Premium Power Value-Added Service Product Decision-Making Method Based on Multi-Index Two-Sided Matching

With the further reform and development of the electricity retail market in China, the premium power value-added service (PPVS) product is becoming increasingly demanded by high-tech customers. However, the problem of PPVS demand-level matching has not been well studied. How to build an optimal matc...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.62166-62181
Main Authors: Ma, Yuanqian, Li, Qiyuan, Lin, Zhenzhi, Ge, Zheng, Lei, Yi, Zhao, Xinlong
Format: Article
Language:English
Subjects:
Companies
Customers
Decision making
Electricity
Empirical analysis
evaluation index
Indexes
Industrial parks
Linguistics
Model matching
multi-objective optimization
Multiple objective analysis
Optimization
perceived utility
Power quality
Premium power value-added service
two-sided matching
Uninterruptible power systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the further reform and development of the electricity retail market in China, the premium power value-added service (PPVS) product is becoming increasingly demanded by high-tech customers. However, the problem of PPVS demand-level matching has not been well studied. How to build an optimal matching and decision-making model which can improve both customers' and electricity retail companies' satisfaction degree is an urgent issue. Thus, this paper proposes a decision-making method for PPVS products with multi-index expectation based on two-sided matching. Firstly, a multi-index evaluation system is established from the perspective of customers and electricity retail companies. Secondly, the loss and gain matrices of customers and electricity retail companies are constructed, considering the difference between the expected level (EL) and the actual level (AL) of the evaluation indices. Thirdly, perceived utility (PU) of both customers and electricity retail companies are described with the introduction of elation function and disappointment function, due to different perceptions concerning the matching results under both sides' evaluation indices. Fourthly, a multi-objective two-sided matching optimization model that aims to maximize the PU of both sides is developed. Finally, an empirical analysis is conducted on three large high-tech electronic-based customers in an industrial park in western Guangdong for demonstrating the effectiveness and rationality of the proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3071879