Low-carbon resident area optimization operation method and system considering short-time island mode

Abstract

According to the low-carbon resident area optimization operation method and system considering the short-time island mode, the short-time island mode of the low-carbon resident area is considered, and an optimization operation model of the low-carbon resident area is constructed with the lowest comprehensive operation cost as the target; the low-carbon resident area comprises a miniature data center, an electric automobile, a fuel automobile, a renewable direct air trapping system, a distributed photovoltaic system and an energy storage system; and solving the optimized operation model of the low-carbon resident area to obtain an optimized operation scheme of the low-carbon resident area. From the perspective of an island mode under a short-time fault condition, the operation capability of a low-carbon resident area is considered, carbon dioxide in ambient air is directly absorbed in a low-electricity-price period and a new energy high-output period by utilizing a renewable direct air capturing system, and an absorption medium is recycled through a regeneration process, so that the energy consumption is reduced, and the energy consumption is reduced. The carbon dioxide released by regeneration is sold and collected to obtain profits, and the operation cost of the system is reduced while safe and stable operation of the low-carbon resident area is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of optimized operation of residential substations, and particularly relates to a method and system for optimizing the operation of a low-carbon residential substation considering the short-term islanding mode. Background Art

[0002] Nowadays, countries around the world pay great attention to carbon emission control, and environmental protection and climate change have become important issues faced by the international community. In order to address climate change, reduce greenhouse gas emissions, and promote the popularization of green energy, countries have successively formulated strict carbon emission standards. This trend not only affects national policy-making but also directly impacts the decisions of enterprises and local governments. Especially with the rise of new energy and the popularization of electric vehicles, the task of reducing carbon emissions has become even more urgent. With the development of emerging technologies such as data centers, electric vehicles, and distributed power sources, traditional distribution networks are facing unprecedented opportunities and challenges. These emerging technologies have promoted the diversification of energy sources and also exacerbated the load fluctuations and complexity of distribution networks. For example, the scale of energy demand in data centers is increasing, the real-time scheduling requirements for electric vehicle charging stations are getting higher, and the penetration rate of distributed generation into traditional power grids is increasing. All these phenomena require distribution networks to operate more flexibly and efficiently while ensuring safety and stability.

[0003] How to reduce carbon emissions while ensuring the stable and efficient operation of the distribution network has become an important issue in power system planning and operation. Among them, as the basic unit of the distribution network, optimizing the energy efficiency and carbon emissions of the substation not only helps to improve the overall operation efficiency of the power grid but also contributes to achieving local green development goals. Through intelligent substation energy dispatching and reasonable allocation of clean energy, it is possible to reduce the dependence on traditional energy sources without affecting the safety of the power grid, thereby effectively reducing carbon emissions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the operation of a low-carbon residential substation considering the short-term islanding mode in view of the above problems existing in the prior art.

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

[0006] In the first aspect, the present invention proposes a method for optimizing the operation of a low-carbon residential substation considering the short-term islanding mode, including:

[0007] S1. Considering the short-term islanding mode of the low-carbon residential substation, an optimized operation model of the low-carbon residential substation is constructed with the goal of minimizing the comprehensive operation cost. The low-carbon residential substation includes a micro data center, electric vehicles and fuel vehicles, a renewable direct air capture system, a distributed photovoltaic system, and an energy storage system;

[0008] S2. Solve the optimal operation model of the low-carbon residential area to obtain the optimal operation plan of the low-carbon residential area.

[0009] In the above-mentioned S1, the objective function of the optimal operation model includes:

[0010] min C P +C O -I C ;

[0011]

[0012] C O =C W +C S +C G ;

[0013]

[0014] In the above formula, C P is the electricity purchase cost for the operation of the residential area, C O is the operation cost of other systems, I C is the income from selling carbon dioxide after capture, is the active power output from the superior power grid to the e-th low-carbon residential area at time t in season s, is the basic active load of the e-th low-carbon residential area at time t in season s, is the electricity price at time t, d s is the number of typical days in season s, C W is the water-side cooling cost in the micro data, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of the fuel vehicle, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area at time t in season s, T U is the unit time, ψ DA is the amount of carbon dioxide obtained per unit of energy consumed during carbon dioxide regeneration and collection, PR C is the unit price of carbon dioxide sold, is the cooling capacity generated by water-side cooling of the micro data center in the e-th low-carbon residential area at time t in season s, ηW 为 is the water evaporation amount per unit of cooling capacity obtained, PR W is the unit water evaporation cost when using water-side cooling, are respectively the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential area at time t in season s, PR CY is the unit cycle loss cost of the energy storage system, is the number of trips of fuel vehicles in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, $L$ s，t is the average driving mileage of a single trip demand during time period $t$ in season $s$, is the fuel consumption per unit mileage of fuel vehicles during time period $t$ in season $s$, $PR$ G is the unit cost of fuel.

[0015] In the above-mentioned S1, the constraint conditions for optimizing the operation model include the constraints under the short-term island mode of the low-carbon residential distribution area and the operation constraints of the superior distribution network;

[0016] The constraints under the short-term island mode include:

[0017]

[0018] The operation constraints of the superior distribution network include:

[0019]

[0020] In the above formula, is the discharge power when the energy storage system in the $e$-th low-carbon residential distribution area operates in island mode during time period $t$ in season $s$, is the basic active load of the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the fixed operation load of the micro data center in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the actual output of distributed photovoltaics in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the installed capacity of the energy storage system in the $e$-th low-carbon residential distribution area, $\tau$ O is the initial energy state of the energy storage system, is the actual power of the energy storage system in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, $T$ U is the unit time, is the lower limit of the energy state of the energy storage system, $T$ I is the operation duration of the island mode, $t_x$ is any time period within a typical day, and $t_n$ is the last time period of a typical day, is the active power output from the superior distribution network to the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, are respectively the active power and reactive power transmitted on distribution network line $k$ during time period $t$ in season $s$, $\mu$ k，e is the binary coefficient of the connection relationship between distribution network line $k$ and the $e$-th low-carbon residential distribution area, is the basic reactive load of the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, $\Delta U$ s，t，k is the voltage drop on distribution network line $k$ during time period $t$ in season $s$, Resistance and reactance of distribution network line k respectively, U N is the rated voltage of the distribution network node.

[0021] In S1, the constraint conditions of the optimal operation model also include the micro data center constraint of the low-carbon residential area, the vehicle usage demand constraint, and the operation constraint of the renewable direct air capture system;

[0022] The micro data center constraint includes:

[0023]

[0024]

[0025] In the above formula, is the fixed operating load of the e-th low-carbon residential area micro data center during time period t in season s, is the cooling demand coefficient of the micro data center load in season s, is the cooling capacity generated by air-conditioning cooling of the e-th low-carbon residential area micro data center during time period t in season s, is the cooling capacity generated by water-side cooling of the e-th low-carbon residential area micro data center during time period t in season s, is the active power consumed by air-conditioning cooling of the e-th low-carbon residential area micro data center during time period t in season s, η A is the energy efficiency ratio of air-conditioning cooling of the micro data center, is the capacity of the air-conditioning cooling system of the e-th low-carbon residential area micro data center;

[0026] The vehicle usage demand constraint includes:

[0027]

[0028] In the above formula, is the total vehicle usage demand of the e-th low-carbon residential area during time period t in season s, is the number of electric vehicles used in the e-th low-carbon residential area during time period t in season s, is the number of fuel vehicles used in the e-th low-carbon residential area during time period t in season s, is the electric vehicle charging load of the e-th low-carbon residential area during time period t in season s, T U is the unit time, L s，t is the average driving mileage of a single trip demand during time period t in season s, E U is the unit energy consumption of the electric vehicle, is the installed capacity of the electric vehicle charging pile in the e-th low-carbon residential area;

[0029] The operating constraints of the renewable direct air capture system include:

[0030]

[0031] In the above formula, is the power of the absorption process of the renewable direct air capture system in the e-th low-carbon residential area during time period t in season s, is the installed capacity of the absorption fan in the renewable direct air capture system of the e-th low-carbon residential area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area during time period t in season s, is the installed capacity of the regeneration heating device in the renewable direct air capture system of the e-th low-carbon residential area, is the amount of carbon dioxide absorbed by the renewable direct air capture system in the e-th low-carbon residential area during time period t in season s, ψ DA is the carbon dioxide conversion coefficient of the absorption process, is the amount of carbon dioxide regenerated by the renewable direct air capture system in the e-th low-carbon residential area during time period t in season s, Ψ DR is the carbon dioxide conversion coefficient of the regeneration process, is the maximum amount of carbon dioxide that the carbon dioxide absorption medium in the renewable direct air capture system of the e-th low-carbon residential area can absorb. tx is any time period within a typical day, and tn is the last time period of a typical day.

[0032] In S1, the constraint conditions of the optimal operation model also include the energy storage system constraint and the distributed photovoltaic system constraint of the low-carbon residential area;

[0033] The energy storage system constraint includes:

[0034]

[0035] The distributed photovoltaic system constraint includes:

[0036]

[0037] In the above formula, is the actual power of the energy storage system in the e-th low-carbon residential area during time period t in season s, is the discharge power of the energy storage system in the e-th low-carbon residential area during time period t in season s, is the charging power of the energy storage system in the e-th low-carbon residential area during time period t in season s, is the binary operating state variable of the energy storage system in the e-th low-carbon residential area during time period t in season s, δ M is the large M constant, is the upper limit of the charging power of the energy storage system for the e-th low-carbon residential area is the upper limit of the discharging power of the energy storage system for the e-th low-carbon residential area is the lower limit of the energy state of the energy storage system is the installed capacity of the energy storage system for the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, T U is the unit time is the upper limit of the energy state of the energy storage system, tx is any time period within a typical day, and tn is the last time period of a typical day are the charging energy and discharging energy of the energy storage system for the e-th low-carbon residential area during the time period t in season s, respectively is the actual output of the distributed PV in the e-th low-carbon residential area during the time period t in season s is the installed capacity of the distributed PV in the e-th low-carbon residential area is the output coefficient of the distributed PV during the time period t is the seasonal coefficient of PV power generation in season s

[0038] In the second aspect, the present invention proposes an optimized operation system for a low-carbon residential area considering a short-term islanding mode, including an optimized operation model construction module and an optimized operation model solving module;

[0039] The optimized operation model construction module is used to consider the short-term islanding mode of the low-carbon residential area and construct an optimized operation model of the low-carbon residential area with the goal of minimizing the comprehensive operation cost. The low-carbon residential area includes a micro data center, electric vehicles and fuel vehicles, a renewable direct air capture system, a distributed PV system, and an energy storage system;

[0040] The optimized operation model solving module is used to solve the optimized operation model of the low-carbon residential area to obtain an optimized operation plan for the low-carbon residential area.

[0041] The optimized operation model construction module includes an objective function construction unit;

[0042] The objective function construction unit is used to construct the objective function of the following optimized operation model:

[0043] min C P +C O -I C ;

[0044]

[0045] C O =C W +C S +C G ;

[0046]

[0047] In the above formula, C P is the electricity purchase cost for the operation of the substation area, C O is the operation cost of other systems, I C is the revenue from selling carbon dioxide after capture, is the active power output from the superior power grid to the e-th low-carbon residential substation area within the time period t in season s, is the basic active load of the e-th low-carbon residential substation area within the time period t in season s, is the electricity price at time period t, d s is the number of typical days in season s, C W is the water-side cooling cost in the micro data, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of the fuel vehicle, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential substation area within the time period t in season s, T U is the unit time, ψ DA is the amount of carbon dioxide obtained per unit of energy consumed during carbon dioxide regeneration and collection, PR C is the unit price of selling carbon dioxide, is the cooling capacity generated by using water-side cooling in the micro data center of the e-th low-carbon residential substation area within the time period t in season s, η W is the water evaporation amount for obtaining unit cooling capacity, PR W is the unit water evaporation cost when using water-side cooling, are respectively the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential substation area within the time period t in season s, PR CY is the unit cycle loss cost of the energy storage system, is the number of trips of the fuel vehicle in the e-th low-carbon residential substation area within the time period t in season s, L s，t is the average driving mileage of a single trip demand within the time period t in season s, is the fuel consumption per unit mileage of the fuel vehicle when driving in season s, PR G is the unit cost of fuel.

[0048] The optimization operation model construction module further includes a constraint construction unit under the short-time island mode and a superior distribution network operation constraint construction unit;

[0049] The constraint construction unit under the short-time island mode is used to construct the following constraints under the short-time island mode:

[0050]

[0051] The upper-level distribution network operation constraint construction unit is used to construct the following upper-level distribution network operation constraints:

[0052]

[0053] In the above formula, is the discharge power of the energy storage system of the e-th low-carbon residential area at time t in season s when operating in island mode, is the basic active load of the e-th low-carbon residential area at time t in season s, is the fixed operating load of the micro data center in the e-th low-carbon residential area at time t in season s, is the actual output of the distributed photovoltaic in the e-th low-carbon residential area at time t in season s, is the installed capacity of the energy storage system in the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, is the actual power of the energy storage system in the e-th low-carbon residential area at time t in season s, T U is the unit time, is the lower limit of the energy state of the energy storage system, T I is the operating duration of the island mode, tx is any time period within a typical day, and tn is the last time period of a typical day, is the active power output from the upper-level distribution network to the e-th low-carbon residential area at time t in season s, are the active power and reactive power transmitted on the distribution network line k at time t in season s, respectively, μ k，e is the binary coefficient of the connection relationship between the distribution network line k and the t-th low-carbon residential area, is the basic reactive load of the e-th low-carbon residential area at time e in season s, ΔU s，t， k is the voltage drop on the distribution network line k at time t in season s, are the resistance and reactance of the distribution network line k, respectively, U N is the rated voltage of the distribution network node.

[0054] The optimization operation model construction module further includes a micro data center constraint construction unit, a vehicle usage demand constraint construction unit, and a renewable direct air capture system operation constraint construction unit;

[0055] The micro data center constraint construction unit is used to construct the following micro data center constraints:

[0056]

[0057] In the above formula, is the fixed operating load of the e-th low-carbon residential area micro data center during the time period t in season s, is the cooling demand coefficient of the micro data center load in season s, is the cooling capacity generated by the air-conditioning cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s, is the cooling capacity generated by the water-side cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s, is the active power consumed by the air-conditioning cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s, η A is the energy efficiency ratio of the air-conditioning cooling of the micro data center, is the capacity of the air-conditioning cooling system of the micro data center in the e-th low-carbon residential area;

[0058] The vehicle usage demand constraint construction unit is used to construct the following vehicle usage demand constraints:

[0059]

[0060] In the above formula, is the total vehicle usage demand in the e-th low-carbon residential area during the time period t in season s, is the number of electric vehicles used in the e-th low-carbon residential area during the time period t in season s, is the number of fuel vehicles used in the e-th low-carbon residential area during the time period t in season s, is the charging load of electric vehicles in the e-th low-carbon residential area during the time period t in season s, T U is the unit time, L s，t is the average driving mileage of a single trip demand in the e-th low-carbon residential area during the time period t in season s, E U is the unit energy consumption of electric vehicles, is the installed capacity of the electric vehicle charging pile in the e-th low-carbon residential area;

[0061] The renewable direct air capture system operation constraint construction unit is used to construct the following renewable direct air capture system operation constraints:

[0062]

[0063] In the above formula, is the power of the absorption process of the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, is the installed capacity of the absorption fan in the renewable direct air capture system in the e-th low-carbon residential area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, $P_{r,e}$ is the installed capacity of the regeneration heating device in the renewable direct air capture system of the $e$-th low-carbon residential area $C_{e,s,t}^{\text{abs}}$ is the amount of carbon dioxide absorbed by the renewable direct air capture system in the $e$-th low-carbon residential area during time period $t$ in season $s$, $\psi$ DA $\alpha$ is the carbon dioxide conversion coefficient during the absorption process $C_{e,s,t}^{\text{reg}}$ is the amount of carbon dioxide regenerated by the renewable direct air capture system in the $e$-th low-carbon residential area during time period $t$ in season $s$, $\psi$ DR $\beta$ is the carbon dioxide conversion coefficient during the regeneration process $C_{max,e}$ is the maximum amount of carbon dioxide that the carbon dioxide absorption medium in the renewable direct air capture system of the $e$-th low-carbon residential area can absorb. $t_x$ is any time period within a typical day, and $t_n$ is the last time period of a typical day.

[0064] The optimization operation model construction module further includes a energy storage system constraint construction unit and a distributed photovoltaic system constraint construction unit;

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

[0066]

[0067]

[0068] The distributed photovoltaic system constraint construction unit is used to construct the following distributed photovoltaic system constraints:

[0069]

[0070] In the above formula, $P_{e,s,t}^{\text{ess}}$ is the actual power of the energy storage system in the $e$-th low-carbon residential area during time period $t$ in season $s$ $P_{e,s,t}^{\text{disch}}$ is the discharge power of the energy storage system in the $e$-th low-carbon residential area during time period $t$ in season $s$ $P_{e,s,t}^{\text{ch}}$ is the charging power of the energy storage system in the $e$-th low-carbon residential area during time period $t$ in season $s$ $\delta_{e,s,t}^{\text{ess}}$ is the binary operation state variable of the energy storage system in the $e$-th low-carbon residential area during time period $t$ in season $s$, $\delta$ M $M$ is a large constant $P_{e}^{\text{ch,max}}$ is the upper limit of the charging power of the energy storage system in the $e$-th low-carbon residential area $P_{e}^{\text{disch,max}}$ is the upper limit of the discharge power of the energy storage system in the $e$-th low-carbon residential area $E_{e}^{\text{ess,min}}$ is the lower limit of the energy state of the energy storage system $E_{e}^{\text{ess}}$ is the installed capacity of the energy storage system in the $e$-th low-carbon residential area, $\tau$ O $E_{0}^{\text{ess}}$ is the initial energy state of the energy storage system, $T$ U $\Delta t$ is the unit time is the upper limit of the energy state of the energy storage system. \(t_x\) is any time period within a typical day, and \(t_n\) is the last time period of a typical day. are the charging energy and discharging energy of the energy storage system of the \(e\)-th low-carbon residential area power grid in time period \(t\) during season \(s\), respectively. is the actual output of the distributed photovoltaic in the \(e\)-th low-carbon residential area power grid in time period \(t\) during season \(s\). is the installed capacity of the distributed photovoltaic in the \(e\)-th low-carbon residential area power grid. is the output coefficient of the distributed photovoltaic in time period \(t\). is the seasonal coefficient of photovoltaic power generation in season \(s\).

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0072] The present invention proposes an optimized operation method and system for a low-carbon residential area power grid considering the short-term islanding mode. The method takes into account the short-term islanding mode of the low-carbon residential area power grid, aims at the lowest comprehensive operation cost, and constructs an optimized operation model for the low-carbon residential area power grid. The low-carbon residential area power grid includes a micro data center, electric vehicles and fuel vehicles, a renewable direct air capture system, a distributed photovoltaic system, and an energy storage system. By solving the optimized operation model of the low-carbon residential area power grid, an optimized operation plan for the low-carbon residential area power grid is obtained. On the one hand, from the perspective of the islanding mode under short-term faults, the method considers the operation ability of the low-carbon residential area power grid and can effectively improve the safe and stable operation of the low-carbon residential area power grid. On the other hand, the method takes into account the application of emerging loads such as small data centers, flexible charging loads of electric vehicles, distributed photovoltaics and energy storage systems in the low-carbon residential area power grid, and uses the renewable direct air capture system to directly absorb carbon dioxide in the ambient air during low electricity price periods and high output periods of new energy. The absorption medium is recycled through the regeneration process, and the profit is obtained by selling the collected, regenerated and released carbon dioxide. While ensuring the safe and stable operation of the low-carbon residential area power grid, the operation cost of the system is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is the topological structure diagram of the example in Embodiment 1.

[0074] Figure 2 is the time-of-use electricity price diagram of the example in Embodiment 1.

[0075] Figure 3 is the photovoltaic output and micro data center load characteristic diagram of the example in Embodiment 1.

[0076] Figure 4 is the overall flow chart of the method of the present invention.

[0077] Figure 5 is the structure diagram of the low-carbon residential area power grid in Embodiment 1.

[0078] Figure 6 It is the operating state diagram under the island mode described in Embodiment 1.

[0079] Figure 7 It is the structure diagram of the system described in the present invention. Detailed implementation manners

[0080] The present invention will be further described in detail below in conjunction with the detailed implementation manners and the accompanying drawings.

[0081] The present invention proposes an optimized operation method and system for a low-carbon residential distribution area considering the short-term island mode. Considering the short-term island mode of the low-carbon residential distribution area, it includes six important parts: basic load, micro data center, electric vehicles and fuel vehicles, renewable direct air capture system, distributed photovoltaic system, and energy storage system. With the goal of minimizing the comprehensive operation cost, an optimized operation model of the low-carbon residential distribution area is constructed, and by solving the optimized operation model of the low-carbon residential distribution area, an optimized operation plan for the low-carbon residential distribution area is obtained, while ensuring the safe and stable operation of the low-carbon residential distribution area, reducing the operation cost of the system.

[0082] Embodiment 1:

[0083] This embodiment takes a 12-node distribution network example with two residential distribution areas as the research object, and its topological result is as Figure 1 shown. The time-of-use electricity price adopted in the example is as Figure 2 shown. The photovoltaic output and the load characteristics of the micro data center are as Figure 3 shown. The parameter settings adopted in the example are as follows: the number of typical days in the four quarters of a year are 90, 91, 91, and 92 days respectively; the trading unit price of carbon dioxide is 0.2 million yuan per ton; the unit time is 1 hour; when a fault occurs each time, the island operation time is 0.2 hours; the unit cycle loss cost of the energy storage system is 1 yuan per kilowatt-hour; the carbon dioxide conversion coefficients in the absorption and regeneration processes are 2 tons and 0.8 tons per megawatt-hour respectively; the water evaporation amount caused by obtaining a unit of cooling capacity is 4×10 -4 cubic meters per kilowatt-hour; the unit water evaporation cost is 2 yuan per cubic meter; the unit cost of fuel is 10 yuan per liter; the energy efficiency ratio of the air-conditioning system adopted by the micro data center is 4; the initial energy state of the energy storage system is 0.4; the lower and upper limits of the energy state of the energy storage system are 0.1 and 0.9 respectively; the seasonal coefficients of photovoltaic power generation are 0.8, 1, 0.8, and 0.6 respectively; the unit energy consumption of electric vehicles is 0.2 kilowatt-hours per kilometer; the rated voltage of the distribution network nodes is 35 kV; the lower and upper limits of the distribution network node voltage are 31.5 kV and 38.5 kV respectively; the power outage loss per unit of electricity is 100 yuan per kilowatt-hour.

[0084] As Figure 4As shown in the figure, the optimized operation method for a low-carbon residential power distribution area considering the short-term islanding mode is carried out in the following steps in sequence:

[0085] 1. Considering the short-term islanding mode of the low-carbon residential power distribution area, an optimized operation model for the low-carbon residential power distribution area is constructed with the goal of minimizing the comprehensive operation cost;

[0086] The structure of the low-carbon residential power distribution area is as Figure 5 shown, including six important parts: basic load, micro data center, electric vehicles and fuel vehicles, renewable direct air capture system, distributed photovoltaic system, and energy storage system; among them, the basic load is the rigid load necessary to maintain the normal life of the residents in the power distribution area and is a fixed and non-adjustable load; the micro data center has its fixed operating power demand and cooling capacity demand for heat dissipation through air conditioning cooling and water-side cooling; in terms of the vehicle demand of residents, the synchronous application of electric vehicles and fuel vehicles is considered; the renewable direct air capture system can directly absorb carbon dioxide in the ambient air and release carbon dioxide again by heating the absorption medium at an appropriate time, and additional profits can be obtained by selling the absorbed carbon dioxide; the distributed photovoltaic system and the energy storage system, as important distributed power sources in the low-carbon residential power distribution area, provide a certain degree of source-load flexibility for the power distribution area while giving the power distribution area the ability to operate in the short-term islanding mode;

[0087] The objective function of the optimized operation model includes:

[0088] min C P +C O -I C ;

[0089]

[0090] C O =C W +C S +C G ;

[0091]

[0092] In the above formula, C P is the power purchase cost for the operation of the power distribution area, including the power purchase cost generated by all loads other than the basic load, C O is the operation cost of other systems, I C is the income from selling carbon dioxide after capture, is the active power output from the superior power grid to the e-th low-carbon residential power distribution area at time t in season s, is the basic active load of the e-th low-carbon residential power distribution area at time t in season s, is the electricity price at time t, d s is the number of typical days in season s, C Wis the water - side cooling cost in the micro - data, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of the fuel vehicle is the power of the regeneration process of the renewable direct air capture system in the e - th low - carbon residential distribution area during time period t in season s, T U is the unit time, ψ DA is the amount of carbon dioxide obtained per unit of energy consumed during carbon dioxide regeneration and collection, PR C is the unit price of carbon dioxide sold is the cooling capacity generated by water - side cooling of the micro - data center in the e - th low - carbon residential distribution area during time period t in season s, η W is the water evaporation amount per unit of cooling capacity obtained, PRW 为 is the unit water evaporation cost when using water - side cooling are respectively the charging energy and discharging energy of the energy storage system in the e - th low - carbon residential distribution area during time period t in season s. For any time period t, one of them 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 number of trips of the fuel vehicle in the e - th low - carbon residential distribution area during time period t in season s, L s，t is the average driving mileage of a single - trip demand during time period t in season s is the fuel consumption per unit mileage of the fuel vehicle during time period t in season s, PR G is the unit cost of fuel

[0093] The constraint conditions of the optimal operation model include the constraints of the micro - data center in the low - carbon residential distribution area, vehicle usage demand constraints, renewable direct air capture system operation constraints, energy storage system constraints, distributed photovoltaic system constraints, constraints in the short - time island mode, and the operation constraints of the superior distribution network;

[0094] The constraints of the micro - data center include:

[0095] The cooling capacity demand constraint of the micro - data center:

[0096]

[0097] The air - conditioning refrigeration constraint adopted by the micro - data center:

[0098]

[0099] In the above formula, is the fixed operation load of the micro - data center in the e - th low - carbon residential distribution area during time period t in season s is the cooling capacity demand coefficient of the micro - data center load in season s The cooling capacity generated by the air-conditioning cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s The cooling capacity generated by the water-side cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s The active power consumed by the air-conditioning cooling of the micro data center in the e-th low-carbon residential area during the time period t in season s, η A The energy efficiency ratio of the air-conditioning cooling of the micro data center The capacity of the air-conditioning cooling system of the micro data center in the e-th low-carbon residential area

[0100] The vehicle usage demand constraints include:

[0101] The vehicle usage demand constraints in the low-carbon residential area:

[0102]

[0103] The charging demand constraints of electric vehicles in the low-carbon residential area, that is, the charging service capacity on the same day should be able to meet the generated charging demand:

[0104]

[0105] The charging capacity constraints in the low-carbon residential area, that is, the charging power at any time cannot exceed the installed capacity of the charging pile:

[0106]

[0107] In the above formula, The total vehicle usage demand in the e-th low-carbon residential area during the time period t in season s The number of electric vehicles used in the e-th low-carbon residential area during the time period t in season s The number of fuel vehicles used in the e-th low-carbon residential area during the time period t in season s The electric vehicle charging load in the e-th low-carbon residential area during the time period t in season s, T U The unit time, L s，t The average driving mileage of a single trip demand during the time period t in season s, E U The unit energy consumption of electric vehicles The installed capacity of the electric vehicle charging pile in the e-th low-carbon residential area

[0108] The renewable direct air capture system mainly consumes electric energy during the absorption and regeneration periods. During the absorption stage, air is inhaled into the carbon dioxide collector by a fan, and during the regeneration stage, carbon dioxide is released and collected again by heating the absorption medium. The operation constraints of the renewable direct air capture system include:

[0109] Power constraint in the absorption stage:

[0110]

[0111] and power constraint in the regeneration stage:

[0112]

[0113] Absorption stock constraint, which limits the maximum amount of carbon dioxide that can be absorbed by the carbon dioxide absorption medium in the system:

[0114]

[0115] In the above formula, is the power of the absorption process of the renewable direct air capture system in the e-th low-carbon residential distribution area during time period t in season s, is the installed capacity of the absorption fan in the renewable direct air capture system of the e-th low-carbon residential distribution area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential distribution area during time period t in season s, is the installed capacity of the regeneration heating device in the renewable direct air capture system of the e-th low-carbon residential distribution area, is the amount of carbon dioxide absorbed by the renewable direct air capture system in the e-th low-carbon residential distribution area during time period t in season s, Ψ DA is the carbon dioxide conversion coefficient in the absorption process, that is, the amount of carbon dioxide that can be absorbed per unit of energy consumed, is the amount of carbon dioxide regenerated by the renewable direct air capture system in the e-th low-carbon residential distribution area during time period t in season s, ψ DR is the carbon dioxide conversion coefficient in the regeneration process, that is, the amount of carbon dioxide that can be regenerated per unit of energy consumed, is the maximum amount of carbon dioxide that can be absorbed by the carbon dioxide absorption medium in the renewable direct air capture system of the e-th low-carbon residential distribution area, tx is any time period within a typical day, and tn is the last time period of a typical day;

[0116] Energy storage system constraints include:

[0117] Charge and discharge power constraint of the energy storage system:

[0118]

[0119] Energy state constraint of the energy storage system:

[0120]

[0121] In the above formula, is the actual power of the e-th low-carbon residential area energy storage system during time period t in season s. When the energy storage system is in the discharging state, it is in the charging state when is the discharging power of the e-th low-carbon residential area energy storage system during time period t in season s. When the energy storage system is in the discharging state when in the charging state is the charging power of the e-th low-carbon residential area energy storage system during time period t in season s. When the energy storage system is in the discharging state when in the charging state is the binary operation state variable of the e-th low-carbon residential area energy storage system during time period t in season s. When the energy storage system operates in the discharging state when operating in the charging state δ M is the large M constant, is the upper limit of the charging power of the e-th low-carbon residential area energy storage system, is the upper limit of the discharging power of the e-th low-carbon residential area energy storage system, is the lower limit of the energy state of the energy storage system, is the installed capacity of the e-th low-carbon residential area energy storage system, τ O is the initial energy state of the energy storage system, T U is the unit time, is the upper limit of the energy state of the energy storage system. tx is any time period within a typical day, and tn is the last time period of a typical day. are respectively the charging energy and discharging energy of the e-th low-carbon residential area energy storage system during time period t in season s;

[0122] The constraints of the distributed photovoltaic system include:

[0123] The output constraint of the distributed photovoltaic system:

[0124]

[0125] In the above formula, is the actual output of the distributed photovoltaic in the e-th low-carbon residential area during time period t in season s, is the installed capacity of the distributed photovoltaic in the e-th low-carbon residential area, is the output coefficient of the distributed photovoltaic during time period t, is the seasonal coefficient of photovoltaic power generation in season s;

[0126] Considering the situation that the distribution network cannot supply power to low-carbon residential areas during short-term faults, it is necessary to ensure the island operation ability of residential areas. When a low-carbon residential area operates in island mode, its operation status is as follows Figure 6 shown in the figure. The charging process of all electric vehicles and the absorption and regeneration processes of the renewable direct air capture system are suspended. The micro data centers all adopt water-side cooling, and only the distributed photovoltaic system and the energy storage system supply power to the basic load of the area and the fixed load of the micro data centers. The constraints in the short-term island mode include:

[0127] Power balance constraint in short-term island mode:

[0128]

[0129] Energy constraint of the energy storage system in short-term island mode:

[0130]

[0131] In the above formula, is the discharge power of the energy storage system of the e-th low-carbon residential area in time period t during season s when operating in island mode. In this mode, the energy storage system is in the discharge state. is the basic active load of the e-th low-carbon residential area in time period t during season s. is the fixed operating load of the micro data center of the e-th low-carbon residential area in time period t during season s. is the actual output of the distributed photovoltaic of the e-th low-carbon residential area in time period t during season s. is the installed capacity of the energy storage system of the e-th low-carbon residential area, and τ O is the initial energy state of the energy storage system. is the actual power of the energy storage system of the e-th low-carbon residential area in time period t during season s, and T U is the unit time. is the lower limit of the energy state of the energy storage system, and T I is the operation duration of the island mode. tx is any time period within a typical day, and tn is the last time period of a typical day.

[0132] The operating constraints of the upstream distribution network include:

[0133] Node power balance constraint:

[0134]

[0135] Distribution line power constraint:

[0136]

[0137]

[0138] Distribution network node voltage constraint:

[0139]

[0140] In the above formula, is the active power output from the upper-level distribution network to the e-th low-carbon residential area during time period t in season s, are respectively the active power and reactive power transmitted on distribution network line l during time period t in season s, μ k，e is the binary coefficient of the connection relationship between distribution network line k and the e-th low-carbon residential area. When μ k，e = 1, distribution network line k is connected to the e-th low-carbon residential area. When μ k，e = 0, distribution network line k is not connected to the e-th low-carbon residential area. is the basic reactive power load of the e-th low-carbon residential area during time period t in season s, is the line capacity of distribution network line k, ΔU s，t，k is the voltage drop on distribution network line k during time period t in season s, are respectively the resistance and reactance of distribution network line k, U N is the rated voltage of the distribution network node, U s，t，i 、U s，t，j are respectively the node voltages of distribution network nodes i and j during time period t in season s. Nodes i and j are the two endpoints of distribution network line k, U s，t，e is the node voltage of the upper-level distribution network to the e-th low-carbon residential area during time period t in season s, U m 、U M are respectively the lower limit and upper limit of the distribution network node voltage.

[0141] 2. Conduct simulation operation based on the MATLAB / CPLEX platform, solve the optimal operation model of the low-carbon residential area, and obtain the optimal operation plan of the low-carbon residential area;

[0142] The parameter settings of the simulation platform hardware equipment are: Intel Core i7-9750H, 32G RAM, 2.6GHz.

[0143] To verify the effectiveness of this solution, a traditional microgrid optimal operation method that does not consider the short-term island operation ability of the residential area and the carbon dioxide capture system is introduced as Method 2. Both this solution proposed method as Method 1 and Method 2 are applied to a 12-node distribution network example with two residential areas for comparison. The economic comparison results are shown in Table 1:

[0144] Table 1 Economic results of the two methods

[0145]

[0146] Since the residential distribution area in Method 2 does not have the ability to operate in island mode, it is necessary to include the power outage loss cost caused by faults in the system operation cost;

[0147] The power outage loss cost caused by the fault is:

[0148]

[0149] In the above formula, C t is the power outage loss cost caused by the fault, T I is the operation duration of the island mode, is the basic active power load of the e-th low-carbon residential distribution area in period t in season s, is the fixed operation load of the micro data center of the e-th low-carbon residential distribution area in period t in season s, is the frequency of power outage accidents in season s, PR I is the power outage loss per unit of electricity;

[0150] Table 1 lists the power purchase cost, system operation cost, carbon dioxide sale revenue, and comprehensive cost when using the two methods. It can be seen from Table 1 that although the power purchase cost when using Method 1 is slightly higher than that when using Method 2, the system operation cost of Method 1 is 25.22% lower than that of Method 2, and an additional carbon dioxide sale revenue of 6.14×10 6 yuan is generated; from the perspective of the comprehensive cost, the comprehensive cost when using Method 1 is 26.58% lower than that when using Method 2; the above results show that the optimized operation method of the low-carbon residential distribution area considering the short-term island mode proposed in this scheme achieves the effect of reducing the system operation cost while ensuring the safe and stable operation of the low-carbon residential distribution area.

[0151] Example 2:

[0152] As Figure 7 shown, the optimized operation system of the low-carbon residential distribution area considering the short-term island mode includes an optimized operation model construction module and an optimized operation model solving module;

[0153] The optimized operation model construction module is used to consider the short-term island mode of the low-carbon residential distribution area and construct an optimized operation model of the low-carbon residential distribution area with the goal of minimizing the comprehensive operation cost. The low-carbon residential distribution area includes a micro data center, electric vehicles and fuel vehicles, a renewable direct air capture system, a distributed photovoltaic system, and an energy storage system;

[0154] The optimized operation model solving module is used to solve the optimized operation model of the low-carbon residential distribution area to obtain an optimized operation plan for the low-carbon residential distribution area.

[0155] The optimization operation model construction module includes an objective function construction unit;

[0156] The objective function construction unit is used to construct the objective function of the following optimization operation model:

[0157] min C P +C O -I C ;

[0158]

[0159] C O =C W +C S +C G ;

[0160]

[0161] In the above formula, C P is the electricity purchase cost for the operation of the substation area, C O is the operation cost of other systems, I C is the income from selling carbon dioxide after capture, is the active power output from the superior power grid to the e-th low-carbon residential substation area within the time period t in season s, is the basic active load of the e-th low-carbon residential substation area within the time period t in season s, is the electricity price at time period t, d s is the number of typical days in season s, C W is the water-side cooling cost in the micro data, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of the fuel vehicle, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential substation area within the time period t in season w, T U is the unit time, ψ DA is the amount of carbon dioxide obtained per unit of energy consumed during carbon dioxide regeneration and collection, PR C is the unit price of carbon dioxide sold, is the cooling capacity generated by water-side cooling of the micro data center in the e-th low-carbon residential substation area within the time period t in season s, η W is the water evaporation amount per unit of cooling capacity obtained, PR W is the unit water evaporation cost when using water-side cooling, are respectively the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential substation area within the time period t in season s, PR CY is the unit cycle loss cost of the energy storage system, is the number of trips of fuel vehicles in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, $L$ s，t is the average driving mileage of a single trip demand during time period $t$ in season $s$, is the fuel consumption per unit mileage of fuel vehicles during time period $t$ in season $s$, $PR$ G is the unit cost of fuel.

[0162] The optimization operation model construction module further includes a constraint construction unit under the short-term island mode, an upper-level distribution network operation constraint construction unit, a micro data center constraint construction unit, a vehicle usage demand constraint construction unit, a renewable direct air capture system operation constraint construction unit, an energy storage system constraint construction unit, and a distributed photovoltaic system constraint construction unit;

[0163] The constraint construction unit under the short-term island mode is used to construct the following constraints under the short-term island mode:

[0164]

[0165] The upper-level distribution network operation constraint construction unit is used to construct the following upper-level distribution network operation constraints:

[0166]

[0167] In the above formula, is the discharge power of the energy storage system in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$ when operating in island mode, is the basic active load of the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the fixed operation load of the micro data center in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the actual output of distributed photovoltaics in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, is the installed capacity of the energy storage system in the $e$-th low-carbon residential distribution area, $\tau$ O is the initial energy state of the energy storage system, is the actual power of the energy storage system in the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, $T$ U is the unit time, is the lower limit of the energy state of the energy storage system, $T$ I is the operating duration of the island mode, $t_x$ is any time period within a typical day, and $t_n$ is the last time period of a typical day, is the active power output from the upper-level distribution network to the $e$-th low-carbon residential distribution area during time period $t$ in season $s$, are respectively the active power and reactive power transmitted on the distribution network line $k$ during time period $t$ in season $s$, $\mu$ k,eis the binary coefficient of the connection relationship between the distribution network line k and the e-th low-carbon residential area, is the basic reactive power load of the e-th low-carbon residential area during the time period t in season s, ΔU s,t,k is the voltage drop on the distribution network line k during the time period t in season s, are the resistance and reactance of the distribution network line k respectively, U N is the rated voltage of the distribution network node;

[0168] The micro data center constraint construction unit is used to construct the following micro data center constraints:

[0169]

[0170] In the above formula, is the fixed operating load of the micro data center of the e-th low-carbon residential area during the time period t in season s, is the cooling demand coefficient of the micro data center load in season s, is the cooling capacity generated by air-conditioning cooling of the micro data center of the e-th low-carbon residential area during the time period t in season s, is the cooling capacity generated by water-side cooling of the micro data center of the e-th low-carbon residential area during the time period t in season s, is the active power consumed by air-conditioning cooling of the micro data center of the e-th low-carbon residential area during the time period t in season s, η A is the energy efficiency ratio of air-conditioning cooling of the micro data center, is the capacity of the air-conditioning cooling system of the micro data center of the e-th low-carbon residential area;

[0171] The vehicle usage demand constraint construction unit is used to construct the following vehicle usage demand constraints:

[0172]

[0173] In the above formula, is the total vehicle usage demand of the e-th low-carbon residential area during the time period t in season s, is the number of electric vehicles used in the e-th low-carbon residential area during the time period t in season s, is the number of fuel vehicles used in the e-th low-carbon residential area during the time period t in season s, is the electric vehicle charging load of the e-th low-carbon residential area during the time period t in season s, T U is the unit time, L S，t is the average driving mileage of a single trip demand during the time period t in season s, E U is the unit energy consumption of the electric vehicle, is the installed capacity of the electric vehicle charging pile in the e-th low-carbon residential area;

[0174] The operating constraint construction unit of the renewable direct air capture system is used to construct the following operating constraints of the renewable direct air capture system:

[0175]

[0176] In the above formula, is the power of the absorption process of the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, is the installed capacity of the absorption fan in the renewable direct air capture system of the e-th low-carbon residential area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, is the installed capacity of the regeneration heating device in the renewable direct air capture system of the e-th low-carbon residential area, is the amount of carbon dioxide absorbed by the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, ψ DA is the carbon dioxide conversion coefficient of the absorption process, is the amount of carbon dioxide regenerated by the renewable direct air capture system in the e-th low-carbon residential area during the time period t in season s, ψ DR is the carbon dioxide conversion coefficient of the regeneration process, is the maximum amount of carbon dioxide that the carbon dioxide absorption medium in the renewable direct air capture system of the e-th low-carbon residential area can absorb. tx is any time period within a typical day, and tn is the last time period of a typical day;

[0177] The energy storage system constraint construction unit is used to construct the following energy storage system constraints:

[0178]

[0179] The distributed photovoltaic system constraint construction unit is used to construct the following distributed photovoltaic system constraints:

[0180]

[0181] In the above formula, is the actual power of the energy storage system in the e-th low-carbon residential area during the time period t in season s, is the discharge power of the energy storage system in the e-th low-carbon residential area during the time period t in season s, is the charging power of the energy storage system in the e-th low-carbon residential area during the time period t in season s, is the binary operating state variable of the energy storage system in the e-th low-carbon residential area during the time period t in season s, δ M is the large M constant, is the upper limit of the charging power of the energy storage system for the e-th low-carbon residential area is the upper limit of the discharging power of the energy storage system for the e-th low-carbon residential area is the lower limit of the energy state of the energy storage system is the installed capacity of the energy storage system for the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, T U is the unit time is the upper limit of the energy state of the energy storage system. tx is any time period within a typical day, and tn is the last time period of a typical day are the charging energy and discharging energy of the energy storage system for the e-th low-carbon residential area during the time period t in season s respectively is the actual output of the distributed photovoltaic in the e-th low-carbon residential area during the time period t in season s is the installed capacity of the distributed photovoltaic in the e-th low-carbon residential area is the output coefficient of the distributed photovoltaic during the time period t is the seasonal coefficient of the photovoltaic power generation in season s

Claims

1. A low-carbon residential area optimization operation method taking into account short-term island mode, characterized in that: The method comprises: S1. Considering the short-term island mode of low-carbon residential areas, with the goal of minimizing the comprehensive operating cost, an optimal operation model of low-carbon residential areas is constructed, wherein the low-carbon residential areas include micro data centers, electric vehicles and fuel vehicles, renewable direct air capture systems, distributed photovoltaic systems, and energy storage systems; S2. Solve the optimal operation model of the low-carbon residential area and obtain the optimal operation plan of the low-carbon residential area.

2. The low-carbon residential area optimization operation method taking into account the short-term island mode according to claim 1 is characterized in that: In S1, the objective function of the optimized operation model includes: my C P +C O -IN C ; C O =C W +C S +C G ; In the above formula, C P is the electricity purchase cost for the area operation, C O is the operating cost of other systems, I C The revenue from the sale of captured carbon dioxide is is the active power output from the upper power grid to the e-th low-carbon residential area in time period t during season s, is the basic active load of the e-th low-carbon residential area in time period t during season s, is the electricity price in period t, d s is the number of typical days in season s, C W is the water side cooling cost in the micro data center, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of a fuel car, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area in the season s and period t, T U is the unit time, ψ DA The amount of carbon dioxide obtained per unit of energy consumed when collecting and regenerating carbon dioxide, PR C is the unit price of carbon dioxide sold, is the cooling capacity generated by water-side cooling in the micro data center of the e-th low-carbon residential area in the period t in season s, η W To obtain the water evaporation per unit cooling capacity, PR W is the unit water evaporation cost when water side cooling is used, are the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential area in time period t at season s, PR CY is the unit cycle loss cost of the energy storage system, is the number of fuel vehicle trips in the e-th low-carbon residential area in time period t during season s, L s，t is the average mileage of a single trip demand in time period t in season s, is the fuel consumption per unit mileage of a fuel vehicle in season s, PR G is the unit cost of fuel.

3. The low-carbon residential area optimization operation method taking into account the short-term island mode according to claim 1 is characterized in that: In S1, the constraints of the optimization operation model include the constraints of the short-term island mode of the low-carbon residential area and the operation constraints of the upper distribution network; The constraints in the short-term island mode include: The upper-level distribution network operation constraints include: In the above formula, is the discharge power of the energy storage system in the e-th low-carbon residential area in time period t when operating in island mode, is the basic active load of the e-th low-carbon residential area in time period t during season s, is the fixed operating load of the e-th low-carbon residential micro data center in time period t during season s, is the actual output of distributed photovoltaic power generation in the e-th low-carbon residential area in time period t during season s, is the installed capacity of the energy storage system in the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, is the actual power of the energy storage system in the e-th low-carbon residential area in time period t during season s, T U is the unit time, is the lower limit of the energy state of the energy storage system, T I is the operating time of the island mode, tx is any period in a typical day, tn is the last period of a typical day, is the active power output from the upper distribution network to the e-th low-carbon residential area in time period t during season s, are respectively the active power and reactive power transmitted on the distribution network line k in the season s and time period t, μ k，e is the binary coefficient of the connection relationship between the distribution network line k and the e-th low-carbon residential area, is the base reactive load of the e-th low-carbon residential area in time period t at season s, ΔU s，t，k is the voltage drop on the distribution network line k in time period t in season s, are the resistance and reactance of the distribution network line k, U N is the rated voltage of the distribution network node.

4. The low-carbon residential area optimization operation method taking into account the short-term island mode according to claim 1 is characterized in that: In S1, the constraints of the optimization operation model also include micro data center constraints in low-carbon residential areas, vehicle usage demand constraints, and renewable direct air capture system operation constraints; The micro data center constraints include: In the above formula, is the fixed operating load of the e-th low-carbon residential micro data center in time period t during season s, is the cooling demand coefficient of the micro data center load in season s, The cooling capacity generated by air conditioning in the micro data center of the e-th low-carbon residential area in time period t during season s is: The cooling capacity generated by water-side cooling in the micro data center of the e-th low-carbon residential area in the season s during the period t is: is the active power consumed by the micro data center in the e-th low-carbon residential area for air conditioning cooling in season s and time period t, η A Energy efficiency ratio of air conditioning for micro data centers, The capacity of the air conditioning cooling system for the micro data center in the e-th low-carbon residential area; The vehicle use demand constraints include: In the above formula, is the total vehicle demand of the e-th low-carbon residential area in time period t during season s, is the number of electric vehicles used in the e-th low-carbon residential area in time period t during season s, is the number of fuel vehicles used in the e-th low-carbon residential area in time period t during season s, is the electric vehicle charging load of the e-th low-carbon residential area in time period t during season s, T U is the unit time, L s，t is the average mileage of a single trip demand in time period t in season s, E U is the unit energy consumption of electric vehicles, The installed capacity of electric vehicle charging piles in the e-th low-carbon residential area; The renewable direct air capture system operating constraints include: In the above formula, is the power absorbed by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, The installed capacity of absorption fans in the renewable direct air capture system for the e low carbon residential area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area in the season s and period t, is the installed capacity of the regenerative heating device in the renewable direct air capture system of the e-th low-carbon residential area, is the amount of carbon dioxide absorbed by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, ψ DA is the carbon dioxide conversion coefficient of the absorption process, is the amount of carbon dioxide regenerated by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, ψ DR is the carbon dioxide conversion coefficient of the regeneration process, is the maximum amount of carbon dioxide that can be absorbed by the carbon dioxide absorption medium in the renewable direct air capture system of the e-th low-carbon residential area, tx is any time period in a typical day, and tn is the last time period of a typical day.

5. The low-carbon residential area optimization operation method taking into account the short-term island mode according to claim 1 is characterized in that: In S1, the constraints of the optimization operation model also include energy storage system constraints and distributed photovoltaic system constraints in low-carbon residential areas; The energy storage system constraints include: The distributed photovoltaic system constraints include: In the above formula, is the actual power of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the discharge power of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the charging power of the energy storage system in the e-th low-carbon residential area in time period t during season s, is the binary operating state variable of the e-th low-carbon residential energy storage system in time period t at season s, δ M is the large M constant, is the upper limit of the charging power of the energy storage system in the e-th low-carbon residential area, is the upper limit of the discharge power of the energy storage system in the e-th low-carbon residential area, is the lower limit of the energy state of the energy storage system, is the installed capacity of the energy storage system in the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, T U is the unit time, is the upper limit of the energy state of the energy storage system, tx is any period in a typical day, tn is the last period of a typical day, are the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the actual output of distributed photovoltaic power generation in the e-th low-carbon residential area in time period t during season s, is the installed capacity of distributed photovoltaic power generation in the e-th low-carbon residential area, is the output coefficient of distributed photovoltaic in time period t, is the seasonal coefficient of photovoltaic power generation in season s.

6. A low-carbon residential area optimization operation system taking into account short-term island mode, characterized in that: The system includes an optimization operation model construction module and an optimization operation model solution module; The optimization operation model construction module is used to consider the short-term island mode of the low-carbon residential area, and to construct the optimization operation model of the low-carbon residential area with the goal of minimizing the comprehensive operation cost. The low-carbon residential area includes a micro data center, electric vehicles and fuel vehicles, a renewable direct air capture system, a distributed photovoltaic system, and an energy storage system; The optimization operation model solving module is used to solve the optimization operation model of the low-carbon residential area to obtain the optimization operation plan of the low-carbon residential area.

7. The low-carbon residential area optimization operation system taking into account the short-term island mode according to claim 6 is characterized in that: The 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 optimization operation model: my C P +C O -IN C ; C O =C W +C S +C G ; In the above formula, C P is the electricity purchase cost for the area operation, C O is the operating cost of other systems, I C The revenue from the sale of captured carbon dioxide is is the active power output from the upper power grid to the e-th low-carbon residential area in time period t during season s, is the basic active load of the e-th low-carbon residential area in time period t during season s, is the electricity price in period t, d s is the number of typical days in season s, C W is the water side cooling cost in the micro data center, C S is the cycle loss cost of the energy storage system, C G is the fuel cost of a fuel car, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area in the season s and period t, T U is the unit time, ψ DA The amount of carbon dioxide obtained per unit of energy consumed when collecting and regenerating carbon dioxide, PR C is the unit price of carbon dioxide sold, is the cooling capacity generated by water-side cooling in the micro data center of the e-th low-carbon residential area in the period t in season s, η W To obtain the water evaporation per unit cooling capacity, PR W is the unit water evaporation cost when water side cooling is used, are the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential area in time period t at season s, PR CY is the unit cycle loss cost of the energy storage system, is the number of fuel vehicle trips in the e-th low-carbon residential area in time period t during season s, L s，t is the average mileage of a single trip demand in time period t in season s, is the fuel consumption per unit mileage of a fuel vehicle in season s, PR G is the unit cost of fuel.

8. The low-carbon residential area optimization operation system taking into account the short-term island mode according to claim 6 is characterized in that: The optimization operation model construction module also includes a constraint construction unit in short-term island mode and an upper-level distribution network operation constraint construction unit; The constraint construction unit in the short-term island mode is used to construct the following constraints in the short-term island mode: The upper-level distribution network operation constraint construction unit is used to construct the following upper-level distribution network operation constraints: In the above formula, is the discharge power of the energy storage system in the e-th low-carbon residential area in time period t when operating in island mode, is the basic active load of the e-th low-carbon residential area in time period t during season s, is the fixed operating load of the e-th low-carbon residential micro data center in time period t during season s, is the actual output of distributed photovoltaic power generation in the e-th low-carbon residential area in time period t during season s, is the installed capacity of the energy storage system in the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, is the actual power of the energy storage system in the e-th low-carbon residential area in time period t during season s, T U is the unit time, is the lower limit of the energy state of the energy storage system, T I is the operating time of the island mode, tx is any period in a typical day, tn is the last period of a typical day, is the active power output from the upper distribution network to the e-th low-carbon residential area in time period t during season s, are respectively the active power and reactive power transmitted on the distribution network line k in the season s and time period t, μ k，e is the binary coefficient of the connection relationship between the distribution network line k and the e-th low-carbon residential area, is the base reactive load of the e-th low-carbon residential area in time period t at season s, ΔU s，t，k is the voltage drop on the distribution network line k in time period t in season s, are the resistance and reactance of the distribution network line k, U N is the rated voltage of the distribution network node.

9. The low-carbon residential area optimization operation system taking into account short-term island mode according to claim 6 is characterized in that: The optimization operation model construction module also includes a micro data center constraint construction unit, a vehicle usage demand constraint construction unit, and a renewable direct air capture system operation constraint construction unit; The micro data center constraint construction unit is used to construct the following micro data center constraints: In the above formula, is the fixed operating load of the e-th low-carbon residential micro data center in time period t during season s, is the cooling demand coefficient of the micro data center load in season s, The cooling capacity generated by air conditioning in the micro data center of the e-th low-carbon residential area in time period t during season s is: The cooling capacity generated by water-side cooling in the micro data center of the e-th low-carbon residential area in the season s during the period t is: is the active power consumed by the micro data center in the e-th low-carbon residential area for air conditioning cooling in season s and time period t, η A Energy efficiency ratio of air conditioning for micro data centers, The capacity of the air conditioning cooling system for the micro data center in the e-th low-carbon residential area; The vehicle use demand constraint construction unit is used to construct the following vehicle use demand constraint: In the above formula, is the total vehicle demand of the e-th low-carbon residential area in time period t during season s, is the number of electric vehicles used in the e-th low-carbon residential area in time period t during season s, is the number of fuel vehicles used in the e-th low-carbon residential area in time period t during season s, is the electric vehicle charging load of the e-th low-carbon residential area in time period t during season s, T U is the unit time, L s,t is the average mileage of a single trip demand in time period t in season s, E U is the unit energy consumption of electric vehicles, The installed capacity of electric vehicle charging piles in the e-th low-carbon residential area; The renewable direct air capture system operation constraint building unit is used to build the following renewable direct air capture system operation constraint: In the above formula, is the power absorbed by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, The installed capacity of absorption fans in the renewable direct air capture system for the e low carbon residential area, is the power of the regeneration process of the renewable direct air capture system in the e-th low-carbon residential area in the season s and period t, is the installed capacity of the regenerative heating device in the renewable direct air capture system of the e-th low-carbon residential area, is the amount of carbon dioxide absorbed by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, ψ DA is the carbon dioxide conversion coefficient of the absorption process, is the amount of carbon dioxide regenerated by the renewable direct air capture system in the e-th low-carbon residential area in time period t during season s, ψ DR is the carbon dioxide conversion coefficient of the regeneration process, is the maximum amount of carbon dioxide that can be absorbed by the carbon dioxide absorption medium in the renewable direct air capture system of the e-th low-carbon residential area, tx is any time period in a typical day, and tn is the last time period of a typical day.

10. The low-carbon residential area optimization operation system taking into account short-term island mode according to claim 6, characterized in that: The optimization operation model construction module also includes an energy storage system constraint construction unit and a distributed photovoltaic system constraint construction unit; The energy storage system constraint construction unit is used to construct the following energy storage system constraints: The distributed photovoltaic system constraint construction unit is used to construct the following distributed photovoltaic system constraints: In the above formula, is the actual power of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the discharge power of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the charging power of the energy storage system in the e-th low-carbon residential area in time period t during season s, is the binary operating state variable of the e-th low-carbon residential energy storage system in time period t at season s, δ M is the large M constant, is the upper limit of the charging power of the energy storage system in the e-th low-carbon residential area, is the upper limit of the discharge power of the energy storage system in the e-th low-carbon residential area, is the lower limit of the energy state of the energy storage system, is the installed capacity of the energy storage system in the e-th low-carbon residential area, τ O is the initial energy state of the energy storage system, T U is the unit time, is the upper limit of the energy state of the energy storage system, tx is any period in a typical day, tn is the last period of a typical day, are the charging energy and discharging energy of the energy storage system in the e-th low-carbon residential area in time period t at season s, is the actual output of distributed photovoltaic power generation in the e-th low-carbon residential area in time period t during season s, is the installed capacity of distributed photovoltaic power generation in the e-th low-carbon residential area, is the output coefficient of distributed photovoltaic in time period t, is the seasonal coefficient of photovoltaic power generation in season s.

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