Zone area voltage coordinated optimization method and system considering carbon saving

Abstract

The invention discloses a transformer area voltage coordinated optimization method and system considering carbon saving. According to the method, the output of the capacitor bank, the SVG and the photovoltaic inverter is dynamically adjusted according to the power-voltage coefficient relation table, and the problems of voltage overshoot and undershoot caused by photovoltaic power generation fluctuation are effectively solved. And the equipment response is rapid, the voltage of each node in the transformer area is ensured to be within an allowable range, the occurrence of overvoltage and undervoltage is avoided, and the operation reliability of a power grid is improved. In combination with a transformer load rate and life loss coefficient model, the system can optimize scheduling in real time according to the load state of the transformer, and premature aging caused by long-term high-load operation of the transformer is avoided. Through reasonable load distribution, the additional influence of carbon emission on the service life of the transformer is reduced, and long service life and high efficiency of transformer operation are realized. The optimized scheduling strategy enables the capacitor bank, the SVG and the photovoltaic inverter to work cooperatively, thereby reducing the overall energy loss of the system, and indirectly reducing the carbon emission.

Description

Technical Field

[0001] The present invention belongs to the technical field of voltage coordination optimization, and specifically relates to a method and system for voltage coordination optimization in a transformer substation considering carbon saving. Background Art

[0002] The distribution system not only needs to ensure the stability of the grid operation, but also needs to minimize carbon emissions. In the area where a large number of distributed photovoltaic systems are connected, how to coordinate the control of capacitor banks, static VAR generators (SVGs), and photovoltaic inverters, and combine them with transformer load conditions and carbon emission factors, is a key issue that needs to be solved urgently. Traditional voltage optimization methods mainly focus on using capacitors and on-load tap transformers (OLTCs) for reactive power compensation and voltage adjustment.

[0003] However, the traditional method has the following shortcomings:

[0004] 1) Slow equipment response: Traditional capacitors and OLTCs have a long response time and are unable to cope with rapid changes in PV output and load fluctuations.

[0005] 2) Ignoring transformer aging and life loss: The life of distribution transformers will be shortened rapidly under long-term high-load operation, but the existing methods do not include transformer life loss and its carbon emission costs throughout its life cycle into the optimization control.

[0006] 3) Insufficient photovoltaic power consumption: During the peak period of photovoltaic power generation, part of the electricity is fed back to the upper-level power grid, resulting in energy waste and additional losses, while aggravating the voltage fluctuation problem.

[0007] Therefore, a new substation optimization method is needed to coordinately control the status and carbon emission impact of capacitors, SVG, photovoltaic inverters and transformers, minimize carbon emissions and improve the photovoltaic absorption level while ensuring grid stability. Summary of the invention

[0008] The purpose of the present invention is to provide a method and system for coordinated optimization of transformer substation voltage taking carbon saving into consideration in order to solve the above-mentioned problems.

[0009] The technical solution adopted by the present invention is as follows: a method for coordinated optimization of voltage in a substation considering carbon saving, the method comprising the following steps:

[0010] S1: Data collection and initialization:

[0011] Collect real-time data such as voltage of each node in the substation, load power, photovoltaic power generation power, etc.

[0012] Initialization parameters include transformer capacity, target voltage, weight coefficient, transformer life cycle carbon emissions, power-voltage coefficient of each device, etc.

[0013] S2: Establish equipment control strategies for capacitor banks, SVGs, and PV inverters;

[0014] S3: Establish transformer load and life model:

[0015] A life loss model is established using the transformer load rate and life loss coefficient to calculate the life loss of the transformer under the current load.

[0016] Consider the carbon emissions of the transformer throughout its life cycle, calculate the additional carbon emissions during operation, and incorporate the carbon emission costs into the optimization model.

[0017] S4: Establish optimization objective function:

[0018] A multi-objective optimization model is constructed with the goals of node voltage stability, minimization of transformer carbon emissions and maximization of photovoltaic consumption.

[0019] The objective function includes indicators such as node voltage deviation, additional transformer carbon emissions and PV local power consumption, and is weighted according to the weight coefficient.

[0020] S5: Determine the constraints:

[0021] Set node voltage constraints to ensure that each node voltage is within the allowable range.

[0022] Set transformer load rate constraints to avoid transformer overload.

[0023] Set reactive power constraints for SVG and capacitors to ensure safe operation of equipment.

[0024] Set PV inverter power constraints to ensure efficient use of PV power.

[0025] Set the power-voltage coefficient relationship between SVG, capacitor and PV inverter to calculate the device regulation power.

[0026] S6: Select optimization algorithm: Select a suitable optimization algorithm, such as genetic algorithm, particle swarm algorithm, etc., according to the scale of the station and computing requirements.

[0027] S7: Solve the optimization model: Input the collected data and model parameters into the optimization algorithm to solve the optimal control power of SVG, capacitor and photovoltaic inverter.

[0028] S8: Send control instructions: Convert the optimization results into equipment control instructions and send them to the capacitor bank, SVG and PV inverter for real-time regulation.

[0029] S9: Monitoring and adjustment: Continuously monitor the substation operation status and equipment status, and adjust the optimization model parameters and equipment control strategies according to actual conditions to ensure stable operation of the system, after which the entire substation voltage coordination optimization process considering carbon saving can be completed.

[0030] In a preferred embodiment, in step S1:

[0031] Real-time data collection methods include:

[0032] Voltage data collection: Use high-precision voltage sensors to monitor the voltage of each node in the substation in real time to ensure the accuracy and stability of the data. The sensor should have network communication function to transmit data to the energy management system in real time.

[0033] Load power monitoring: By installing smart meters, real-time power consumption data of each node load is collected to achieve dynamic monitoring of the load status of the substation.

[0034] Photovoltaic power generation monitoring: Utilize the data interface of the photovoltaic power generation system to obtain the power generation of photovoltaic panels in real time in order to optimize energy distribution and use.

[0035] Parameter initialization methods include:

[0036] Transformer capacity setting: Determine the rated capacity of the transformer based on the maximum load demand and future development plan of the substation, and initialize the parameters through the system.

[0037] Target voltage setting: According to the grid standards and equipment operation requirements, the target voltage value of each node in the substation is set and initialized in the system.

[0038] Weight coefficient configuration: According to the operating characteristics and importance of different equipment, weight coefficients are assigned to parameters such as voltage, load power, photovoltaic power generation, etc., so as to perform optimization calculations in the energy management system.

[0039] Transformer life cycle carbon emissions calculation: Combine the rated capacity, operating efficiency and service life of the transformer to estimate its carbon emissions over the entire life cycle and initialize it in the system.

[0040] Equipment power-voltage coefficient determination: By testing the power consumption characteristics of each device at different voltages, the power-voltage coefficient is determined and initialized in the system for energy efficiency analysis and optimized control.

[0041] In a preferred embodiment, in step S2:

[0042] The control strategy of the capacitor bank is: switching control to compensate reactive power and maintain voltage stability.

[0043] The SVG control strategy is to dynamically adjust reactive power output to achieve rapid response to load fluctuations.

[0044] The control strategy of the photovoltaic inverter is to adjust the active and reactive power according to the power-voltage coefficient relationship table to achieve photovoltaic priority consumption.

[0045] In a preferred embodiment, in step S3, the transformer load rate and the life loss coefficient are used to establish a life loss model:

[0046]

[0047] Where P load is the load power, S rated is the rated capacity of the transformer, α and n are empirical coefficients.

[0048] Considering the carbon emissions of the transformer throughout its life cycle, the formula for calculating the additional carbon emissions during operation is:

[0049]

[0050] Of which CO2 lifecycle Set a value for the carbon footprint of the transformer's life cycle.

[0051] In a preferred embodiment, the step S4 specifically includes:

[0052] A multi-objective optimization model is constructed with the goals of node voltage stability, transformer carbon emission minimization and photovoltaic consumption maximization:

[0053]

[0054] Where V i represents the voltage of the ith node; V ref Indicates the target voltage;

[0055] Indicates additional carbon emissions from transformers;

[0056] Indicates the power consumed by photovoltaic power on site;

[0057] ω1, ω2, ω3 represent weight coefficients.

[0058] In a preferred implementation, in step S5, the node voltage constraint condition is:

[0059]

[0060] The transformer load factor constraint is:

[0061] The reactive power constraints of SVG and capacitor are:

[0062] The power constraints of the photovoltaic inverter are:

[0063] In a preferred embodiment, in step S5, the relationship between the reactive power regulated by SVG and the voltage coefficient of each node is calculated as follows: Q SVG,i =k SVG,i ·(V i -V ref );

[0064] Where Q SVG,i represents the reactive power of SVG at node i;

[0065] k SVG,i It represents the voltage-reactive power coefficient of SVG at node i.

[0066] In a preferred embodiment, in step S5, the relationship between the capacitor-controlled reactive power and the voltage coefficient of each node is calculated as follows: Q cap,i =k cap,i ·(V i -V ref );

[0067] Where Q cap,i represents the reactive power of the capacitor at node i;

[0068] k cap,i Represents the voltage-reactive coefficient of the capacitor at node i.

[0069] In a preferred embodiment, in step S5, the calculation formula for the relationship between the active power and reactive power regulated by the photovoltaic inverter and the voltage coefficient of each node is:

[0070]

[0071]

[0072] Where: P PV,i represents the active power of the PV inverter at node i;

[0073] Q PV,i represents the reactive power of the PV inverter at node i;

[0074] represents the voltage-active power coefficient of the PV inverter at node i;

[0075] Represents the voltage-reactive power factor of the volt inverter at node i.

[0076] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0077] 1. In the present invention, by dynamically adjusting the output of the capacitor bank, SVG and photovoltaic inverter according to the power-voltage coefficient relationship table, the present invention effectively solves the voltage overshoot and undershoot problems caused by photovoltaic power generation fluctuations. The equipment responds quickly, ensuring that the voltage of each node in the substation is within the allowable range, avoiding the occurrence of overvoltage and undervoltage, and improving the operational reliability of the power grid. Combined with the transformer load rate and life loss coefficient model, the system can optimize the scheduling in real time according to the load state of the transformer to avoid premature aging caused by long-term high-load operation of the transformer. Taking into account the carbon emissions of the transformer throughout its life cycle, the additional impact of carbon emissions on the life of the transformer is reduced by reasonably distributing the load, thereby achieving a long life and high efficiency of the transformer operation.

[0078] 2. In the present invention, the active and reactive power of the photovoltaic inverter is dynamically adjusted to realize the on-site consumption of photovoltaic power, reduce the loss of photovoltaic power sent back to the upper power grid, and improve the utilization rate of photovoltaic power generation. The system can give priority to absorbing renewable energy during the peak period of photovoltaic power generation, effectively reduce the consumption of fossil energy, and realize the efficient utilization of clean energy. Incorporating the carbon emissions of the transformer life cycle into the optimization model, the system not only pays attention to voltage stability and power loss, but also considers the economic efficiency of carbon emissions, and promotes green operation of the substation. The optimized scheduling strategy enables the capacitor bank, SVG and photovoltaic inverter to work together, reducing the overall energy loss of the system and indirectly reducing carbon emissions.

[0079] 3. In the present invention, the system ensures the coordinated operation of capacitors, SVG and photovoltaic inverters by integrating terminal control, and dynamically adjusts the equipment output according to the real-time operating status of the substation, thus achieving global optimal intelligent control. Through the adaptive optimization algorithm, the system can cope with different loads and photovoltaic power generation fluctuations, improving the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A flow chart of the fusion terminal of the present invention reading the status of each device and issuing instructions;

[0081] Figure 2 The node in the present invention is a load node, taking node i as an example, a schematic diagram of the power-voltage coefficient of the power control device and the node i. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0083] Reference Figure 1-2 ,

[0084] A method for optimizing coordinated voltage in a substation considering carbon saving, the method comprising the following steps:

[0085] S1: Data collection and initialization:

[0086] Collect real-time data such as voltage of each node in the substation, load power, photovoltaic power generation power, etc.

[0087] Initialization parameters include transformer capacity, target voltage, weight coefficient, transformer life cycle carbon emissions, power-voltage coefficient of each device, etc.

[0088] S2: Establish equipment control strategies for capacitor banks, SVGs, and PV inverters;

[0089] S3: Establish transformer load and life model:

[0090] A life loss model is established using the transformer load rate and life loss coefficient to calculate the life loss of the transformer under the current load.

[0091] Consider the carbon emissions of the transformer throughout its life cycle, calculate the additional carbon emissions during operation, and incorporate the carbon emission costs into the optimization model.

[0092] S4: Establish optimization objective function:

[0093] A multi-objective optimization model is constructed with the goals of node voltage stability, minimization of transformer carbon emissions and maximization of photovoltaic consumption.

[0094] The objective function includes indicators such as node voltage deviation, additional transformer carbon emissions and PV local power consumption, and is weighted according to the weight coefficient.

[0095] S5: Determine the constraints:

[0096] Set node voltage constraints to ensure that each node voltage is within the allowable range.

[0097] Set transformer load rate constraints to avoid transformer overload.

[0098] Set reactive power constraints for SVG and capacitors to ensure safe operation of equipment.

[0099] Set PV inverter power constraints to ensure efficient use of PV power.

[0100] Set the power-voltage coefficient relationship between SVG, capacitor and PV inverter to calculate the device regulation power.

[0101] S6: Select optimization algorithm: Select a suitable optimization algorithm, such as genetic algorithm, particle swarm algorithm, etc., according to the scale of the station and computing requirements.

[0102] S7: Solve the optimization model: Input the collected data and model parameters into the optimization algorithm to solve the optimal control power of SVG, capacitor and photovoltaic inverter.

[0103] S8: Send control instructions: Convert the optimization results into equipment control instructions and send them to the capacitor bank, SVG and PV inverter for real-time regulation.

[0104] S9: Monitoring and adjustment: Continuously monitor the substation operation status and equipment status, and adjust the optimization model parameters and equipment control strategies according to actual conditions to ensure stable operation of the system, after which the entire substation voltage coordination optimization process considering carbon saving can be completed.

[0105] In step S1:

[0106] Real-time data collection methods include:

[0107] Voltage data collection: Use high-precision voltage sensors to monitor the voltage of each node in the substation in real time to ensure the accuracy and stability of the data. The sensor should have network communication function to transmit data to the energy management system in real time.

[0108] Load power monitoring: By installing smart meters, real-time power consumption data of each node load is collected to achieve dynamic monitoring of the load status of the substation.

[0109] Photovoltaic power generation monitoring: Utilize the data interface of the photovoltaic power generation system to obtain the power generation of photovoltaic panels in real time in order to optimize energy distribution and use.

[0110] Parameter initialization methods include:

[0111] Transformer capacity setting: Determine the rated capacity of the transformer based on the maximum load demand and future development plan of the substation, and initialize the parameters through the system.

[0112] Target voltage setting: According to the grid standards and equipment operation requirements, the target voltage value of each node in the substation is set and initialized in the system.

[0113] Weight coefficient configuration: According to the operating characteristics and importance of different equipment, weight coefficients are assigned to parameters such as voltage, load power, photovoltaic power generation, etc., so as to perform optimization calculations in the energy management system.

[0114] Transformer life cycle carbon emissions calculation: Combine the rated capacity, operating efficiency and service life of the transformer to estimate its carbon emissions over the entire life cycle and initialize it in the system.

[0115] Equipment power-voltage coefficient determination: By testing the power consumption characteristics of each device at different voltages, the power-voltage coefficient is determined and initialized in the system for energy efficiency analysis and optimized control.

[0116] In step S2:

[0117] The control strategy of the capacitor bank is: switching control to compensate reactive power and maintain voltage stability.

[0118] The SVG control strategy is to dynamically adjust reactive power output to achieve rapid response to load fluctuations.

[0119] The control strategy of the photovoltaic inverter is to adjust the active and reactive power according to the power-voltage coefficient relationship table to achieve photovoltaic priority consumption.

[0120] In step S3, the transformer load factor and the life loss coefficient are used to establish a life loss model:

[0121]

[0122] Where P load is the load power, S rated is the rated capacity of the transformer, α and n are empirical coefficients.

[0123] Considering the carbon emissions of the transformer throughout its life cycle, the formula for calculating the additional carbon emissions during operation is:

[0124]

[0125] in Set a value for the carbon footprint of the transformer's life cycle.

[0126] Step S4 specifically includes:

[0127] A multi-objective optimization model is constructed with the goals of node voltage stability, transformer carbon emission minimization and photovoltaic consumption maximization:

[0128]

[0129] Where V i represents the voltage of the ith node; V ref Indicates the target voltage;

[0130] Indicates additional carbon emissions from transformers;

[0131] Indicates the power consumed by photovoltaic power on site;

[0132] ω1, ω2, ω3 represent weight coefficients.

[0133] In step S5, the node voltage constraint condition is:

[0134]

[0135] The transformer load factor constraint is:

[0136] The reactive power constraints of SVG and capacitor are:

[0137] The power constraints of the photovoltaic inverter are:

[0138] In step S5, the relationship between the reactive power regulated by SVG and the voltage coefficient of each node is calculated as follows: Q SVG,i =k SVG,i ·(V i -V ref );

[0139] Where Q SVG,i represents the reactive power of SVG at node i;

[0140] k SVG,i It represents the voltage-reactive power coefficient of SVG at node i.

[0141] In step S5, the relationship between the reactive power controlled by the capacitor and the voltage coefficient of each node is calculated as follows: Q cap,i =k cap,i ·(V i -V ref );

[0142] Where Q cap,i represents the reactive power of the capacitor at node i;

[0143] k cap,i Represents the voltage-reactive coefficient of the capacitor at node i.

[0144] In step S5, the calculation formula for the relationship between the active power and reactive power of the photovoltaic inverter and the voltage coefficient of each node is:

[0145]

[0146]

[0147] Where: P PV,i Represents the active power of the PV inverter at node i.

[0148] Q PV,i Represents the reactive power of the PV inverter at node i

[0149] Represents the voltage-power factor of the photovoltaic inverter at node i

[0150] Represents the voltage-reactive power factor of the volt inverter at node i.

[0151] In the present invention, by dynamically adjusting the output of the capacitor bank, SVG and photovoltaic inverter according to the power-voltage coefficient relationship table, the present invention effectively solves the voltage overshoot and undershoot problems caused by photovoltaic power generation fluctuations. The equipment responds quickly, ensuring that the voltage of each node in the substation is within the allowable range, avoiding the occurrence of overvoltage and undervoltage, and improving the operational reliability of the power grid. Combined with the transformer load rate and life loss coefficient model, the system can optimize the scheduling in real time according to the load state of the transformer, avoiding premature aging of the transformer caused by long-term high-load operation. Considering the carbon emissions of the transformer throughout its life cycle, the additional impact of carbon emissions on the life of the transformer is reduced by reasonably distributing the load, and the long life and high efficiency of the transformer operation are achieved.

[0152] In the present invention, the active and reactive power of the photovoltaic inverter is dynamically adjusted to realize the on-site consumption of photovoltaic power, reduce the loss of photovoltaic power sent back to the upper power grid, and improve the utilization rate of photovoltaic power generation. The system can preferentially consume renewable energy during the peak period of photovoltaic power generation, effectively reduce the consumption of fossil energy, and realize the efficient utilization of clean energy. Incorporating the carbon emissions of the transformer life cycle into the optimization model, the system not only pays attention to voltage stability and power loss, but also considers the economic efficiency of carbon emissions, and promotes the green operation of the substation. The optimized scheduling strategy enables the capacitor bank, SVG and photovoltaic inverter to work together, reducing the overall energy loss of the system and indirectly reducing carbon emissions.

[0153] In the present invention, the system ensures the coordinated operation of capacitors, SVG and photovoltaic inverters by integrating terminal control, and dynamically adjusts the equipment output according to the real-time operating status of the substation, thus achieving global optimal intelligent control. Through the adaptive optimization algorithm, the system can cope with different load and photovoltaic power generation fluctuation scenarios, improving the flexibility and adaptability of the system.

[0154] 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 term "comprises" or any other variant thereof is 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. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing coordinated voltage in a power distribution area considering carbon saving, characterized in that: The method comprises the following steps: S1: Data collection and initialization: Collect real-time data such as voltage, load power, photovoltaic power generation power, etc. at each node in the substation; Initialization parameters, including transformer capacity, target voltage, weight coefficient, transformer life cycle carbon emissions, power-voltage coefficient of each device, etc.; S2: Establish equipment control strategies for capacitor banks, SVGs, and PV inverters; S3: Establish transformer load and life model: Use the transformer load rate and life loss coefficient to establish a life loss model to calculate the transformer life loss under the current load; Consider the carbon emissions of the transformer throughout its life cycle, calculate the additional carbon emissions during operation, and incorporate the carbon emission costs into the optimization model; S4: Establish optimization objective function: A multi-objective optimization model is constructed with the goals of node voltage stability, transformer carbon emission minimization and PV absorption maximization. The objective function includes indicators such as node voltage deviation, additional transformer carbon emissions and PV local power consumption, and is weighted according to the weight coefficient; S5: Determine the constraints: Set node voltage constraints to ensure that each node voltage is within the allowable range; Set transformer load rate constraints to avoid transformer overload operation; Set reactive power constraints for SVG and capacitors to ensure safe operation of equipment; Set PV inverter power constraints to ensure efficient use of PV power; Set the power-voltage coefficient relationship between SVG, capacitor and PV inverter to calculate the device regulation power; S6: Select optimization algorithm: Select a suitable optimization algorithm, such as genetic algorithm, particle swarm algorithm, etc., according to the scale of the station area and computing requirements; S7: Solving the optimization model: inputting the collected data and model parameters into the optimization algorithm to solve the optimal control power of SVG, capacitor and photovoltaic inverter; S8: Sending control instructions: Converting the optimization results into equipment control instructions and sending them to the capacitor bank, SVG and PV inverter for real-time regulation; S9: Monitoring and adjustment: Continuously monitor the substation operation status and equipment status, and adjust the optimization model parameters and equipment control strategies according to actual conditions to ensure stable operation of the system, after which the entire substation voltage coordination optimization process considering carbon saving can be completed.

2. A method for coordinated optimization of voltage in a substation area considering carbon saving as claimed in claim 1, characterized in that: In step S1: Real-time data collection methods include: Voltage data collection: Use high-precision voltage sensors to monitor the voltage of each node in the substation in real time to ensure the accuracy and stability of the data; the sensor should have network communication function to transmit the data to the energy management system in real time; Load power monitoring: By installing smart meters, real-time power consumption data of each node load is collected to achieve dynamic monitoring of the load status of the substation; Photovoltaic power generation monitoring: using the data interface of the photovoltaic power generation system to obtain the power generation of photovoltaic panels in real time in order to optimize energy distribution and use; Parameter initialization methods include: Transformer capacity setting: Determine the rated capacity of the transformer based on the maximum load demand and future development plan of the substation, and initialize the parameters through the system; Target voltage setting: According to the grid standards and equipment operation requirements, the target voltage value of each node in the substation is set and initialized in the system; Weight coefficient configuration: according to the operating characteristics and importance of different equipment, weight coefficients are assigned to parameters such as voltage, load power, photovoltaic power generation, etc., so as to perform optimization calculations in the energy management system; Transformer life cycle carbon emissions calculation: Combine the rated capacity, operating efficiency and service life of the transformer to estimate its carbon emissions throughout its life cycle and initialize it in the system; Equipment power-voltage coefficient determination: By testing the power consumption characteristics of each device at different voltages, the power-voltage coefficient is determined and initialized in the system for energy efficiency analysis and optimized control.

3. The method for optimizing the coordinated voltage of a substation considering carbon saving according to claim 1, characterized in that: In step S2: The control strategy of the capacitor bank is: switching control to compensate reactive power and maintain voltage stability; The SVG control strategy is: dynamically adjust reactive power output to achieve rapid response to load fluctuations; The control strategy of the photovoltaic inverter is to adjust the active and reactive power according to the power-voltage coefficient relationship table to achieve photovoltaic priority consumption.

4. The method for optimizing the coordinated voltage of a power station considering carbon saving according to claim 1, characterized in that: In step S3, the transformer load rate and the life loss coefficient are used to establish a life loss model: Where P load is the load power, S rated is the rated capacity of the transformer, α and n are empirical coefficients; Considering the carbon emissions of the transformer throughout its life cycle, the formula for calculating the additional carbon emissions during operation is: Of which CO2 lifecycle Set a value for the carbon footprint of the transformer's life cycle.

5. The method for optimizing the coordinated voltage of a substation considering carbon saving according to claim 1, characterized in that: The step S4 specifically includes: A multi-objective optimization model is constructed with the goals of node voltage stability, transformer carbon emission minimization and photovoltaic consumption maximization: Where V i represents the voltage of the ith node; V ref Indicates the target voltage; Indicates additional carbon emissions from transformers; Indicates the power consumed by photovoltaic power on site; ω1, ω2, ω3 represent weight coefficients.

6. The method for optimizing the coordinated voltage of a substation considering carbon saving according to claim 1, characterized in that: In step S5, the node voltage constraint condition is: The transformer load factor constraint is: The reactive power constraints of SVG and capacitor are: The power constraints of the photovoltaic inverter are:

7. The method for optimizing the coordinated voltage of a substation considering carbon saving according to claim 1, characterized in that: In step S5, the relationship between the reactive power regulated by SVG and the voltage coefficient of each node is calculated as follows: SVG,i =k SVG,i ·(V i -V ref ); Where Q SVG,i represents the reactive power of SVG at node i; k SVG,i It represents the voltage-reactive coefficient of SVG at node i.

8. The method for optimizing the coordinated voltage of a substation considering carbon saving according to claim 1, characterized in that: In step S5, the relationship between the reactive power controlled by the capacitor and the voltage coefficient of each node is calculated as follows: cap,i =k cap,i ·(V i -V ref ); Where Q cap,i represents the reactive power of the capacitor at node i; k cap,i Represents the voltage-reactive coefficient of the capacitor at node i.

9. The method for optimizing coordinated voltage in a substation area considering carbon saving according to claim 1, characterized in that: In step S5, the calculation formula for the relationship between the active power and reactive power regulated by the photovoltaic inverter and the voltage coefficient of each node is: Where: P PV,i represents the active power of the PV inverter at node i; Q PV,i represents the reactive power of the PV inverter at node i; represents the voltage-active power coefficient of the PV inverter at node i; Represents the voltage-reactive power factor of the volt inverter at node i.

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