Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain
Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market shar...
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2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/4671850 |
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doaj-d9ab6f1a5fde44bbbfc638dd3532cc612020-11-24T21:03:08ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/46718504671850Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov ChainQingyou Yan0Chao Qin1Mingjian Nie2Le Yang3School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Changping, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Changping, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Changping, Beijing 102206, ChinaDue to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was constructed to forecast the electricity consumption based on the analysis of five factors which are economic development, policy factors, environmental factors, power energy substitution, and power grid development. For a real application, the retail electricity market of Guangdong province in China was selected. The total, industrial, and commercial electricity consumption in Guangdong from 2016 to 2020 were predicted under different scenarios, and the market shares of the main market entities were analyzed using Markov chain model. Results indicated that the direct trading electricity would account for 70% to 90% of the total electricity consumption in the future. This provided valuable reference for the decision-making of suppliers and the development of electricity industry.http://dx.doi.org/10.1155/2018/4671850 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingyou Yan Chao Qin Mingjian Nie Le Yang |
spellingShingle |
Qingyou Yan Chao Qin Mingjian Nie Le Yang Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain Mathematical Problems in Engineering |
author_facet |
Qingyou Yan Chao Qin Mingjian Nie Le Yang |
author_sort |
Qingyou Yan |
title |
Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain |
title_short |
Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain |
title_full |
Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain |
title_fullStr |
Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain |
title_full_unstemmed |
Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain |
title_sort |
forecasting the electricity demand and market shares in retail electricity market based on system dynamics and markov chain |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was constructed to forecast the electricity consumption based on the analysis of five factors which are economic development, policy factors, environmental factors, power energy substitution, and power grid development. For a real application, the retail electricity market of Guangdong province in China was selected. The total, industrial, and commercial electricity consumption in Guangdong from 2016 to 2020 were predicted under different scenarios, and the market shares of the main market entities were analyzed using Markov chain model. Results indicated that the direct trading electricity would account for 70% to 90% of the total electricity consumption in the future. This provided valuable reference for the decision-making of suppliers and the development of electricity industry. |
url |
http://dx.doi.org/10.1155/2018/4671850 |
work_keys_str_mv |
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