. The economic comparison results of the two strategies are shown in Table 1:

[0154] Table 1 Economic results of two strategies

[0155]

[0156] Table 1 lists the annual power purchase cost, annual system operation cost, annual carbon trading revenue, and annual comprehensive cost when adopting the two strategies. It can be seen from Table 1 that when adopting Strategy 1, the annual power purchase cost is reduced by 17.81% compared with that when adopting Strategy 2, and an additional annual carbon trading revenue of 2.35×10 6 yuan is generated. From the perspective of the annual comprehensive cost, adopting Strategy 1 reduces it by 22.18% compared with adopting Strategy 2; the above results show that the strategy proposed by this method achieves the effect of reducing the system operation cost while ensuring the safe and stable operation of the microgrid.

[0157] Example 2:

[0158] As Figure 8 shown, a multi-intelligent microgrid joint optimal operation system considering carbon capture includes a joint optimal operation model construction module and a joint optimal operation model solution module;

[0159] The joint optimal operation model construction module is used to consider the differences of intelligent microgrids in different regions and the different characteristics of multiple loads in the intelligent microgrid, apply carbon capture technology to enable the operation mode of direct air capture of carbon dioxide during low electricity price periods and large-scale new energy periods, and construct a multi-intelligent microgrid joint optimal operation model with the goal of minimizing the comprehensive cost;

[0160] The joint optimal operation model solution module is used to solve the multi-intelligent microgrid joint optimal operation model to obtain the multi-intelligent microgrid joint optimal operation strategy.

[0161] The joint optimal operation model construction module includes an objective function construction unit;

[0162] The objective function construction unit is used to construct the objective function of the following multi-intelligent microgrid joint optimal operation model:

[0163] min C E +C F -I C ;

[0164]

[0165] C F = C P + C V + C B + C T ;

[0166]

[0167] In the above formula, C E is the power purchase cost for the smart microgrid to purchase electricity from the superior power grid, C F is the system operation cost, I C is the carbon trading revenue obtained by selling carbon sinks through carbon trading, d is the number of typical days in a year, is the active power transmitted from the superior power grid to the smart microgrid i during period t, is the fixed active load of the smart microgrid i during period t, i R and i I are the residential smart microgrid and the industrial and commercial smart microgrid respectively, is the residential electricity price during period t, is the industrial and commercial electricity price during period t, C P is the penalty cost for reasonably reducing the performance of adjustable load, C V is the subsidy cost for the electric vehicle to perform reverse discharging in V2G mode, C B is the cycle loss cost brought by the charge and discharge of the energy storage system, C T is the transformer loss cost when the distribution transformer in the substation area is operating at a high load rate, is the carbon sink obtained by the smart microgrid i through direct air capture of carbon dioxide during period t, PR C is the unit price of carbon trading, is the original load of the adjustable load in the smart microgrid i during period t, is the actual load of the adjustable load in the smart microgrid i after optimization and adjustment during period t, T U is the unit time, PR P is the unit penalty cost for adjusting the load, is the active power of the electric vehicle in the smart microgrid i performing reverse discharging in V2G during period t, PR DI is the unit subsidy price for the reverse discharging of the electric vehicle, are the charging energy and discharging energy of the energy storage system in the smart microgrid i during period t respectively, PR CY is the unit cycle loss cost of the energy storage system, is the binary variable of the operation state of the distribution transformer in the smart microgrid i during period t, PR OL is the unit loss cost when the smart microgrid transformer is operating at a high load rate.

[0168] The combined optimization operation model construction module further includes a carbon capture system constraint construction unit and a multi - load constraint construction unit for the intelligent microgrid;

[0169] The carbon capture system constraint construction unit is used to construct the following carbon capture system constraints:

[0170]

[0171] In the above formula, is the carbon sink obtained by the intelligent microgrid i through direct air capture of carbon dioxide during period t, is the operating power of the carbon capture system in the intelligent microgrid i during period t, is the amount of carbon dioxide that can be captured per unit of energy consumed during period t, is the power upper limit of the carbon capture system in the intelligent microgrid i;

[0172] The multi - load constraint construction unit for the intelligent microgrid is used to construct the following multi - load constraints for the intelligent microgrid:

[0173]

[0174] In the above formula, is the actual load of the adjustable load in the intelligent microgrid i after optimized adjustment during period t, is the original load of the adjustable load in the intelligent microgrid i during period t, is a binary variable indicating whether the power of the adjustable load can be adjusted, δ M is a large M constant, κ m is the lower limit of the adjustment ratio of the adjustable load in the intelligent microgrid, T U is the unit time, is the upper limit of the energy that the adjustable load in the intelligent microgrid i is allowed to adjust within a typical day, is the actual shiftable load of the intelligent microgrid i during period t, is the energy demand of the shiftable load in the intelligent microgrid i within a typical day, is the active power of the electric vehicle for forward charging in the intelligent microgrid i during period t, is the number of intelligent charging piles in the intelligent microgrid i, P SC is the rated active power of the AC slow charging pile for forward charging, α i is a binary variable of the electric vehicle charging pile type, P FC is the rated active power of the DC fast charging pile for forward charging, is the active power of the electric vehicle for reverse discharging in the intelligent microgrid i during period t, P SD is the rated active power of the AC slow charging pile for reverse discharging, P FDis the rated active power of the DC fast charging pile for reverse power discharge, is a binary variable of the charging and discharging state of the electric vehicle, is the number of electric vehicles with charging demand in the intelligent microgrid i during period t, E U is the energy demand for a single charge of the electric vehicle, is the number of electric vehicles initially charged in the intelligent microgrid i during period t, is the number of electric vehicles transferred between the intelligent microgrid i and the intelligent microgrid j during period t, is the maximum proportion of the number of electric vehicles that can be transferred in the intelligent microgrid i during period t.

[0175] The joint optimization model construction module further includes a distributed photovoltaic system output constraint construction unit and a energy storage system constraint construction unit;

[0176] The distributed photovoltaic system output constraint construction unit is used to construct the following distributed photovoltaic system output constraints:

[0177]

[0178] The energy storage system constraint construction unit is used to construct the following energy storage system constraints:

[0179]

[0180] In the above formula, is the actual output of the distributed photovoltaic in the intelligent microgrid i during period t, is the installed capacity of the distributed photovoltaic in the intelligent microgrid i, is the output coefficient of the distributed photovoltaic during period t, P MB is the maximum charge and discharge power of a single energy storage, is the number of energy storage systems in the intelligent microgrid i, is the power of the energy storage system in the intelligent microgrid i during period t, are the lower and upper limits of the energy state of the energy storage system, BC ES is the capacity of a single energy storage system, BC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last period in a typical day, are the charging energy and discharging energy of the energy storage system in the intelligent microgrid i during period t, is a binary variable of the operating state of the energy storage system, A t,i is an auxiliary variable.

[0181] The joint optimization operation model construction module further includes an in-intelligent-microgrid substation area constraint construction module and a mobile energy storage operation constraint construction module;

[0182] The constraint construction unit in the intelligent microgrid substation area is used to construct the following constraints in the intelligent microgrid substation area:

[0183]

[0184] In the above formula, is the active power transmitted from the superior power grid to the intelligent microgrid i during period t, are the charging energy and discharging energy of the mobile energy storage system in the intelligent microgrid i during period t, respectively. T U is the unit time, is the fixed active load of the intelligent microgrid i during period t, is the actual load of the adjustable load in the intelligent microgrid i after optimization and adjustment during period t, is the active power of the electric vehicle charging forward in the intelligent microgrid i during period t, is the active power of the electric vehicle discharging backward using V2G in the intelligent microgrid i during period t, is the power of the energy storage system in the intelligent microgrid i during period t, is the actual output of the distributed photovoltaic in the intelligent microgrid i during period t, The operating power of the carbon capture system in the intelligent microgrid i during period t, is the capacity of the distribution transformer in the substation area of the intelligent microgrid i. φL is the minimum load rate of the distribution transformer, φH is the maximum load rate of the distribution transformer, and δ M is the large M constant, is the binary variable of the operating state of the distribution transformer in the intelligent microgrid i during period t, δ S is relative to the minimum constant, is the actual load rate of the distribution transformer in the intelligent microgrid i during period t, φ O is the rated load rate of the distribution transformer;

[0185] The mobile energy storage operation constraint construction unit is used to construct the following mobile energy storage operation constraints:

[0186]

[0187]

[0188] In the above formula, is the energy stored in the mobile energy storage system of the intelligent microgrid i during period t, is the energy transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j during period t, is the total capacity in the mobile energy storage system of the intelligent microgrid i during period t, They are the lower limit and the upper limit of the energy state of the energy storage system respectively. It is the total capacity transferred from the mobile energy storage system of the intelligent microgrid i to the intelligent microgrid j within the time period t. It is the maximum capacity of the mobile energy storage system that can be connected in the intelligent microgrid i. It is the binary variable of the working state of the mobile energy storage system in the intelligent microgrid i within the time period t.

Claims

1. A multi-intelligent microgrid joint optimization operation method considering carbon capture, characterized in that: The method comprises: S1. Considering the differences of smart microgrids in different regions and the different characteristics of multiple loads in smart microgrids, carbon capture technology is applied to enable the operation mode of direct air capture of carbon dioxide during low electricity price periods and new energy peak periods, with the goal of minimizing the overall cost, to build a joint optimization operation model for multiple smart microgrids; S2. Solve the joint optimization operation model of multiple intelligent microgrids and obtain the joint optimization operation strategy of multiple intelligent microgrids.

2. A multi-intelligent microgrid joint optimization operation method considering carbon capture according to claim 1, characterized in that: In S1, the objective function of the multi-intelligent microgrid joint optimization operation model includes: my C E +C F -IN C ; C F =C P +C V +C B +C T ; In the above formula, C E is the electricity purchase cost of the smart microgrid from the upper power grid, C F is the system operating cost, I C is the carbon trading income obtained by selling carbon sinks through carbon trading, d is the typical daily number in a year, is the active power transmitted from the upper power grid to the smart microgrid i in time period t, is the fixed active load of smart microgrid i in time period t, i R 、i I They are residential smart microgrid and industrial and commercial smart microgrid. is the residential electricity price in period t, is the electricity price in the industrial and commercial area during period t, C P In order to reasonably reduce the penalty cost of adjustable load performance, C V Subsidy cost for electric vehicles to use V2G mode for reverse discharge, C B The cycle loss cost caused by charging and discharging of the energy storage system, C T The transformer loss cost when the distribution transformer in the substation is in a high load rate operation state. is the carbon sink obtained by smart microgrid i through direct air capture of carbon dioxide in period t, PR C is the unit price of carbon trading, is the original load of the adjustable load in the smart microgrid i during time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, T U For unit time, PR P The unit penalty cost for adjusting load is is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, PR DI is the unit subsidy price for reverse discharge of electric vehicles, are the charging energy and discharging energy of the energy storage system in the smart microgrid i in time period t, PR CY is the unit cycle loss cost of the energy storage system, is the binary variable of the operating status of the distribution transformer in the smart microgrid i in time period t, PR OL It is the unit loss cost of the smart microgrid transformer when it is working at a high load rate.

3. The method for joint optimization operation of multiple intelligent microgrids considering carbon capture according to claim 1 is characterized in that: In S1, the constraints of the multi-intelligent microgrid joint optimization operation model include carbon capture system constraints and multi-load constraints of the intelligent microgrid; The carbon capture system constraints include: In the above formula, is the carbon sink obtained by smart microgrid i through direct air capture of carbon dioxide in period t, is the operating power of the carbon capture system in the smart microgrid i during period t, is the amount of carbon dioxide that can be captured per unit of energy consumed in time period t, is the power upper limit of the carbon capture system in the smart microgrid i; The multi-load constraints of the smart microgrid include: In the above formula, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the original load of the adjustable load in the smart microgrid i during time period t, is a binary variable that determines whether the load power can be adjusted, δ M is the large M constant, κ m is the lower limit of the regulation ratio of the adjustable load in the smart microgrid, T U is the unit time, is the upper limit of the energy that can be adjusted by the adjustable load in the smart microgrid i in a typical day, is the actual transferable load of smart microgrid i in time period t, is the energy demand of the shiftable load in the smart microgrid i in a typical day, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the number of smart charging piles in smart microgrid i, P SC is the rated active power of the AC slow charging pile in the forward direction, α i is a binary variable for the type of electric vehicle charging station, P FC is the rated active power of the DC fast charging pile in the forward direction. is the active power of reverse discharge of electric vehicles in smart microgrid i during time period t, P SD is the rated active power of the AC slow charging pile in reverse discharge, P FD is the rated active power of the DC fast charging pile in reverse discharge, is a binary variable of the charging and discharging status of the electric vehicle, is the number of electric vehicles with charging demand in smart microgrid i during time period t, E U The energy required for a single charge of an electric vehicle, is the number of electric vehicles initially charged in smart microgrid i during time period t, is the number of electric vehicles transferred between smart microgrid i and smart microgrid j in time period t, It is the maximum proportion of electric vehicles that can be transferred in smart microgrid i within time period t.

4. The method for joint optimization operation of multiple intelligent microgrids considering carbon capture according to claim 1 is characterized in that: In S1, the constraints of the multi-intelligent microgrid joint optimization operation model also include distributed photovoltaic system output constraints and energy storage system constraints; The output constraint of the distributed photovoltaic system is: The energy storage system constraints include: In the above formula, is the actual output of distributed photovoltaic in smart microgrid i in time period t, is the installed capacity of distributed photovoltaics in the smart microgrid i, is the output coefficient of distributed photovoltaic in time period t, P MB is the maximum charge and discharge power of a single energy storage unit, is the number of energy storage systems in smart microgrid i, is the power of the energy storage system in the smart microgrid i during time period t, are the lower and upper limits of the energy state of the energy storage system, BC ES is the capacity of a single energy storage system, BC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last period of a typical day, are the charging energy and discharging energy of the energy storage system in the smart microgrid i in time period t, is a binary variable of the energy storage system operation status, A t，i is an auxiliary variable.

5. The method for joint optimization operation of multiple intelligent microgrids considering carbon capture according to claim 1 is characterized in that: In S1, the constraints of the multi-intelligent microgrid joint optimization operation model also include constraints within the intelligent microgrid station area and mobile energy storage operation constraints; The constraints within the smart microgrid area include: In the above formula, is the active power transmitted from the upper power grid to the smart microgrid i in time period t, are the charging energy and discharging energy of the mobile energy storage system in the smart microgrid i in time period t, T U is the unit time, is the fixed active load of smart microgrid i in time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, is the power of the energy storage system in the smart microgrid i during time period t, is the actual output of distributed photovoltaic in smart microgrid i in time period t, The operating power of the carbon capture system in the smart microgrid i during time period t, is the capacity of the distribution transformer in the smart microgrid i, φ L is the minimum load factor of the distribution transformer, φ H is the maximum load factor of the distribution transformer, δ M is the large M constant, is the binary variable of the operating status of the distribution transformer in the smart microgrid i in time period t, δ S for relative to The smallest constant, is the actual load rate of the distribution transformer in the smart microgrid i during time period t, φ O is the rated load rate of the distribution transformer; The mobile energy storage operation constraints include: In the above formula, is the energy stored in the mobile energy storage system of smart microgrid i in time period t, is the energy transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the total capacity of the mobile energy storage system of smart microgrid i in time period t, are the lower and upper limits of the energy state of the energy storage system, respectively. is the total capacity transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the maximum capacity of the mobile energy storage system that can be connected to the smart microgrid i, It is a binary variable representing the working status of the mobile energy storage system in the smart microgrid i during time period t.

6. A multi-intelligent microgrid joint optimization operation system considering carbon capture, characterized in that: The system includes a joint optimization operation model building module and a joint optimization operation model solving module; The joint optimization operation model building module is used to consider the differences of smart microgrids in different regions and the different characteristics of multiple loads in the smart microgrid, apply carbon capture technology to enable the operation mode of direct air capture of carbon dioxide during low electricity price periods and new energy peak periods, and build a joint optimization operation model for multiple smart microgrids with the goal of minimizing comprehensive costs; The joint optimization operation model solving module is used to solve the multi-intelligent microgrid joint optimization operation model and obtain the multi-intelligent microgrid joint optimization operation strategy.

7. A multi-intelligent microgrid joint optimization operation system considering carbon capture according to claim 6, characterized in that: The joint optimization operation model construction module includes an objective function construction unit; The objective function construction unit is used to construct the objective function of the following multi-intelligent microgrid joint optimization operation model: my C E +C F -IN C ; C F =C P +C V +C B +C T ; In the above formula, C E is the electricity purchase cost of the smart microgrid from the upper power grid, C F is the system operating cost, I C is the carbon trading income obtained by selling carbon sinks through carbon trading, d is the typical daily number in a year, is the active power transmitted from the upper power grid to the smart microgrid i in time period t, is the fixed active load of smart microgrid i in time period t, i R 、i I They are residential smart microgrid and industrial and commercial smart microgrid. is the residential electricity price in period t, is the electricity price in the industrial and commercial area during period t, C P In order to reasonably reduce the penalty cost of adjustable load performance, C V Subsidy cost for electric vehicles to use V2G mode for reverse discharge, C B The cycle loss cost caused by charging and discharging of the energy storage system, C T The transformer loss cost when the distribution transformer in the substation is in a high load rate operation state. is the carbon sink obtained by smart microgrid i through direct air capture of carbon dioxide in period t, PR C is the unit price of carbon trading, is the original load of the adjustable load in the smart microgrid i during time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, T U For unit time, PR P The unit penalty cost for adjusting load is is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, PR DI is the unit subsidy price for reverse discharge of electric vehicles, are the charging energy and discharging energy of the energy storage system in the smart microgrid i in time period t, PR CY is the unit cycle loss cost of the energy storage system, is the binary variable of the operating status of the distribution transformer in the smart microgrid i in time period t, PR OL It is the unit loss cost of the smart microgrid transformer when it is working at a high load rate.

8. The multi-intelligent microgrid joint optimization operation system considering carbon capture according to claim 6 is characterized in that: The joint optimization operation model construction module also includes a carbon capture system constraint construction unit and a multi-load constraint construction unit of the smart microgrid; The carbon capture system constraint construction unit is used to construct the following carbon capture system constraints: In the above formula, is the carbon sink obtained by smart microgrid i through direct air capture of carbon dioxide in period t, is the operating power of the carbon capture system in the smart microgrid i during period t, is the amount of carbon dioxide that can be captured per unit of energy consumed in time period t, is the power upper limit of the carbon capture system in the smart microgrid i; The multi-load constraint construction unit of the smart microgrid is used to construct the following multi-load constraints of the smart microgrid: In the above formula, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the original load of the adjustable load in the smart microgrid i during time period t, is a binary variable that determines whether the load power can be adjusted, δ M is the large M constant, κ m is the lower limit of the regulation ratio of the adjustable load in the smart microgrid, T U is the unit time, is the upper limit of the energy that can be adjusted by the adjustable load in the smart microgrid i in a typical day, is the actual transferable load of smart microgrid i in time period t, is the energy demand of the shiftable load in the smart microgrid i in a typical day, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the number of smart charging piles in smart microgrid i, P SC is the rated active power of the AC slow charging pile in the forward direction, α i is a binary variable for the type of electric vehicle charging station, P FC is the rated active power of the DC fast charging pile in the forward direction. is the active power of reverse discharge of electric vehicles in smart microgrid i during time period t, P SD is the rated active power of the AC slow charging pile in reverse discharge, P FD is the rated active power of the DC fast charging pile in reverse discharge, is a binary variable of the charging and discharging status of the electric vehicle, is the number of electric vehicles with charging demand in smart microgrid i during time period t, E U The energy required for a single charge of an electric vehicle, is the number of electric vehicles initially charged in smart microgrid i during time period t, is the number of electric vehicles transferred between smart microgrid i and smart microgrid j in time period t, It is the maximum proportion of electric vehicles that can be transferred in smart microgrid i within time period t.

9. The multi-intelligent microgrid joint optimization operation system considering carbon capture according to claim 6 is characterized in that: The joint optimization model construction module also includes a distributed photovoltaic system output constraint construction unit and an energy storage system constraint construction unit; The distributed photovoltaic system output constraint construction unit is used to construct the following distributed photovoltaic system output constraint: The energy storage system constraint construction unit is used to construct the following energy storage system constraints: In the above formula, is the actual output of distributed photovoltaic in smart microgrid i in time period t, is the installed capacity of distributed photovoltaics in the smart microgrid i, is the output coefficient of distributed photovoltaic in time period t, P MB is the maximum charge and discharge power of a single energy storage unit, is the number of energy storage systems in smart microgrid i, is the power of the energy storage system in the smart microgrid i during time period t, are the lower and upper limits of the energy state of the energy storage system, BC ES is the capacity of a single energy storage system, BC O is the initial capacity of a single energy storage system, T U is the unit time, tn is the last period of a typical day, are the charging energy and discharging energy of the energy storage system in the smart microgrid i in time period t, is a binary variable of the energy storage system operation status, A t，i is an auxiliary variable.

10. The multi-intelligent microgrid joint optimization operation system considering carbon capture according to claim 6, characterized in that: The joint optimization operation model construction module also includes a smart microgrid area constraint construction module and a mobile energy storage operation constraint construction module; The smart microgrid area constraint building unit is used to build the following smart microgrid area constraints: In the above formula, is the active power transmitted from the upper power grid to the smart microgrid i in time period t, are the charging energy and discharging energy of the mobile energy storage system in the smart microgrid i in time period t, T U is the unit time, is the fixed active load of smart microgrid i in time period t, is the actual load after optimization and adjustment of the adjustable load in the smart microgrid i in time period t, is the active power of the electric vehicle in the smart microgrid i in the forward charging state during time period t, is the active power of the electric vehicle in the smart microgrid i using V2G for reverse discharge in time period t, is the power of the energy storage system in the smart microgrid i during time period t, is the actual output of distributed photovoltaic in smart microgrid i in time period t, The operating power of the carbon capture system in the smart microgrid i during time period t, is the capacity of the distribution transformer in the smart microgrid i, φ L is the minimum load factor of the distribution transformer, φ H is the maximum load factor of the distribution transformer, δ M is the large M constant, is the binary variable of the operating status of the distribution transformer in the smart microgrid i in time period t, δ S for relative to The smallest constant, is the actual load rate of the distribution transformer in the smart microgrid i during time period t, φ O is the rated load rate of the distribution transformer; The mobile energy storage operation constraint construction unit is used to construct the following mobile energy storage operation constraints: In the above formula, is the energy stored in the mobile energy storage system of smart microgrid i in time period t, is the energy transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the total capacity of the mobile energy storage system of smart microgrid i in time period t, are the lower and upper limits of the energy state of the energy storage system, respectively. is the total capacity transferred from the mobile energy storage system of smart microgrid i to smart microgrid j during time period t, is the maximum capacity of the mobile energy storage system that can be connected to the smart microgrid i, It is a binary variable representing the working status of the mobile energy storage system in the smart microgrid i during time period t.

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