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Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

机译：基于系统动力学和马尔可夫链的电力零售市场电力需求和市场份额预测

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摘要

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.
机译：由于零售电力市场的放松管制，消费者可以自由选择零售电力供应商，并且由于新进入者的增加，市场主体面临激烈的竞争。在这种情况下，预测市场份额稳定后所有市场实体的变化对于供应商做出营销决策很重要。本文建立了基于马尔可夫链的市场份额预测模型，并基于经济发展，政策因素，环境因素，电力能源替代，能源利用，能源替代，能源利用，能源替代，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用，能源利用等五个因素进行了分析，建立了系统动力学模型来预测用电量。和电网发展。为了实际应用，选择了中国广东省的零售电力市场。在不同的情景下，对2016年至2020年广东省的工业，商业和商业用电量进行了预测，并使用马尔可夫链模型分析了主要市场实体的市场份额。结果表明，直接交易电力将在未来占总用电量的70％至90％。这为供应商的决策和电力行业的发展提供了宝贵的参考。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第2期|4671850.1-4671850.11|共11页
  • 作者

    Yan Qingyou; Qin Chao; Nie Mingjian; Yang Le;

  • 作者单位

    North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China;

    North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China;

    North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China;

    North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China;

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