Intelligent power distribution network full-chain regulation and control model considering full-life-cycle low-carbon and energy-saving

Abstract

The invention discloses an intelligent power distribution network full-chain regulation and control model considering full-life-cycle low-carbon and energy-saving, and belongs to the technical field of intelligent power distribution networks. The invention discloses a full-life-cycle low-carbon and energy-saving intelligent power distribution network full-chain regulation and control model. The model comprises the following steps: S1, constructing an intelligent power distribution network full-chain regulation and control model; s2, carrying out multi-objective optimization scheduling and example simulation; s3, analyzing the power condition of each device; s4, adjusting the intelligent informatization level to obtain the influence of the change of the intelligent informatization level on the cost; and S5, obtaining the most suitable intelligent informatization level. According to the invention, the problem that the energy-saving and emission-reducing effects cannot be well improved in the prior art is solved, the carbon emission can be effectively reduced, the low-carbon and energy-saving potential of system operation can be improved, the whole process of power generation, transmission, distribution and use is disclosed, the low-carbon and energy-saving potential of the intelligent power distribution network can be fully excavated, and the measurement and calculation precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart distribution networks, and in particular to a full-chain control model of a smart distribution network taking into account low carbon and energy conservation throughout the entire life cycle. Background Art

[0002] The life cycle theory was proposed by American economist Raymond Vernon in International Investment and International Trade in the Product Life Cycle in 1966. He pointed out that the life cycle is a dynamic cycle of a company's development and growth, including germination, growth, development, maturity, and decline. The research on the life cycle theory is committed to finding a special organizational form that is suitable for enterprises in different life cycles and can continuously promote the development and continuation of the company, so that the company can find a business model that is consistent with the company's healthy development from internal management, so as to better promote the orderly, efficient, healthy and healthy development of the enterprise. The smart distribution network reveals the whole process of power generation, transmission, distribution and use. The ordinary model cannot well improve the effect of energy conservation and emission reduction. Therefore, it is necessary to provide a full-chain regulation model of the smart distribution network that takes into account the low-carbon and energy-saving of the whole life cycle. Summary of the invention

[0003] The purpose of the present invention is to provide a full-chain control model of an intelligent distribution network that takes into account low-carbon and energy-saving throughout the entire life cycle, which can enhance the low-carbon and energy-saving potential of system operation, and at the same time reveal the entire process of power generation, transmission, distribution and use, which is conducive to fully tapping the low-carbon and energy-saving potential of intelligent distribution networks, improving the measurement accuracy, and solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a full-chain control model of a smart distribution network taking into account low-carbon and energy-saving throughout the entire life cycle, comprising the following steps: S1: Calculate the total cost, constraints and total carbon emissions of the distribution network operation, and build a full-chain regulation model of the smart distribution network that takes into account low carbon and energy saving throughout the life cycle based on the calculation results; S2: After the model is built, the multi-objective particle swarm algorithm is used to solve the model and perform multi-objective optimization scheduling, and then a case simulation is performed; S3: Based on the simulation results, the initial intelligent information level of the smart distribution network is selected to analyze the power status of each device; S4: Adjust the level of intelligence and informationization, compare the data of operating cost and environmental cost, and obtain the impact of the change of intelligence and informationization level on cost; S5: Obtain the most suitable level of intelligence and informationization based on the impact of changes in the level of intelligence and informationization on costs.

[0005] Preferably, the total cost of distribution network operation is calculated in step S1, specifically including: Calculate the cost of purchasing electricity from the upper power grid using the following formula:

[0006] In the formula, is the electricity purchase price of the smart distribution network from the upper power grid during period t, The power purchased by the smart distribution network from the upper power grid during period t; The cost of gas turbine power generation is calculated as follows:

[0007] In the formula, is the gas turbine power generation cost, , and are the power generation cost coefficients of micro-turbines, is the power generation power of the gas turbine in the smart distribution network during period t; Calculate the load demand response compensation cost, the calculation formula is as follows:

[0008] In the formula, Compensate for load demand response costs, is the translation load compensation factor, and are the values ​​of the shiftable load before and after shifting in the smart distribution network in period t, respectively; The cost of curtailing wind and solar power is calculated using the following formula:

[0009] In the formula, Cost of curtailing wind and solar power. Penalty costs for wind curtailment units, Penalty cost for abandoned light units, is the abandoned air volume in period t, is the amount of abandoned light in period t; Calculate the cost of improving intelligent information performance using the following formula:

[0010] In the formula, To improve the cost of intelligent information performance, is the energy storage operation cost coefficient, is the energy storage charging and discharging power in period t; The total operating cost of the distribution network is calculated based on the calculated cost of purchasing electricity from the power grid, the cost of gas turbine power generation, the cost of load demand response compensation, the cost of curtailing wind and solar power, and the cost of improving intelligent information performance.

[0011] Preferably, the total cost of distribution network operation is calculated using the following formula:

[0012]

[0013] In the formula, is the total cost of distribution network operation, is the cost growth coefficient, A is the intelligent information level of the smart distribution network; Preferably, the calculation constraints specifically include: Calculate the distributed wind power output constraint, the calculation formula is as follows:

[0014] In the formula, is the minimum output of distributed wind power, It is the maximum output of distributed wind power; Calculate the distributed photovoltaic output constraint, the calculation formula is as follows:

[0015] In the formula, The maximum output of distributed photovoltaics, It is the minimum output of distributed photovoltaic; Calculate the gas turbine output constraint, the calculation formula is as follows:

[0016] In the formula, is the minimum output of the gas turbine, is the maximum output of the gas turbine, ; Calculate the translational load constraint using the following formula:

[0017]

[0018] In the formula, is the lower limit of the translatable load, is the upper limit of the load that can be translated; Calculate the line power flow constraint, the calculation formula is as follows:

[0019] In the formula, is the lower limit of the transmission power of the smart distribution network line, is the upper limit of the transmission power of the smart distribution network line, Transmit power for smart distribution grid lines; Calculate the active power constraint using the following formula:

[0020] In the formula, is the fixed load in the system, For transferable loads, The output of distributed wind power at time t, Provide power for distributed photovoltaics at time t.

[0021] Preferably, the calculation constraint condition also includes calculation energy storage constraint, and the calculation formula is as follows: Calculate the state of charge of the energy storage at time t, the calculation formula is as follows:

[0022] In the formula, is the energy storage charging power at time t, is the power discharged at time t, The charging power for energy storage, is the energy storage discharge efficiency; =

[0023] The energy storage state of charge is equal at the beginning and end of the cycle.

[0024]

[0025] In the formula, The value is 0 or 1. The maximum power for charging the energy storage, is the maximum power of energy storage discharge;

[0026] In the formula, is the minimum state of charge for energy storage, It is the maximum charge state of energy storage.

[0027] Preferably, the calculation of the total carbon emissions specifically includes: Calculate the carbon emissions generated by purchasing electricity from the upper-level power grid. The calculation formula is as follows

[0028] In the formula, Carbon emissions from purchasing electricity from the upper grid, Carbon emission coefficient for smart distribution network purchasing electricity from upstream power grid; The calculation formula for carbon emissions generated by wind power output and photovoltaic power output over the entire life cycle is as follows:

[0029]

[0030] In the formula, Carbon emissions from wind power generation, The carbon emissions generated by photovoltaic power generation, In order to consider the carbon emission coefficient of wind power output over the entire life cycle, To consider the carbon emission coefficient of photovoltaic output over the entire life cycle; The carbon emissions generated by gas turbine power generation are calculated as follows:

[0031] In the formula, Carbon emissions from gas turbine power generation, The total carbon emissions generated by the operation of the smart distribution network are calculated based on the calculation results of carbon emissions generated by purchasing electricity from the superior power grid, carbon emissions generated by photovoltaic output, carbon emissions generated by wind output, and carbon emissions generated by gas turbine power generation. The calculation formula is as follows:

[0032]

[0033] In the formula, is the total carbon emissions generated by the operation of the smart distribution network. is the emission reduction factor.

[0034] Preferably, the smart distribution network optimization dispatching model is constructed according to the total operating cost of the smart distribution network and the total carbon emissions, which is specifically described as follows:

[0035] Preferably, the multi-objective optimization scheduling specifically includes: S201: Data initialization, input system composition and structure parameters, model parameters, parameter; S202: Input individual particles into the simulation model as system variables, correct the variables that violate the constraints, and calculate the system's operating cost, environmental cost, and penalty items as individual fitness values.

[0036] S203: Using individual fitness as input of the optimization model to obtain the offspring population.

[0037] S204: Determine the individual extreme value pbest, and use pbest as the initial individual extreme value of the particle; S205: sort the population in layers and store the optimal non-dominated Pareto solution into an external archive set; S206: Saved using external archive collection The best solution, gbest is selected from the external set according to the crowding distance of the best solution by using the roulette method; S207: Introduce a small probability random mutation mechanism to generate a small probability disturbance to the particle's original position.

[0038] S208: Return to step S203 until the termination condition is met, and take the termination condition as the maximum number of iterations, and output the final optimization scheduling result.

[0039] Compared with the prior art, the present invention has the following beneficial effects: As the rate of intelligent informationization increases, the present invention can effectively reduce carbon emissions. At the same time, it will cause an increase in operating costs but the increase is relatively low. It can enhance the low-carbon energy-saving potential of system operation and reveal the entire process of power generation, transmission and distribution, which is conducive to fully tapping the low-carbon energy-saving potential of the intelligent distribution network and improving the measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a full-chain control model of an intelligent distribution network taking into account low carbon and energy saving throughout the entire life cycle of the present invention; Figure 2 This is a diagram of optimization results of a full-chain control model of an intelligent distribution network taking into account low-carbon and energy-saving throughout the entire life cycle of the present invention; Figure 3 It is an environmental cost diagram of a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle of the present invention; Figure 4 This is an operating cost diagram of the full-chain control model of an intelligent distribution network that takes into account low carbon and energy conservation throughout the entire life cycle of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In order to solve the problem that the existing technology cannot effectively improve the effect of energy conservation and emission reduction, please refer to Figure 1 , this embodiment provides the following technical solutions: A full-chain regulation model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle includes the following steps: S1: Calculate the total operating cost, constraints and total carbon emissions of the distribution network, and build a full-chain control model of the smart distribution network that takes into account low carbon and energy conservation throughout the entire life cycle based on the calculation results. The model has two objective functions. The first objective function is to minimize the system operating cost, and the second objective function is to minimize the environmental cost.

[0043] S2: After the model is built, the multi-objective particle swarm algorithm is used to solve the model and perform multi-objective optimization scheduling, and then a case simulation is performed; S3: Based on the simulation results, the initial intelligent information level of the smart distribution network is selected, which is 40%, and the power of each device is analyzed; S4: Adjust the level of intelligence and informationization, compare the data of operating cost and environmental cost, and obtain the impact of the change of intelligence and informationization level on cost; S5: Obtain the most suitable level of intelligent informationization based on the impact of changes in the level of intelligent informationization on costs. By comparing the data on operating costs and environmental costs, we can obtain the impact of changes in the level of intelligent informationization on costs, and find the most suitable level of intelligent informationization to tap the greatest potential for low-carbon emission reductions.

[0044] The total cost of distribution network operation is calculated in step S1, which specifically includes: Calculate the cost of purchasing electricity from the upper power grid using the following formula:

[0045] In the formula, is the electricity purchase price of the smart distribution network from the upper power grid during period t, The power purchased by the smart distribution network from the upper power grid during period t; The cost of gas turbine power generation is calculated as follows:

[0046] In the formula, is the gas turbine power generation cost, , and are the power generation cost coefficients of micro-turbines, is the power generation power of the gas turbine in the smart distribution network during period t; Calculate the load demand response compensation cost, the calculation formula is as follows:

[0047] In the formula, Compensate for load demand response costs, is the translation load compensation factor, and are the values ​​of the shiftable load before and after shifting in the smart distribution network in period t, respectively; The cost of curtailing wind and solar power is calculated using the following formula:

[0048] In the formula, Cost of curtailing wind and solar power. Penalty costs for wind curtailment units, Penalty cost for abandoned light units, is the abandoned air volume in period t, is the amount of abandoned light in period t; Calculate the cost of improving intelligent information performance using the following formula:

[0049] In the formula, To improve the cost of intelligent information performance, is the energy storage operation cost coefficient, is the energy storage charging and discharging power in period t; The total operating cost of the distribution network is calculated based on the calculated cost of purchasing electricity from the power grid, the cost of gas turbine power generation, the cost of load demand response compensation, the cost of curtailing wind and solar power, and the cost of improving intelligent information performance.

[0050] Calculate the total cost of distribution network operation, the calculation formula is as follows:

[0051]

[0052] In the formula, is the total cost of distribution network operation, is the cost growth coefficient, A is the intelligent information degree of the smart distribution network, and the intelligent information degree of the smart distribution network is expressed by the distribution automation coverage rate. The intelligent information degree affects the overall operation cost of the smart distribution network.

[0053] Compute constraints, including: Calculate the distributed wind power output constraint, the calculation formula is as follows:

[0054] In the formula, is the minimum output of distributed wind power, It is the maximum output of distributed wind power; Calculate the distributed photovoltaic output constraint, the calculation formula is as follows:

[0055] In the formula, The maximum output of distributed photovoltaics, It is the minimum output of distributed photovoltaic; Calculate the gas turbine output constraint, the calculation formula is as follows:

[0056] In the formula, is the minimum output of the gas turbine, is the maximum output of the gas turbine, ; Calculate the translational load constraint using the following formula:

[0057]

[0058] In the formula, is the lower limit of the translatable load, is the upper limit of the load that can be translated; Calculate the line power flow constraint, the calculation formula is as follows:

[0059] In the formula, is the lower limit of the transmission power of the smart distribution network line, is the upper limit of the transmission power of the smart distribution network line, Transmit power for smart distribution grid lines; Calculate the active power constraint using the following formula:

[0060] In the formula, is the fixed load in the system, For transferable loads, The output of distributed wind power at time t, Provide power for distributed photovoltaics at time t.

[0061] The calculation constraints also include the calculation energy storage constraints, and the calculation formula is as follows: Calculate the state of charge of the energy storage at time t, the calculation formula is as follows:

[0062] In the formula, is the energy storage charging power at time t, is the power discharged at time t, The charging power for energy storage, is the energy storage discharge efficiency; =

[0063] The energy storage state of charge is equal at the beginning and end of the cycle.

[0064]

[0065] In the formula, The value is 0 or 1, which means that the energy storage cannot be charged and discharged at the same time. The maximum power for charging the energy storage, is the maximum power of energy storage discharge;

[0066] In the formula, is the minimum state of charge for energy storage, It is the maximum charge state of energy storage.

[0067] Calculate the total carbon emissions, including: On this basis, we further consider the goal of minimizing environmental costs during the operation of the distribution network. Improving the degree of intelligence and informationization can reduce the carbon emissions of system operation. Calculate the carbon emissions generated by purchasing electricity from the upper-level power grid. The calculation formula is as follows

[0068] In the formula, Carbon emissions from purchasing electricity from the upper grid, Carbon emission coefficient for smart distribution network purchasing electricity from upstream power grid; The calculation formula for carbon emissions generated by wind power output and photovoltaic power output over the entire life cycle is as follows:

[0069]

[0070] In the formula, Carbon emissions from wind power generation, The carbon emissions generated by photovoltaic power generation, In order to consider the carbon emission coefficient of wind power output over the entire life cycle, To consider the carbon emission coefficient of photovoltaic output over the entire life cycle; The carbon emissions generated by gas turbine power generation are calculated as follows:

[0071] In the formula, Carbon emissions from gas turbine power generation, The total carbon emissions generated by the operation of the smart distribution network are calculated based on the calculation results of carbon emissions generated by purchasing electricity from the superior power grid, carbon emissions generated by photovoltaic output, carbon emissions generated by wind output, and carbon emissions generated by gas turbine power generation. The calculation formula is as follows:

[0072]

[0073] In the formula, is the total carbon emissions generated by the operation of the smart distribution network. is the emission reduction factor.

[0074] According to the total operating cost and total carbon emissions of the smart distribution network, an optimal dispatching model for the smart distribution network is constructed. The specific description is as follows:

[0075] Multi-objective optimization scheduling, including: S201: Data initialization, input system composition and structure parameters, model parameters, Parameters, and initialize the particle population at the same time. Each particle in the population corresponds to a scheduling plan within a scheduling cycle; S202: Input individual particles into the simulation model as system variables, correct the variables that violate the constraints, and calculate the system's operating cost, environmental cost, and penalty items as individual fitness values.

[0076] S203: Using individual fitness as input of the optimization model to obtain the offspring population.

[0077] S204: Determine the individual extreme value pbest, and use pbest as the initial individual extreme value of the particle. If the current particle dominates pbest, then the current particle is used as the individual extreme value of pbest. If the two cannot be compared, then calculate the number of other particles dominated by the two in the group, and the one that dominates more is used as the individual extreme value pbest. S205: sort the population in layers, store the optimal non-dominated solution Pareto into an external archive set, remove non-Pareto solutions, and determine whether the external archive set exceeds the specified capacity. If so, select m particles according to the crowding distance; S206: global optimal value gbest, saved in an external archive collection The best solution, gbest is selected from the external set according to the crowding distance of the best solution by using the roulette method; S207: Small probability mutation. In order to prevent the multi-objective particle swarm optimization algorithm from converging to the local optimal frontier rather than the global optimal frontier too early, a small probability random mutation mechanism is introduced to generate a small probability disturbance to the particle position on the original position. The small probability is ±30%, which increases the particle's ability to find the global optimal frontier.

[0078] S208: Return to step S203 until the termination condition is met. The termination condition is set as the maximum number of iterations, and the final optimization scheduling result is output. Figure 2 In the analysis, A=40% is selected as the standard value of the intelligent information degree of the smart distribution network, and Matlab software is used for simulation. At this time, the operating cost is 183,350.96 yuan and the environmental cost is 57,780.80 yuan. In this system, the peak electricity consumption period occurs between 11 a.m. and 13 p.m.

[0079] Depend on Figure 3 From Table 1, we can see Running costs 142432.89 183350.96 187762.60 195938.58 186163.81 203684.72 Environmental costs 60094.85 57780.80 46342.73 40727.77 37867.66 40525.89 A 0.3 0.4 0.5 0.6 0.7 0.8 With the increase of intelligent informationization rate, environmental costs have dropped significantly. Intelligent informationization is crucial to the low-carbon operation of smart distribution networks.

[0080] Depend on Figure 4 It can be seen that with the increase in the rate of intelligent informatization, the operating cost gradually increases, but the increase is small.

[0081] To sum up, as the rate of intelligent informationization increases, carbon emissions can be effectively reduced. At the same time, it will cause an increase in operating costs, but the increase will be relatively low. The low-carbon energy-saving potential of the smart distribution network reaches its maximum level when the level of intelligent informationization reaches 70%. Continuing to improve the level of intelligent informationization will lead to a rapid increase in infrastructure construction and equipment operating costs, which will increase low-carbon costs.

[0082] In summary, the present invention is a full-chain control model of an intelligent distribution network that takes into account low carbon and energy saving throughout the entire life cycle. As the rate of intelligence and informatization increases, the present invention can effectively reduce carbon emissions. At the same time, it will cause an increase in operating costs, but the increase is relatively low. It can enhance the low-carbon and energy-saving potential of system operation, and at the same time, it reveals the entire process of power generation, transmission and distribution, which is conducive to fully tapping the low-carbon and energy-saving potential of intelligent distribution networks and improving the measurement accuracy.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0084] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A full-chain control model of smart distribution network considering low carbon and energy saving throughout the whole life cycle, characterized in that: The following steps are involved: S1: Calculate the total cost, constraints and total carbon emissions of the distribution network operation, and build a full-chain regulation model of the smart distribution network that takes into account low carbon and energy saving throughout the life cycle based on the calculation results; S2: After the model is built, the multi-objective particle swarm algorithm is used to solve the model and perform multi-objective optimization scheduling, and then a case simulation is performed; S3: Based on the simulation results, the initial intelligent information level of the smart distribution network is selected to analyze the power status of each device; S4: Adjust the level of intelligence and informationization, compare the data of operating cost and environmental cost, and obtain the impact of the change of intelligence and informationization level on cost; S5: Obtain the most suitable level of intelligence and informationization based on the impact of changes in the level of intelligence and informationization on costs.

2. According to claim 1, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: The total cost of distribution network operation is calculated in step S1, specifically including: Calculate the cost of purchasing electricity from the upper power grid using the following formula: ； In the formula, is the electricity purchase price of the smart distribution network from the upper power grid during period t, The power purchased by the smart distribution network from the upper power grid during period t; The cost of gas turbine power generation is calculated as follows: ； In the formula, is the gas turbine power generation cost, , and are the power generation cost coefficients of micro-turbines, is the power generation power of the gas turbine in the smart distribution network during period t; Calculate the load demand response compensation cost, the calculation formula is as follows: ； In the formula, Compensate for load demand response costs, is the translation load compensation factor, and are the values ​​of the shiftable load before and after shifting in the smart distribution network in period t, respectively; The cost of curtailing wind and solar power is calculated using the following formula: ； In the formula, Cost of curtailing wind and solar power. Penalty costs for wind curtailment units, Penalty cost for abandoned light units, is the abandoned air volume in period t, is the amount of abandoned light in period t; Calculate the cost of improving intelligent information performance using the following formula: ； In the formula, To improve the cost of intelligent information performance, is the energy storage operation cost coefficient, is the energy storage charging and discharging power in period t; The total operating cost of the distribution network is calculated based on the calculated cost of purchasing electricity from the power grid, the cost of gas turbine power generation, the cost of load demand response compensation, the cost of curtailing wind and solar power, and the cost of improving intelligent information performance.

3. According to claim 2, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: The total cost of distribution network operation is calculated by the following formula: ； ； In the formula, is the total cost of distribution network operation, is the cost growth coefficient, and A is the degree of intelligence and informationization of the smart distribution network.

4. According to claim 1, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: The computing constraints specifically include: Calculate the distributed wind power output constraint, the calculation formula is as follows: ； In the formula, is the minimum output of distributed wind power, It is the maximum output of distributed wind power; Calculate the distributed photovoltaic output constraint, the calculation formula is as follows: ； In the formula, The maximum output of distributed photovoltaics, It is the minimum output of distributed photovoltaic; Calculate the gas turbine output constraint, the calculation formula is as follows: ； In the formula, is the minimum output of the gas turbine, is the maximum output of the gas turbine, ; Calculate the translational load constraint using the following formula: ； ； In the formula, is the lower limit of the translatable load, is the upper limit of the load that can be translated; Calculate the line power flow constraint, the calculation formula is as follows: ； In the formula, is the lower limit of the transmission power of the smart distribution network line, is the upper limit of the power transmission of the smart distribution network line, Transmit power for smart distribution grid lines; Calculate the active power constraint using the following formula: ； In the formula, is the fixed load in the system, For transferable loads, The output of distributed wind power at time t, Provide power for distributed photovoltaics at time t.

5. According to claim 4, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the life cycle is characterized in that: The calculation constraint condition also includes calculation energy storage constraint, and the calculation formula is as follows: Calculate the state of charge of the energy storage at time t, the calculation formula is as follows: ； In the formula, is the energy storage charging power at time t, is the power discharged at time t, The charging power for energy storage, is the energy storage discharge efficiency; = ； The energy storage state of charge is equal at the beginning and end of the cycle. ； ； In the formula, The value is 0 or 1. The maximum power for charging the energy storage, is the maximum power of energy storage discharge; ； In the formula, is the minimum state of charge for energy storage, It is the maximum charge state of energy storage.

6. According to claim 1, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: The calculation of total carbon emissions specifically includes: Calculate the carbon emissions generated by purchasing electricity from the upper-level power grid. The calculation formula is as follows ； In the formula, Carbon emissions from purchasing electricity from the upper grid, Carbon emission coefficient for smart distribution network purchasing electricity from upstream power grid; The calculation formula for carbon emissions generated by wind power output and photovoltaic power output over the entire life cycle is as follows: ； ； In the formula, Carbon emissions from wind power generation, The carbon emissions generated by photovoltaic power generation, In order to consider the carbon emission coefficient of wind power output over the entire life cycle, To consider the carbon emission coefficient of photovoltaic output over the entire life cycle; The carbon emissions generated by gas turbine power generation are calculated using the following formula: ； In the formula, Carbon emissions from gas turbine power generation, The total carbon emissions generated by the operation of the smart distribution network are calculated based on the calculation results of carbon emissions generated by purchasing electricity from the superior power grid, carbon emissions generated by photovoltaic output, carbon emissions generated by wind output, and carbon emissions generated by gas turbine power generation. The calculation formula is as follows: ； ； In the formula, is the total carbon emissions generated by the operation of the smart distribution network. is the emission reduction factor.

7. According to claim 6, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: According to the total operating cost and total carbon emissions of the smart distribution network, an optimal dispatching model for the smart distribution network is constructed, which is specifically described as follows:

8. According to claim 1, a full-chain control model of a smart distribution network taking into account low carbon and energy saving throughout the entire life cycle is characterized by: The multi-objective optimization scheduling specifically includes: S201: Data initialization, input system composition and structure parameters, model parameters, and multi-objective particle swarm optimization algorithm parameters; S202: Input individual particles into the simulation model as system variables, correct the variables that violate the constraints, and calculate the system's operating cost, environmental cost, and penalty items as individual fitness values; S203: Using individual fitness as input of the optimization model to obtain the offspring population; S204: Determine the individual extreme value pbest, and use pbest as the initial individual extreme value of the particle; S205: sort the population in layers and store the optimal non-dominated Pareto solution into an external archive set; S206: using the Pareto optimal solution saved in the external archive set, and referencing the roulette wheel method to select gbest from the external set according to the crowding distance of the optimal solution; S207: Introduce a small probability random mutation mechanism to generate a small probability disturbance to the particle's original position; S208: Return to step S203 until the termination condition is met, and take the termination condition as the maximum number of iterations, and output the final optimization scheduling result.

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