A method and system for optimizing voltage coordination in substations considering carbon saving

Abstract

The present invention discloses a method and system for coordinated optimization of substation voltages taking carbon conservation into consideration. In the present invention, by dynamically adjusting the outputs of capacitor banks, SVGs, and photovoltaic inverters according to a power-voltage coefficient relationship table, the present invention effectively solves the problem of voltage overshoot and undershoot caused by fluctuations in photovoltaic power generation. The equipment responds quickly, ensuring that the voltages of each node in the substation are 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 scheduling in real time according to the load status of the transformer, avoiding premature aging of the transformer caused by long-term high-load operation. By reasonably distributing the load, the additional impact of carbon emissions on the life of the transformer is reduced, and the long life and high efficiency of the transformer operation are achieved. 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.

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 substation taking carbon saving into consideration. Background Art

[0002] Distribution systems must not only ensure grid stability but also minimize carbon emissions. In areas where a large number of distributed photovoltaic systems are connected, coordinating the control of capacitor banks, static VAR generators (SVGs), and photovoltaic inverters, while factoring in transformer load conditions and carbon emissions, is a critical issue that needs to be addressed. Traditional voltage optimization methods primarily focus on using capacitors and on-load tap-changer transformers (OLTCs) for reactive power compensation and voltage regulation.

[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 existing methods do not incorporate transformer life loss and its carbon emission costs throughout its life cycle into 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 exacerbating the voltage fluctuation problem.

[0007] Therefore, a new substation optimization method is needed to coordinately control the status and carbon emission impact of capacitors, SVGs, photovoltaic inverters and transformers, so as to minimize carbon emissions and improve the level of photovoltaic absorption 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, load power, photovoltaic power generation power, etc. at each node in the substation.

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

[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 absorption.

[0019] The objective function includes indicators such as node voltage deviation, additional carbon emissions of transformers and on-site photovoltaic 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 operation.

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

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

[0025] Set the power-voltage coefficient relationship between SVG, capacitors, and PV inverters to calculate the device control power.

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

[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 photovoltaic 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 strategy according to actual conditions to ensure stable system operation. After that, 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 at each node in the substation in real time to ensure data accuracy and stability. The sensors should have network communication capabilities 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, set the target voltage value of each node in the substation and initialize the configuration 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, and photovoltaic power generation power to facilitate optimization calculations in the energy management system.

[0039] Transformer lifecycle carbon emissions calculation: Combined with the transformer's rated capacity, operating efficiency, and service life, the carbon emissions over its entire lifecycle are estimated and initialized in the system.

[0040] Determine the power-voltage coefficient of the device: By testing the power consumption characteristics of each device at different voltages, the power-voltage coefficient is determined and initialized in the system to facilitate 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 for 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, the step S3 uses the transformer load rate and the life loss coefficient to establish a life loss model:

[0046]

[0047] Among them, 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 emissions 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, minimizing transformer carbon emissions, and maximizing photovoltaic absorption:

[0053]

[0054] Where V i Represents the voltage of the i-th 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 embodiment, 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 controlled by SVG and the voltage coefficient of each node is calculated as follows: 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 It represents the voltage-reactive coefficient of the capacitor at node i.

[0069] In a preferred embodiment, in step S5, the relationship between the active power and reactive power controlled by the photovoltaic inverter and the voltage coefficient of each node is calculated as follows:

[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 fluctuations in photovoltaic power generation. 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, this system can optimize scheduling in real time according to the load status of the transformer, avoiding premature aging of the transformer caused by long-term high-load operation. Taking into account the carbon emissions of the transformer throughout its life cycle, by reasonably distributing the load, the additional impact of carbon emissions on the life of the transformer is reduced, and the long life and high efficiency of the transformer operation are achieved.

[0078] 2. In the present invention, the active and reactive power of the photovoltaic inverter are dynamically adjusted to achieve on-site photovoltaic consumption, reduce the loss of photovoltaic electricity 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 reducing the consumption of fossil energy and achieving efficient utilization of clean energy. Incorporating the carbon emissions of the transformer life cycle into the optimization model, the system not only focuses on voltage stability and power loss, but also considers the economic efficiency of carbon emissions, promoting 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. The system, through integrated terminal control, ensures the coordinated operation of capacitors, SVGs, and photovoltaic inverters. It dynamically adjusts device output based on the real-time operating status of the substation, achieving globally optimal intelligent control. Through adaptive optimization algorithms, the system can cope with varying loads and photovoltaic power generation fluctuations, enhancing its flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The fusion terminal of the present invention reads the status of each device and issues 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 between the power control device and node i. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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 intended to limit the present invention.

[0083] Reference Figure 1-2 ,

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

[0085] S1: Data collection and initialization:

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

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

[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 absorption.

[0094] The objective function includes indicators such as node voltage deviation, additional carbon emissions of transformers and on-site photovoltaic 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 operation.

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

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

[0100] Set the power-voltage coefficient relationship between SVG, capacitors, and PV inverters to calculate the device control power.

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

[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 photovoltaic 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 strategy according to actual conditions to ensure stable system operation. After that, 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 at each node in the substation in real time to ensure data accuracy and stability. The sensors should have network communication capabilities 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, set the target voltage value of each node in the substation and initialize the configuration 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, and photovoltaic power generation power to facilitate optimization calculations in the energy management system.

[0114] Transformer lifecycle carbon emissions calculation: Combined with the transformer's rated capacity, operating efficiency, and service life, the carbon emissions over its entire lifecycle are estimated and initialized in the system.

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

[0116] In step S2:

[0117] The control strategy of the capacitor bank is: switching control to compensate for 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] Among them, 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 emissions 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, minimizing transformer carbon emissions, and maximizing photovoltaic absorption:

[0128]

[0129] Where V i Represents the voltage of the i-th 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 controlled 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 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 );

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

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

[0144] In step S5, the relationship between the active and reactive power controlled by the photovoltaic inverter and the voltage coefficient of each node is calculated as follows:

[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 photovoltaic inverter at node i

[0149] Represents the voltage-power coefficient 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 fluctuations in photovoltaic power generation. 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, this system can optimize scheduling in real time according to the load status of the transformer, avoiding premature aging of the transformer caused by long-term high-load operation. Taking into account the carbon emissions of the transformer throughout its life cycle, by reasonably distributing the load, the additional impact of carbon emissions on the life of the transformer is reduced, and the long life and high efficiency of the transformer operation are achieved.

[0152] In the present invention, by dynamically adjusting the active and reactive power of the photovoltaic inverter, photovoltaic power is consumed on site, the loss of photovoltaic power sent back to the upper power grid is reduced, and the utilization rate of photovoltaic power generation is improved. The system can preferentially consume renewable energy during the peak period of photovoltaic power generation, effectively reducing the consumption of fossil energy and achieving efficient utilization of clean energy. Incorporating the carbon emissions of the transformer life cycle into the optimization model, the system not only focuses on voltage stability and power loss, but also considers the economic efficiency of carbon emissions, promoting 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 this invention, the system ensures coordinated operation of capacitors, SVGs, and photovoltaic inverters through integrated terminal control. It also dynamically adjusts device output based on the real-time operating status of the substation, achieving globally optimal intelligent control. Through adaptive optimization algorithms, the system can adapt to varying loads and photovoltaic power generation fluctuations, enhancing its flexibility and adaptability.

[0154] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any 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 that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[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 they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 various embodiments of the present invention.

Claims

1. A method for optimizing coordinated voltage in a substation area considering carbon conservation, characterized by: The method comprises the following steps: S1: Data collection and initialization: Collect real-time data on voltage, load power, and photovoltaic power generation at each node in the substation area; Initialization parameters, including transformer capacity, target voltage, weight coefficient, transformer lifecycle carbon emissions, and power-voltage coefficient of each device; 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 was constructed with the goals of node voltage stability, minimizing transformer carbon emissions, and maximizing photovoltaic absorption. The objective function includes node voltage deviation, transformer additional carbon emissions, and photovoltaic local power consumption indicators, 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 SVG and capacitor reactive power constraints to ensure safe equipment operation; Set PV inverter power constraints to ensure efficient use of PV power; Set the power-voltage coefficient relationship between SVG, capacitors, and photovoltaic inverters to calculate the device control power; S6: Select optimization algorithm: Select the optimization algorithm based on the scale of the area and computing requirements; 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; S8: Sending 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; S9: Monitoring and adjustment: Continuously monitor the substation operation status and equipment status, and adjust the optimization model parameters and equipment control strategy according to actual conditions to ensure stable system operation. After that, the entire substation voltage coordination optimization process considering carbon saving can be completed.

2. The method for coordinated optimization of transformer substation voltage considering carbon saving according to 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 sensors should have network communication capabilities to transmit 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 area; 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; 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: Based on the operating characteristics and importance of different devices, weight coefficients are assigned to node voltage deviation, load active power, load reactive power, PV active power, and PV reactive power to facilitate optimization calculations in the energy management system. Transformer lifecycle carbon emissions calculation: Combined with the transformer's rated capacity, operating efficiency, and service life, the carbon emissions over its entire lifecycle are estimated and initialized in the system; Determine the power-voltage coefficient of the device: By testing the power consumption characteristics of each device at different voltages, the power-voltage coefficient is determined and initialized in the system to facilitate energy efficiency analysis and optimized control.

3. The method for optimizing voltage coordination in 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 for reactive power and maintain voltage stability; The SVG control strategy is to 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. A method for coordinated optimization of voltage in a substation considering carbon saving as claimed in claim 1, Its characteristics are: In step S3, the transformer load rate and the life loss coefficient are used to establish a life loss model: ； in is the load power, 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: ; in Set a value for the carbon emissions of the transformer's life cycle.

5. The method for coordinated optimization of transformer substation voltage 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, minimizing transformer carbon emissions, and maximizing photovoltaic absorption: ； in represents the voltage of the i-th node; Indicates the target voltage; Indicates additional carbon emissions from transformers; Indicates the power consumed by photovoltaic power on site; Represents the weight coefficient.

6. The method for coordinated optimization of transformer substation voltage 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: ; In step S5, the relationship between the reactive power controlled by SVG and the voltage coefficient of each node is calculated as follows: ； in represents the reactive power of SVG at node i; represents the voltage-reactive coefficient of SVG at node i.

7. A method for coordinated optimization of voltage in a substation considering carbon saving as claimed in claim 1, Its characteristics are: In step S5, SVG regulates the reactive power and the voltage coefficient of each node The relationship calculation formula is: ; in represents the reactive power of SVG at node i; represents the voltage-reactive coefficient of SVG at node i.

8. A method for coordinated optimization of voltage in a substation considering carbon saving as claimed in claim 1, Its characteristics are: In step S5, the relationship between the capacitor-controlled reactive power and the voltage coefficient of each node is calculated as follows: ; in represents the reactive power of the capacitor at node i; represents the voltage-reactive coefficient of the capacitor at node i.

9. A method for coordinated optimization of voltage in a substation considering carbon saving as claimed in claim 1, Its characteristics are: In step S5, the relationship between the active power and reactive power controlled by the photovoltaic inverter and the voltage coefficient of each node is calculated as follows: ； ； in: represents the active power of the PV inverter at node 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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