Transformer substation low-carbon operation method and system based on energy efficiency improvement

Abstract

The invention belongs to the technical field of transformer substation low-carbon operation, and discloses a transformer substation low-carbon operation method based on energy efficiency improvement, and the method comprises the following specific steps: 1, carrying out the multi-source data collection and energy consumption reference establishment, and collecting the operation data of a transformer substation; comprising transformer load rate, cooling system power consumption, station power consumption, environment temperature and humidity and distributed energy output data. By systematically integrating multi-source data acquisition, accurate energy efficiency evaluation, multi-target collaborative optimization and closed-loop dynamic regulation and control, a low-carbon energy efficiency improvement system covering the whole operation link of the transformer substation is constructed, so that the energy utilization efficiency is remarkably improved, and the carbon emission intensity is effectively reduced; and the safety and engineering applicability of an optimization strategy are ensured through digital twin verification and adaptive control, an intelligent operation mode with continuous self-optimization capability is finally formed, and a reliable technical support and a practical path are provided for a power grid enterprise to achieve a dual-carbon target.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of substation low-carbon operation, and particularly relates to a substation low-carbon operation method and system based on energy efficiency improvement. BACKGROUND

[0002] Energy efficiency improvement refers to significantly reducing the energy consumption and operation loss of equipment in a substation in the process of substation operation under the premise of ensuring power supply reliability and power quality through systematic optimization strategies and technical means. The core connotation thereof covers intelligent regulation and control of transformer load rate and cooling system, reactive power compensation and harmonic control of station power system, collaborative management of distributed energy such as light storage micro-grid, and operation mode optimization based on big data analysis, so as to finally realize the transformation and upgrading of the substation from an energy consumer to an efficient and low-carbon hub, and achieve the dual goals of the lowest carbon emission intensity and the highest energy comprehensive utilization efficiency per unit of power supply.

[0003] The prior art mainly relies on a discrete control strategy based on a fixed threshold and a combination of periodic artificial inspection, that is, starting and stopping or gear adjustment of main energy-consuming equipment is performed through a preset single parameter threshold (such as starting all fans at the upper limit of the transformer temperature or switching fixed capacity capacitors at the lower limit of the power factor), and energy efficiency improvement measures are formulated by relying on periodic on-site data copying and experience judgment of operation and maintenance personnel. This static and localized control mode cannot perceive the real-time operation state and dynamic coupling relationship of the system, so that the substation is in a long-term "overly conservative operation" or "partial equipment high energy consumption" working condition, which not only causes a large amount of invalid energy consumption and redundant carbon emission, but also makes it difficult to balance operation economy and environmental friendliness due to the lack of overall optimization of carbon efficiency and collaborative targets, and finally forms a high carbon lock effect, so it needs to be improved. SUMMARY

[0004] The purpose of the present application is to provide a substation low-carbon operation method and system based on energy efficiency improvement to solve the problems raised in the background art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a substation low-carbon operation method based on energy efficiency improvement, the specific steps are as follows:

[0006] Step 1: Multi-source data acquisition and energy consumption benchmark establishment

[0007] Substation operation data are collected, including transformer load rate, cooling system power consumption, station power consumption, environmental temperature and humidity, and distributed energy output data. Equipment energy efficiency benchmark curves are established through historical data analysis to identify high energy consumption operation intervals;

[0008] Step 2: Energy efficiency state evaluation and loss tracing analysis

[0009] Based on real-time operation data, the transformer load loss and no-load loss, the comprehensive loss of station transformer, and the equivalent energy consumption of cooling system are calculated, the comprehensive energy efficiency level of the whole station is evaluated by using entropy weight-TOPSIS method, and the key factors affecting energy efficiency are identified through Pearson correlation analysis.

[0010] Step three: multi-objective collaborative optimization model construction

[0011] A multi-objective function is established with the lowest carbon emission intensity and the highest comprehensive energy efficiency as the target, and the voltage deviation, equipment load rate, and power factor as the constraint conditions, and an optimization model is constructed including transformer tap adjustment, reactive power compensation device switching, and energy storage system charging and discharging strategy.

[0012] Step four: dynamic optimization strategy generation and verification

[0013] An improved multi-objective particle swarm optimization algorithm is used to solve the optimization model, and the transformer economic operation interval, the optimal capacity of reactive power compensation, and the light-storage collaborative scheduling strategy are generated, and the effectiveness and safety of the strategy are verified through the digital twin platform.

[0014] Step five: adaptive control execution and real-time adjustment

[0015] The optimization strategy is issued to the transformer on-load voltage regulating switch, reactive power compensation device, and energy storage converter execution unit, and the rolling optimization and dynamic adjustment are carried out according to the real-time load change and distributed energy output.

[0016] Step six: energy efficiency closed-loop evaluation and continuous optimization

[0017] Based on the energy efficiency data after the strategy execution, the carbon emission reduction and energy saving amount are calculated, the energy efficiency benchmark curve and optimization model parameters are updated, and a closed-loop management system of monitoring-evaluation-optimization-execution is formed.

[0018] Preferably, the energy efficiency state evaluation in step two adopts an energy efficiency grade division method based on fuzzy comprehensive evaluation, divides the energy efficiency state of the substation into four grades of excellent, good, qualified, and unqualified, and gives specific energy efficiency improvement suggestions.

[0019] Preferably, the multi-objective function in step three is specifically represented as the lowest carbon emission intensity and the highest comprehensive energy efficiency of the substation as the optimization target, and its mathematical expression is:

[0020]

[0021] Wherein is the carbon emission intensity of the unit power supply of the substation, is the comprehensive energy efficiency index of the substation; the constraint conditions of the optimization model include:

[0022] 1) Node voltage constraint: where U is the node voltage, and are its allowed lower and upper limits, respectively; 2) equipment capacity constraints: where is the apparent power of the i-th transformer or line, is its rated capacity; 3) power factor constraints: where is the power factor of the substation point of common coupling.

[0023] Preferably, the improved multi-objective particle swarm algorithm in step four introduces a simulated annealing mechanism and a crowding degree sorting strategy, which maintains population diversity while improving convergence speed and ensures the Pareto optimality of the optimization results.

[0024] Preferably, the photovoltaic storage cooperative scheduling strategy in step five includes three operating modes: photovoltaic output smoothing, peak-valley price arbitrage, and voltage support, which can be adaptively switched according to grid dispatching instructions and on-site demand.

[0025] Preferably, the carbon emission reduction calculation in step six takes into account the grid emission factor, the contribution rate of on-site distributed energy, and the energy efficiency improvement effect, and uses the life cycle assessment method for quantitative evaluation.

[0026] Preferably, it also includes accessing the grid demand side response signal, dynamically adjusting the station's energy storage charging and discharging strategy and interruptible load switching scheme according to the price signal and load control requirements.

[0027] The substation low-carbon operation system based on energy efficiency improvement includes the following modules:

[0028] Data acquisition and processing module: used for real-time acquisition of substation electrical quantities, equipment status and environmental parameters, data cleaning and normalization processing;

[0029] Energy efficiency evaluation and analysis module: used for calculating the energy consumption indicators of each device in the station, identifying energy efficiency bottlenecks and optimization potential;

[0030] Optimization decision generation module: used for building a multi-objective optimization model and solving it to generate device control strategies;

[0031] Strategy execution and control module: used for converting the optimization strategy into control instructions to drive the actuator to act;

[0032] Effect evaluation and feedback module: used for evaluating the execution effect of the control strategy and updating the optimization model parameters;

[0033] Visual display module: used for real-time display of energy efficiency data, carbon emission online monitoring results and optimization suggestions.

[0034] Preferably, the optimization decision generation module integrates the core program of the improved multi-objective particle swarm algorithm and is equipped with a strategy simulation verification unit and a real-time data interaction interface to ensure that the optimization strategy completes security verification and effect pre-evaluation in the digital twin environment before being issued and executed.

[0035] The beneficial effects of this invention are as follows:

[0036] This application constructs a low-carbon energy efficiency improvement system covering all aspects of substation operation by systematically integrating multi-source data acquisition, precise energy efficiency assessment, multi-objective collaborative optimization, and closed-loop dynamic control. It not only significantly improves energy utilization efficiency and effectively reduces carbon emission intensity, but also ensures the safety and engineering applicability of the optimization strategy through digital twin verification and adaptive control. Ultimately, it forms an intelligent operation mode with continuous self-optimization capabilities, providing reliable technical support and practical path for power grid companies to achieve dual carbon targets. Attached Figure Description

[0037] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for low-carbon operation of substations based on energy efficiency improvement, and the specific steps are as follows:

[0040] Step 1: Multi-source data acquisition and energy consumption benchmark establishment

[0041] Collect substation operation data, including transformer load rate, cooling system power consumption, station power consumption, ambient temperature and humidity, and distributed energy output data. Analyze historical data to establish equipment energy efficiency benchmark curves and identify high-energy-consumption operating ranges.

[0042] Step Two: Energy Efficiency Status Assessment and Loss Source Analysis

[0043] Based on real-time operating data, transformer load loss and no-load loss, station service transformer comprehensive loss, and cooling system equivalent energy consumption are calculated. The entropy weight-TOPSIS method is used to evaluate the overall energy efficiency level of the station. Pearson correlation analysis is used to identify key factors affecting energy efficiency.

[0044] Step 3: Construction of a multi-objective collaborative optimization model

[0045] A multi-objective function is established with the goal of minimizing carbon emission intensity and maximizing overall energy efficiency. With voltage deviation, equipment load rate, and power factor as constraints, an optimization model is constructed that includes transformer tap changer adjustment, reactive power compensation device switching, and energy storage system charging and discharging strategies.

[0046] Step 4: Dynamic Optimization Strategy Generation and Verification

[0047] An improved multi-objective particle swarm optimization algorithm is used to solve the optimization model, generating the economic operating range of the transformer, the optimal capacity for reactive power compensation, and the photovoltaic-storage coordinated scheduling strategy. The effectiveness and security of the strategy are verified through a digital twin platform.

[0048] Step 5: Adaptive Control Execution and Real-Time Adjustment

[0049] The optimization strategy is distributed to the execution units such as the on-load tap changer of the transformer, the reactive power compensation device, and the energy storage converter, and is continuously optimized and dynamically adjusted according to the real-time load changes and the output of distributed energy.

[0050] Step Six: Energy Efficiency Closed-Loop Assessment and Continuous Optimization

[0051] Based on the energy efficiency data after the strategy is implemented, carbon emission reduction and energy saving are calculated, the energy efficiency benchmark curve and optimization model parameters are updated, and a closed-loop management system of monitoring-evaluation-optimization-implementation is formed.

[0052] By constructing a complete technical process covering data acquisition, status assessment, optimization decision-making, strategy verification, and closed-loop control, an energy efficiency benchmark curve is established to identify key energy consumption points. A multi-objective optimization model is adopted to coordinate the dual objectives of carbon emission reduction and energy efficiency improvement. Furthermore, a digital twin platform is used to realize the virtual verification and rolling optimization of strategies. Ultimately, a closed-loop management system with self-learning and continuous improvement capabilities is formed, thereby systematically solving the technical problems of the disconnect between energy efficiency management and carbon emission control and the lack of foresight and adaptability in optimization strategies in traditional substation operation.

[0053] In step two, the energy efficiency status assessment adopts an energy efficiency level classification method based on fuzzy comprehensive evaluation, which classifies the energy efficiency status of substations into four levels: excellent, good, qualified, and unqualified, and provides specific suggestions for energy efficiency improvement.

[0054] Based on the quantitative assessment of energy efficiency, a fuzzy comprehensive evaluation system is introduced. By establishing a multi-dimensional evaluation index system that includes equipment operating efficiency, energy utilization efficiency, and environmental adaptability, and using membership functions to handle the fuzziness and uncertainty of each index, the complex energy efficiency data is finally transformed into intuitive grade evaluations and specific improvement suggestions, providing clear operational guidance for operators.

[0055] In step three, the multi-objective function is specifically expressed as having the optimization objectives of minimizing the carbon emission intensity of the substation and maximizing its overall energy efficiency. Its mathematical expression is as follows:

[0056]

[0057] in Carbon emission intensity of electricity supplied by a substation operating unit The comprehensive energy efficiency index of the substation; the constraints of the optimization model include:

[0058] 1) Node voltage constraints: Where U is the node voltage. and These are the lower and upper limits allowed, respectively; 2) Equipment capacity constraints: ,in Let be the apparent power of the i-th transformer or line. 3) Power factor constraint: (This is related to the rated capacity of the power factor.) ,in This refers to the power factor at the substation's grid connection point.

[0059] The goal of low-carbon and high-efficiency operation is quantified into two key indicators: calculable carbon emission intensity and comprehensive energy efficiency index. The optimal solution is sought under the three basic constraints of voltage deviation, equipment capacity and power factor for safe operation of the power grid. Thus, at the mathematical model level, a unified expression and collaborative solution of the substation safe operation boundary and energy efficiency and carbon emission optimization goals are realized.

[0060] In step four, the improved multi-objective particle swarm optimization algorithm introduces a simulated annealing mechanism and a crowding ranking strategy to improve convergence speed while maintaining population diversity and ensuring Pareto optimality of the optimization results.

[0061] By incorporating the probabilistic jump characteristics of the simulated annealing algorithm to avoid premature convergence, and combining the crowding ranking strategy to maintain the uniformity of the solution set distribution on the Pareto front, its core advantage lies in its ability to quickly obtain a batch of non-dominated solutions with a wide coverage and uniform distribution under complex constraints, providing operators with a variety of optional operating schemes that combine low carbon emissions and high efficiency.

[0062] In step five, the photovoltaic-storage coordinated dispatch strategy includes three operating modes: photovoltaic output smoothing, peak-valley electricity price arbitrage, and voltage support, which are adaptively switched according to grid dispatch instructions and station demand.

[0063] By using a photovoltaic output smoothing mode to suppress power fluctuations and ensure grid stability, a peak-valley electricity price arbitrage mode to reduce the power purchase cost of substations, and a voltage support mode to participate in grid reactive power regulation, the three modes can be adaptively switched according to changes in external grid demand and internal operating status, fully exploring the multiple values ​​and synergistic benefits of distributed energy in the low-carbon operation of substations.

[0064] In step six, the calculation of carbon emission reduction comprehensively considers the grid emission factor, the contribution rate of distributed energy within the station, and the energy efficiency improvement effect, and adopts the life cycle assessment method for quantitative evaluation.

[0065] The life cycle assessment method not only takes into account the carbon emissions corresponding to the direct consumption of grid electricity by substation operation, but also quantitatively assesses the clean power substitution benefits of distributed energy sources such as photovoltaics within the substation and the indirect emission reduction effects brought about by energy efficiency improvement. This ensures the completeness and accuracy of the carbon emission reduction calculation results and provides a reliable basis for the objective evaluation of the low-carbon operation performance of substations.

[0066] This also includes accessing the grid demand-side response signal, and dynamically adjusting the station's energy storage charging and discharging strategy and interruptible load switching scheme according to electricity price signals and load control requirements.

[0067] By placing substations within a broader grid interaction environment, and flexibly adjusting energy storage operation modes and controllable load states in response to market signals such as time-of-use pricing, substations can provide auxiliary services such as peak shaving and backup to the grid while achieving their own economic operation. This enhances the comprehensive value and resource optimization capabilities of substations as important nodes in the grid.

[0068] The energy efficiency-based low-carbon operation system for substations includes the following modules:

[0069] Data acquisition and processing module: used to collect substation electrical quantities, equipment status and environmental parameters in real time, and perform data cleaning and normalization processing;

[0070] Energy efficiency assessment and analysis module: used to calculate the energy consumption indicators of various equipment in the station, identify energy efficiency bottlenecks and optimization potential;

[0071] Optimization Decision Generation Module: Used to construct and solve multi-objective optimization models to generate equipment control strategies;

[0072] Strategy execution and control module: used to convert optimization strategies into control commands to drive the actions of the actuators;

[0073] The effect evaluation and feedback module is used to evaluate the effectiveness of the control strategy and update and optimize the model parameters.

[0074] Visualization module: Used to display energy efficiency data, online carbon emission monitoring results, and optimization suggestions in real time.

[0075] To implement the aforementioned method, the hardware system and functional modules are organized according to a closed-loop control logic of perception-decision-execution-evaluation. The data acquisition module obtains the on-site operating status, the energy efficiency evaluation and optimization decision module completes data analysis and strategy formulation, the strategy execution module realizes closed-loop control of optimization commands, and the effect evaluation and visualization module realizes quantitative evaluation and intuitive display of operating effects, ultimately forming a complete technical carrier integrating monitoring, analysis, optimization, control and evaluation.

[0076] The optimization decision generation module integrates the core program of the improved multi-objective particle swarm algorithm and is equipped with a strategy simulation verification unit and a real-time data interaction interface to ensure that the optimization strategy completes security verification and effect pre-evaluation in the digital twin environment before being issued and executed.

[0077] This module achieves rapid solution of the optimization model by integrating an improved multi-objective particle swarm optimization algorithm compiler and executor. It uses a strategy simulation verification unit to simulate the system state changes after strategy execution in a virtual environment to screen for potential risks, and obtains the latest operating data through a real-time data interaction interface to ensure the timeliness of the optimization strategy, thereby improving the reliability and safety of substation low-carbon operation decisions.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for low-carbon operation of substations based on energy efficiency improvement, characterized in that, The specific steps are as follows: Step 1: Multi-source data acquisition and energy consumption benchmark establishment Collect substation operation data, including transformer load rate, cooling system power consumption, station power consumption, ambient temperature and humidity, and distributed energy output data. Analyze historical data to establish equipment energy efficiency benchmark curves and identify high-energy-consumption operating ranges. Step Two: Energy Efficiency Status Assessment and Loss Source Analysis Based on real-time operating data, transformer load loss and no-load loss, station service transformer comprehensive loss, and cooling system equivalent energy consumption are calculated. The entropy weight-TOPSIS method is used to evaluate the overall energy efficiency level of the station. Pearson correlation analysis is used to identify key factors affecting energy efficiency. Step 3: Construction of a multi-objective collaborative optimization model A multi-objective function is established with the goal of minimizing carbon emission intensity and maximizing overall energy efficiency. With voltage deviation, equipment load rate, and power factor as constraints, an optimization model is constructed that includes transformer tap changer adjustment, reactive power compensation device switching, and energy storage system charging and discharging strategies. Step 4: Dynamic Optimization Strategy Generation and Verification An improved multi-objective particle swarm optimization algorithm is used to solve the optimization model, generating the economic operating range of the transformer, the optimal capacity for reactive power compensation, and the photovoltaic-storage coordinated scheduling strategy. The effectiveness and security of the strategy are verified through a digital twin platform. Step 5: Adaptive Control Execution and Real-Time Adjustment The optimization strategy is distributed to the execution units such as the on-load tap changer of the transformer, the reactive power compensation device, and the energy storage converter, and is continuously optimized and dynamically adjusted according to the real-time load changes and the output of distributed energy. Step Six: Energy Efficiency Closed-Loop Assessment and Continuous Optimization Based on the energy efficiency data after the strategy is implemented, carbon emission reduction and energy saving are calculated, the energy efficiency benchmark curve and optimization model parameters are updated, and a closed-loop management system of monitoring-evaluation-optimization-implementation is formed.

2. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: The energy efficiency status assessment in step two adopts an energy efficiency level classification method based on fuzzy comprehensive evaluation, which classifies the energy efficiency status of substations into four levels: excellent, good, qualified, and unqualified, and provides specific energy efficiency improvement suggestions.

3. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: The multi-objective function mentioned in step three is specifically expressed as having the optimization objectives of minimizing the carbon emission intensity of the substation and maximizing its overall energy efficiency. Its mathematical expression is: ; in Carbon emission intensity of electricity supplied by a substation operating unit The comprehensive energy efficiency index of the substation; the constraints of the optimization model include: 1) Node voltage constraints: Where U is the node voltage. and These are the lower and upper limits allowed, respectively; 2) Equipment capacity constraints: ,in Let be the apparent power of the i-th transformer or line. 3) Power factor constraint: (This is related to the rated capacity of the power factor.) ,in This refers to the power factor at the substation's grid connection point.

4. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: The improved multi-objective particle swarm optimization algorithm described in step four introduces a simulated annealing mechanism and a crowding ranking strategy to improve convergence speed while maintaining population diversity and ensuring Pareto optimality of the optimization results.

5. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: The photovoltaic-storage coordinated dispatch strategy described in step five includes three operating modes: photovoltaic output smoothing, peak-valley electricity price arbitrage, and voltage support, which are adaptively switched according to grid dispatch instructions and station demand.

6. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: The carbon emission reduction calculation in step six comprehensively considers the grid emission factor, the contribution rate of distributed energy within the station, and the energy efficiency improvement effect, and adopts the life cycle assessment method for quantitative evaluation.

7. The method for low-carbon operation of substations based on energy efficiency improvement according to claim 1, characterized in that: It also includes access to the grid demand-side response signal, and dynamically adjusting the station's energy storage charging and discharging strategy and interruptible load switching scheme according to the electricity price signal and load control requirements.

8. A low-carbon operation system for substations based on energy efficiency improvement, characterized in that: Includes the following modules: Data acquisition and processing module: used to collect substation electrical quantities, equipment status and environmental parameters in real time, and perform data cleaning and normalization processing; Energy efficiency assessment and analysis module: used to calculate the energy consumption indicators of various equipment in the station, identify energy efficiency bottlenecks and optimization potential; Optimization Decision Generation Module: Used to construct and solve multi-objective optimization models to generate equipment control strategies; Strategy execution and control module: used to convert optimization strategies into control commands to drive the actions of the actuators; The effect evaluation and feedback module is used to evaluate the effectiveness of the control strategy and update and optimize the model parameters. Visualization module: Used to display energy efficiency data, online carbon emission monitoring results, and optimization suggestions in real time.

9. The substation low-carbon operation system based on energy efficiency improvement according to claim 8, characterized in that: The optimization decision generation module integrates the core program of the improved multi-objective particle swarm algorithm and is equipped with a strategy simulation verification unit and a real-time data interaction interface to ensure that the optimization strategy completes security verification and effect pre-evaluation in the digital twin environment before being issued and executed.

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