Adaptability assessment method of energy storage working conditions based on cloud decision fusion under scenarios of peak shaving and frequency regulation

doi:10.1016/j.est.2020.101784

Journal of Energy Storage

Volume 32, December 2020, 101784

https://doi.org/10.1016/j.est.2020.101784 Get rights and content

Highlights

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An evaluation index system for the adaptability of energy storage conditions based on AHP is established.
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The subjectivity of the evaluation results is reduced by the Entropy weight method.
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Cloud model theory is adopted to overcome the randomness and ambiguity of the evaluation process.
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D-S evidence theory is used to reduce the uncertainty of energy storage system selection.

Abstract

Energy storage participating in grid auxiliary services can effectively enhance the regulation capacity of the grid and promote the consumption of renewable energy, and the selection type of energy storage systems is the basis to ensure its safe and economic operation. Starting from the economics and safety of energy storage systems, an adaptive evaluation method of energy storage working conditions based on the cloud decision fusion is proposed. Aiming at strong subjective characteristics of the analytic hierarchy, an adaptability assessment model of energy storage working conditions based on the entropy weight-analysis hierarchy process method is established to obtain the scores of different types of energy storage systems. Aiming at the characteristics of ambiguity and randomness in decision-making indicators, an adaptability assessment model of energy storage working conditions based on the entropy weight-cloud model is established to obtain the scores of different types of energy storage systems. The results of the two scores are fused using Dempster-Shafer evidence theory to get the evaluation result of the best energy storage condition adaptability. In the application scenarios of the peak shaving and frequency regulation, the effectiveness of the proposed method is verified by simulation analysis of performance indicators of the peak shaving and frequency regulation. The simulation results show that the iron phosphate battery has the highest adaptability to work conditions of the peak shaving and frequency regulation, and the Dempster–Shafer evidence theory can eliminate the randomness and qualitative-quantitative doping of decision indicators on the selection type of energy storage systems, which can provide a theoretical basis for the planning of energy storage stations.

Introduction

Energy storage technology has been widely used in peak shaving, frequency regulation, backup power of the power grid, and renewable energy consumption [1,2], but various energy storage technology development levels are different in integrated power level, continuous discharge time, energy conversion efficiency, cycle life, power, energy density, and cost. Due to the complex and diverse application scenarios of energy storage technologies, different application scenarios have different requirements for energy storage technologies. At present, there are no standardized evaluation indicators for the selection of energy storage configuration schemes in different working conditions. Therefore, it is very important for specific scenarios to carry out research on the adaptability evaluation of energy storage working conditions [3].

Standardized and scientific evaluation methods can effectively guide the selection of energy storage power stations and improve the adaptivity evaluation mechanism of energy storage working conditions. At present, there is a lack of comprehensive evaluation methods for the evaluation of energy storage working conditions, which leads to the blindness of the selection process. The configuration of energy storage systems mainly adopts artificial subjective evaluation methods, lacking scientific and objective evaluation methods, and the evaluation of energy storage system operating conditions suitability is a complex and uncertain system engineering, and the index information of energy storage working condition adaptability evaluation exists Vagueness and randomness, so we need to find an evaluation method that takes into account both the subjectivity of the evaluation results and the fuzziness of the evaluation process.

The current research on energy storage systems mainly focuses on coordinated and optimized control [4], capacity configuration [5], battery state estimation [6,7] and grid auxiliary services [8], and there are few studies on the adaptability evaluation of energy storage conditions. As an important part of an energy storage power station planning, the adaptability analysis of energy storage working conditions should meet technology applicability and economics of investment costs. The process involves multiple decision-making indicators, with the characteristics of incomplete information, qualitative and quantitative information, so it is a complex decision problem. The methods for the adaptability evaluation of energy storage conditions mainly include analytic hierarchy process (AHP), fuzzy theory and multi-objective optimization methods. In [9], the AHP is used to evaluate the operating performance of the second-use battery energy storage system in the active distribution network, but the weights of the AHP were obtained by expert experiences and had a strong subjectivity. In [10], from the perspectives of economy, society, environment and technology, a multi-class heterogeneous electric energy storage system optimization model based on fuzzy optimal/worst method and approaching ideal solution ranking method was established, and the comprehensive performance of ion battery energy storage systems obtained by solving the model is the optimal. In [11], a multi-objective optimization method based on the enhanced ε constraint method was proposed, the optimal technology, the lowest cost and the lowest environmental impact were regarded as the objective function to realize the optimal configuration of energy storage systems. But the decision indicators adopted in the proposed method were too little. In [12], a multi-criteria decision-making method based on fuzzy theory was proposed. The comparison factor of energy storage systems was quantified according to expert experiences and converted into a standard trapezoidal fuzzy number, and the evaluation result was obtained by the AHP. In [13], the selection scheme of electrochemical energy storage systems based on the interval AHP was proposed. The AHP and entropy weight method was combined to overcome the problem of too strong subjectivity, but the proposed method was only aimed at electrochemical energy storage systems, and the evaluation indicators of energy storage working conditions were not comprehensive. In [14], considering the four influencing factors of economy, reliability, energy consumption, and environmental protection, a comprehensive performance evaluation model for integrated energy systems of the microgrid was established, and the weight assignment of each index was respectively determined by the AHP and the improved entropy weight method.

Through the above literature analysis, the AHP can combine qualitative and quantitative analysis of complex decision-making problems, use the experience of decision-makers to measure the importance of various indicators, and effectively solve decision-making problems that are difficult to solve with quantitative methods [15]. However, the analytic hierarchy process is too subjective and overemphasizes the role of experience. The entropy method can get a comprehensive weight, which can effectively reduce the subjectivity of the evaluation results [12].

However, the influence of the ambiguity and randomness of decision indicators on the selection results of energy storage systems cannot be ignored. The cloud model is an effective method to solve the ambiguity and randomness of information. In [16], considering the uncertainty of the randomness and ambiguity, an evaluation method of water quality based on the cloud model was proposed. By evaluating the eutrophication status of 12 typical lakes and reservoirs in China, the effectiveness of the proposed method was verified. In [17], a fuzzy comprehensive evaluation method for the distribution network based on the cooperative game model and trapezoidal cloud model was proposed. A comprehensive evaluation system of the distribution network was established. The constant weight of each index was determined by the cooperative game model, and the normal weight was modified by the variable weight formula. The membership function was obtained according to the trapezoidal cloud model to achieve the comprehensive evaluation of the distribution network. In [18], a comprehensive evaluation model of the distribution network financing lease risk based on the cloud model and entropy weight method was proposed, which fully utilized the qualitative and quantitative conversion of the cloud model and the characteristics of randomness and ambiguity of risk. In [19], a slope stability evaluation method based on a multi-dimensional cloud model was proposed, which can clearly describe the random and fuzzy distribution characteristics of the measured data within a limited interval. In [20], a multi-attribute decision-making method for groundwater remediation based on the cloud model was proposed, the concentration of pollution was summarized by the cloud generator, and the weight of the attribute was calculated by the weight cloud module to eliminate the ambiguity, randomness and uncertainty that exist in the process of groundwater remediation. In [21], a supplier selection framework based on the cloud model and possibility theory was proposed, and the fuzzy analytic hierarchy process was used to determine the index weight considering the uncertainty of standards and sub-standards.

In summary, due to the randomness and ambiguity of energy storage comprehensive decision-making indicators, it is easy to affect the rationality and accuracy of the evaluation results. Therefore, in the application scenario of energy storage participating in the peaking and frequency regulation, in order to reduce the uncertainty of the evaluation results of the adaptability of energy storage is proposed in this paper, a method for evaluating the adaptability of energy storage conditions based on cloud decision fusion is proposed in this paper. First, from the perspective of subjective and objective evaluation, a comprehensive decision-making index system for energy storage conditions based on the entropy weight-analytic hierarchy process is established, and six types of energy storage systems that meet the peaking and frequency regulation are screened out. Then, from the perspective of the randomness and ambiguity of the evaluation process, an adaptive evaluation model for energy storage conditions based on the entropy weight-cloud model is established to evaluate the performance of six energy storage systems. Finally, the Dempster–Shafer (D-S) evidence fusion theory is used to fuse the two scoring results to reduce the uncertainty in the selection of the energy storage system.

Section snippets

Parameters of different energy storage systems for the peak shaving and frequency regulation

Currently, many types of energy storage systems are used in auxiliary services of power systems. The technical indicators of different types of energy storage systems are suitable for different auxiliary services of the power system. In the case of the peak shaving and frequency regulation, the energy storage system should not only have excellent performance in terms of power level, response speed, continuous discharge time, volume power density and volume energy density, but also consider the

Cloud model theory

The cloud model effectively combines the ambiguity and randomness together, which constitutes a qualitative and quantitative mapping between each other, and provides a powerful means for information processing combining qualitative and quantitative. Let U be a domain represented by an accurate numerical quantity, and T be the corresponding qualitative concept on U. For the element x in any domain, there is a random number with a tendency to stability u∈[0,1], called the membership degree of x

Dempster–Shafer evidence theory

Evidence theory was first proposed by Dempster in 1967 and was further developed by his student Shafer in 1976, called Dempster-Shafer evidence theory (D-S evidence theory). D-S evidence theory adopts the “semi-additivity” principle of reliability, and better deals with the contradiction between subjectivity and objectivity in the problem of uncertainty reasoning.

Define the power set 2^Θ as the number of all subsets and sets of Θ. The basic probability assignment function m is defined as 2^Θ

Overall framework of adaptability evaluation for energy storage working conditions based on the cloud decision fusion

Based on the analysis of the operating data of a photovoltaic power station with the installed capacity of 30MW in a certain region of China, under the scenario of the peak shaving and frequency regulation, the indicators such as energy storage technology requirements, safety, economics and environmental are comprehensively considered to achieve the adaptability assessment of energy storage working conditions. Fig. 4.

Primary selection of energy storage systems based on the AHP and entropy weight method

The purpose of the initial screening of energy storage systems is to exclude

Conclusion

According to the differences in energy storage application scenarios, a planning method of energy storage power station for the peak shaving and frequency regulation is studied, and an adaptability evaluation method of energy storage working conditions based on the cloud decision fusion is proposed. The conclusion is as follows:

1)
According to the requirements of specific application scenarios for energy storage systems, a comprehensive decision indicator system of energy storage systems suitable

CRediT authorship contribution statement

Xiaojuan Han: Conceptualization, Methodology, Software, Investigation, Writing - original draft. Zixuan Wei: Validation, Formal analysis, Visualization, Software, Resources, Writing - review & editing, Supervision, Data curation, Writing - review & editing. Zhenpeng Hong: Validation, Formal analysis, Visualization, Software, Resources, Writing - review & editing, Supervision, Data curation, Writing - review & editing. Dengxiang Liang: Validation, Formal analysis, Visualization, Software,

Declaration of Competing Interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Acknowledgments

This paper is supported by the National Natural Science Fund Project of China (51577065).

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