Energy storage access power distribution network scheme technical economic evaluation method

Abstract

The invention discloses an energy storage access power distribution network scheme technical economic evaluation method, which comprehensively considers multiple dimensions such as performance, economy, environment and future potential, and realizes comprehensive and dynamic evaluation of an energy storage access scheme through a dynamic prediction model and a real-time data analysis technology. The invention further provides a dynamic prediction model based on big data and a machine learning technology, accuracy and reliability of a prediction result are improved, and timeliness and accuracy of an evaluation result are ensured through a real-time data analysis and feedback mechanism. Through comprehensive evaluation and optimization suggestions, a scientific and reasonable decision basis is provided for power distribution network planning and energy storage system deployment, the risk and cost of energy storage system access are reduced, the reliability of the energy storage system and the stability and economical efficiency of power distribution network access to the energy storage system are ensured, and economic benefits and social benefits are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a technical and economic evaluation method for a distribution network scheme with energy storage access. Background Art

[0002] Energy storage access mainly refers to the process of connecting the energy storage system to the power grid or other energy systems to achieve the storage, release and regulation of electric energy. This technology plays an important role and significance in improving energy utilization efficiency, enhancing power grid stability, and promoting the development of renewable energy.

[0003] With the large-scale access of renewable energy and the development of smart grids, energy storage systems play an increasingly important role in balancing grid supply and demand and improving system stability and economy. However, the access of energy storage systems requires comprehensive consideration of technical feasibility and economic benefits. Existing evaluation methods often focus on a single dimension and are difficult to fully reflect the comprehensive performance of energy storage access solutions, which can easily cause harm to the safe and stable operation of the grid. Based on the above reasons, the present invention proposes a technical and economic evaluation method for energy storage access distribution network solutions. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the evaluation method proposed in the present invention comprehensively considers multiple dimensions such as performance, economy, environment and future potential, and realizes a comprehensive and dynamic evaluation of energy storage access solutions through dynamic prediction models and real-time data analysis technology, thereby avoiding the harm that large-scale energy storage access to the power grid may cause to the safe and stable operation of the power grid. In addition, economic benefits and environmental benefits are taken as optimization targets in the optimization process, thereby achieving the goal of coordinated development of multiple resources such as economy and environment. In addition, the present invention also proposes a dynamic prediction model based on big data and machine learning technology, which improves the accuracy and reliability of the prediction results, and the real-time data analysis and feedback mechanism ensures the timeliness and accuracy of the evaluation results. Through comprehensive evaluation and optimization suggestions, the present invention provides a scientific and reasonable decision-making basis for distribution network planning and energy storage system deployment, which helps to reduce the risk and cost of energy storage system access, ensure the reliability of the energy storage system and the stability and economy of the distribution network access to the energy storage system, and improve economic and social benefits.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a technical and economic evaluation method for a distribution network solution with energy storage access, implemented based on a power system, comprising the following steps:

[0008] S1: Collect multi-dimensional data based on the energy storage system, and evaluate the energy storage system indicators based on the collected energy storage system data;

[0009] S2: Build a dynamic prediction model based on big data and machine learning to track and predict the energy storage system in real time;

[0010] S3: Based on the collection and analysis of real-time data from the distribution network, the operating status of the energy storage system after access and the grid load change data are monitored and analyzed in real time;

[0011] S4: Conduct a multi-dimensional dynamic evaluation of the monitored and analyzed data, and based on the evaluation results, propose comprehensive decisions guided by energy storage demand and aimed at the coordinated development of multiple resources including the economy and the environment.

[0012] Preferably, the multi-dimensional data of the energy storage system in step S1 includes performance dimension, economic dimension, environmental dimension and potential dimension;

[0013] The performance dimension is used to obtain the performance data of the energy storage system itself, so as to evaluate the value of the energy storage system connected to the power system based on its own performance;

[0014] The economic dimension is used to collect data on the initial investment cost, operating cost, expected return, payback period, and risk analysis of the energy storage system, thereby achieving an assessment based on direct economic value;

[0015] The environmental dimension is based on obtaining the contribution data of energy storage system to energy conservation and emission reduction under the operation of the energy storage system, so as to evaluate the energy storage system by reducing energy consumption and carbon emissions;

[0016] The potential dimension is used to evaluate data on the energy storage system's technological transformation space, policy changes, and market demand.

[0017] Preferably, the performance dimension, economic dimension, environmental dimension and potential dimension have different weights when evaluating the energy storage system, and the weights of the multiple dimensions will change according to the actual needs of the energy storage system to be connected to the distribution network.

[0018] Preferably, the dynamic prediction model based on big data and machine learning in step S2 includes a data preprocessing module, a model module, a model training module and a dynamic prediction module;

[0019] The data preprocessing module performs preprocessing operations on the collected power system data and multi-dimensional data of the energy storage system, removes abnormal, repeated and missing data, and converts them into unified standard data;

[0020] The model module is used to store and run the algorithm model, so as to evaluate the economic benefits of the energy storage system connected to the distribution network based on the algorithm model;

[0021] The model training module uses the preprocessed data to perform training operations on the algorithm model to improve the accuracy of the model;

[0022] The dynamic prediction module inputs real-time power system data based on the trained algorithm model, and outputs a dynamic energy storage system access plan based on the actual needs of the power system, thereby obtaining an energy storage system access decision based on the best economic benefits, thereby providing feedback to verify the accuracy of the model.

[0023] Preferably, the model module predicts the relationship between the economic benefits of the energy storage system and the multidimensional data of the energy storage system itself and the input variables of the actual needs of the distribution network by finding the best linear relationship, and the formula is: y = θ0 + θ1x1 + θ2x2 + ... + θnxn + ∈

[0024] Where y is the predicted value, xi is the input variable, θi is the model parameter, and ∈ is the error term.

[0025] Preferably, in step S3, weights are assigned to the operating status of the energy storage system after connection and the change in grid load, and the evaluation indicators and weights are dynamically adjusted according to real-time data feedback to ensure the timeliness and accuracy of the evaluation results.

[0026] Preferably, the decision-making based on the multi-dimensional dynamic evaluation results in step S4 includes the following steps:

[0027] S11: Based on the multi-dimensional dynamic evaluation results, the order of different benefits generated by the energy storage system after connecting to the distribution network is ranked;

[0028] S12: Optimize the decision based on the ranking of dynamic evaluation and in combination with the actual needs of the distribution network, the performance of the energy storage system and environmental factors, and evaluate the optimal decision as the best decision in terms of reliability and economy of energy storage access;

[0029] S13: Re-input the above-mentioned optimal decision into the dynamic prediction model, and perform feedback optimization on the energy storage system configuration optimization and operation strategy adjustment to further improve the technical and economic decision-making performance of the model.

[0030] (III) Beneficial effects

[0031] Compared with the prior art, the present invention provides a technical and economic evaluation method for a distribution network solution with energy storage access, which has the following beneficial effects: the evaluation method proposed in the present invention comprehensively considers multiple dimensions such as performance, economy, environment and future potential, and realizes a comprehensive and dynamic evaluation of the energy storage access solution through a dynamic prediction model and real-time data analysis technology, thereby avoiding the harm that large-scale energy storage access to the power grid may cause to the safe and stable operation of the power grid, and takes economic benefits and environmental benefits as optimization targets in the optimization process, thereby realizing the goal of coordinated development of multiple resources such as economy and environment; and the present invention also proposes a dynamic prediction model based on big data and machine learning technology, which improves the accuracy and reliability of the prediction results, and the real-time data analysis and feedback mechanism ensures the timeliness and accuracy of the evaluation results; through comprehensive evaluation and optimization suggestions, the present invention provides a scientific and reasonable decision-making basis for distribution network planning and energy storage system deployment, which helps to reduce the risk and cost of energy storage system access, ensure the reliability of the energy storage system and the stability and economy of the distribution network access to the energy storage system, and improve economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0033] In order to better understand the purpose, structure and function of the present invention, and to address the shortcomings of the existing energy storage system access to the distribution network, the evaluation method proposed in the present invention comprehensively considers multiple dimensions such as performance, economy, environment and future potential, and through dynamic prediction models and real-time data analysis technology, a comprehensive and dynamic evaluation of the energy storage access scheme is achieved to avoid the harm that large-scale energy storage access to the power grid may cause to the safe and stable operation of the power grid, and economic benefits and environmental benefits are taken as optimization targets in the optimization process, achieving the goal of coordinated development of multiple resources such as economy and environment; and the present invention also proposes a dynamic prediction model based on big data and machine learning technology, which improves the accuracy and reliability of the prediction results, and the real-time data analysis and feedback mechanism ensures the timeliness and accuracy of the evaluation results; the present invention provides a scientific and reasonable decision-making basis for distribution network planning and energy storage system deployment through comprehensive evaluation and optimization suggestions, which helps to reduce the risk and cost of energy storage system access, ensure the reliability of the energy storage system and the stability and economy of the distribution network access to the energy storage system, and improve economic and social benefits. A technical and economic evaluation method for a distribution network scheme of energy storage access in the present invention is further described in detail.

[0034] refer to Figure 1 The present invention provides a method for evaluating the technical and economic performance of a distribution network solution for energy storage access, which is implemented based on a power system and includes the following steps:

[0035] S1: Collect multi-dimensional data based on the energy storage system, and evaluate the energy storage system indicators based on the collected energy storage system data;

[0036] In this technical solution, the multi-dimensional data of the energy storage system includes performance dimension, economic dimension, environmental dimension and potential dimension;

[0037] The performance dimension is used to obtain the performance data of the energy storage system itself, so as to evaluate the value of the energy storage system connected to the power system based on its own performance;

[0038] The economic dimension is used to collect data on the initial investment cost, operating cost, expected return, payback period, and risk analysis of the energy storage system, thereby achieving an assessment based on direct economic value;

[0039] The environmental dimension is based on obtaining the contribution data of energy storage system to energy conservation and emission reduction under the operation of the energy storage system, so as to evaluate the energy storage system by reducing energy consumption and carbon emissions;

[0040] The potential dimension is used to evaluate data on the energy storage system's technological transformation space, policy changes, and market demand.

[0041] Specifically, the performance dimension, economic dimension, environmental dimension and potential dimension have different weights when evaluating the energy storage system, and the weights of the multiple dimensions will change according to the actual needs of the energy storage system to be connected to the distribution network. It comprehensively considers multiple dimensions such as technology, economy, environment and future potential, and realizes a comprehensive and dynamic evaluation of the energy storage access plan through dynamic prediction models and real-time data analysis technology.

[0042] S2: Build a dynamic prediction model based on big data and machine learning to track and predict the energy storage system in real time;

[0043] In this technical solution, the dynamic prediction model based on big data and machine learning includes a data preprocessing module, a model module, a model training module and a dynamic prediction module;

[0044] The data preprocessing module performs preprocessing operations on the collected power system data and multi-dimensional data of the energy storage system, removes abnormal, repeated and missing data, and converts them into unified standard data;

[0045] The model module is used to store and run the algorithm model, so as to evaluate the economic benefits of the energy storage system connected to the distribution network based on the algorithm model, and predict the relationship between the economic benefits of the energy storage system and the multidimensional data of the energy storage system itself and the input variables actually required by the distribution network by finding the best linear relationship. The formula is: y = θ0 + θ1x1 + θ2x2 + ... + θnxn + ∈

[0046] Where y is the predicted value, xi is the input variable, θi is the model parameter, and ∈ is the error term;

[0047] The model training module uses the preprocessed data to perform training operations on the algorithm model to improve the accuracy of the model;

[0048] The dynamic prediction module inputs real-time power system data based on the trained algorithm model, and outputs a dynamic energy storage system access plan based on the actual needs of the power system, thereby obtaining an energy storage system access decision based on the best economic benefits, thereby providing feedback to verify the accuracy of the model.

[0049] Specifically, the dynamic prediction model based on big data and machine learning technology improves the accuracy and reliability of prediction results; the real-time data analysis and feedback mechanism ensures the timeliness and accuracy of the evaluation results.

[0050] S3: Based on the collection and analysis of real-time data from the distribution network, the operating status of the energy storage system after access and the grid load change data are monitored and analyzed in real time;

[0051] In this technical solution, weights are assigned to the operating status of the energy storage system after connection and the changes in grid load, and the evaluation indicators and weights are dynamically adjusted based on real-time data feedback to ensure the timeliness and accuracy of the evaluation results.

[0052] S4: Conduct a multi-dimensional dynamic evaluation of the monitored and analyzed data, and based on the evaluation results, propose a comprehensive decision guided by energy storage demand and with the coordinated development of multiple resources such as economy and environment;

[0053] In this technical solution, the decision is made based on the multi-dimensional dynamic evaluation results, including the following steps:

[0054] S11: Based on the multi-dimensional dynamic evaluation results, the order of different benefits generated by the energy storage system after connecting to the distribution network is ranked;

[0055] S12: Optimize the decision based on the ranking of dynamic evaluation and in combination with the actual needs of the distribution network, the performance of the energy storage system and environmental factors, and evaluate the optimal decision as the best decision in terms of reliability and economy of energy storage access;

[0056] S13: Re-input the above-mentioned optimal decision into the dynamic prediction model, and perform feedback optimization on the energy storage system configuration optimization and operation strategy adjustment to further improve the technical and economic decision-making performance of the model.

[0057] Specifically, through comprehensive evaluation and optimization suggestions, a scientific and reasonable decision-making basis is provided for distribution network planning and energy storage system deployment, which helps to reduce investment risks and improve economic and social benefits.

[0058] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A technical and economic evaluation method for energy storage access distribution network solution, based on power system implementation, characterized in that: The following steps are involved: S1: Collect multi-dimensional data based on the energy storage system, and evaluate the energy storage system indicators based on the collected energy storage system data; S2: Build a dynamic prediction model based on big data and machine learning to track and predict the energy storage system in real time; S3: Based on the collection and analysis of real-time data from the distribution network, the operating status of the energy storage system after access and the grid load change data are monitored and analyzed in real time; S4: Conduct a multi-dimensional dynamic evaluation of the monitored and analyzed data, and based on the evaluation results, propose comprehensive decisions guided by energy storage demand and aimed at the coordinated development of multiple resources including the economy and the environment.

2. The method for technical and economic evaluation of a distribution network solution for energy storage access according to claim 1 is characterized in that: The multi-dimensional data of the energy storage system in step S1 includes performance dimension, economic dimension, environmental dimension and potential dimension; The performance dimension is used to obtain the performance data of the energy storage system itself, so as to evaluate the value of the energy storage system connected to the power system based on its own performance; The economic dimension is used to collect data on the initial investment cost, operating cost, expected return, payback period, and risk analysis of the energy storage system, thereby achieving an assessment based on direct economic value; The environmental dimension is based on obtaining the contribution data of energy storage system to energy conservation and emission reduction under the operation of the energy storage system, so as to evaluate the energy storage system by reducing energy consumption and carbon emissions; The potential dimension is used to evaluate data on the energy storage system's technological transformation space, policy changes, and market demand.

3. The method for technical and economic evaluation of a distribution network solution with energy storage access according to claim 2 is characterized in that: The performance dimension, economic dimension, environmental dimension and potential dimension have different weights when evaluating the energy storage system, and the weights of the multiple dimensions will change according to the actual needs of the energy storage system to be connected to the distribution network.

4. The method for technical and economic evaluation of a distribution network solution for energy storage access according to claim 1 is characterized in that: The dynamic prediction model based on big data and machine learning in step S2 includes a data preprocessing module, a model module, a model training module and a dynamic prediction module; The data preprocessing module performs preprocessing operations on the collected power system data and multi-dimensional data of the energy storage system, removes abnormal, repeated and missing data, and converts them into unified standard data; The model module is used to store and run the algorithm model, so as to evaluate the economic benefits of the energy storage system connected to the distribution network based on the algorithm model; The model training module uses the preprocessed data to perform training operations on the algorithm model to improve the accuracy of the model; The dynamic prediction module inputs real-time power system data based on the trained algorithm model, and outputs a dynamic energy storage system access plan based on the actual needs of the power system, thereby obtaining an energy storage system access decision based on the best economic benefits, thereby providing feedback to verify the accuracy of the model.

5. The method for technical and economic evaluation of a distribution network scheme with energy storage access according to claim 4 is characterized in that: The model module predicts the relationship between the economic benefits of the energy storage system and the multidimensional data of the energy storage system itself and the input variables of the actual needs of the distribution network by finding the best linear relationship. The formula is: y = θ0 + θ1x1 + θ2x2 + ... + θnxn + ∈ Where y is the predicted value, xi is the input variable, θi is the model parameter, and ∈ is the error term.

6. The method for technical and economic evaluation of a distribution network solution with energy storage access according to claim 1, characterized in that: In step S3, the operating status of the energy storage system after connection and the change in grid load are weighted, and the evaluation indicators and weights are dynamically adjusted according to real-time data feedback to ensure the timeliness and accuracy of the evaluation results.

7. The method for technical and economic evaluation of a distribution network solution with energy storage access according to claim 1, characterized in that: The decision is made based on the multi-dimensional dynamic evaluation results in step S4, including the following steps: S11: Based on the multi-dimensional dynamic evaluation results, rank the order of different benefits generated after the energy storage system is connected to the distribution network; S12: Optimize the decision based on the ranking of dynamic evaluation and in combination with the actual needs of the distribution network, the performance of the energy storage system and environmental factors, and evaluate the optimal decision as the best decision in terms of reliability and economy of energy storage access; S13: Re-input the above-mentioned optimal decision into the dynamic prediction model, and perform feedback optimization on the energy storage system configuration optimization and operation strategy adjustment to further improve the technical and economic decision-making performance of the model.

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