Prediction model of stochastic power in industrial parks based on Markov chain

doi:10.3969/j.issn.2097-0706.2023.01.002

Abstract

Abstract:

To promote the consumption of distributed energy in industrial parks， a multi-scenario prediction model based on Markov chain is proposed for stochastic power prediction. Firstly， ARIMA model and Markov chain are respectively used to build the load model of a park according to the random characteristics of renewable energy and the load. Then， posterior information is used to adjust the Markov probability matrix adaptively to improve its prediction accuracy which is affected by the periodical fluctuation of load varying with production，season or other factors. To improve the accuracy of multi-step prediction in a prediction horizon，a multi-scenario prediction model based on scenario tree is proposed， considering the multi-step prediction scenarios and their probabilities. The model can make more effective use of Markov probability matrix. Finally，a case study is implemented based on historical power data of an industrial park. The results show that compared with the unadjusted Markov model， the adaptive model is of a lower load prediction error，and the prediction error of the one with 7-day adjustment time is as low as 0.034 5 p.u. Compared with the commonly used Maximum Likelihood Estimation，the proposed multi-scenario prediction model is of a lower weighted average error.

Key words: renewable energy consumption, Markov chain, prediction model, adaptive adjustment, distributed energy

CLC Number:

TK01

WEI Yanping, WANG Jun, LI Nanfan, SHI Changli. Prediction model of stochastic power in industrial parks based on Markov chain[J]. Integrated Intelligent Energy, 2023, 45(1): 14-22.

Figures/Tables 11

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参数	值
自适应调节参数η	1.2
离散状态数s	30
新能源预测误差的期望μ	0
新能源预测误差的标准差σ	0.05
负荷的历史数据周期T_day/d	30
自适应时间t_day/d	7
非叶节点的子节点数α	2
预测时域/预测步数N_p	4
预测周期Δt/h	0.5

项目	R_RMSE（标幺值）
光伏功率（06:00 —18:00）	0.065 6
风机功率	0.061 4
负荷功率	0.034 5

自适应时间t_day/d	R_RMSE(标幺值)	误差降低/%
无	0.037 1	—
3	0.035 6	3.98
5	0.034 9	5.91
7	0.034 5	7.51
10	0.035 1	5.39
14	0.035 3	4.81

项目	R_RMSE（标幺值）		精度提升/%
项目	场景树法	MLE法	精度提升/%
1步	0.030 2	0.031 9	5.32
2步	0.031 8	0.034 0	6.41
3步	0.033 0	0.036 1	8.58
4步	0.034 5	0.039 2	11.90