Optimal Real-time Pricing Model of Smart Grid Based on Markov Decision Process

Authors

Jiangbo Li, College of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
Wang Bo, College of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
Gao Yan, College of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
Huizhen Zhang, College of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: Real-time electricity price strategy is the effective means to save electricity and improve user electricity utility value. A real-time electricity price optimization model based on Markov Decision Process was raised. Using finite horizon method, the model structure the mathematical model which makes the expected utility maximum of supply side and demand side, and optimize the existing electricity utility function according to decreasing risk theory which using logarithmic form can describe the power utility of user more accurate. Particle Swarm Optimization was used to solve this model and make the results compare with the situation of fixed power price, the results show that this model is better than fixed power price in power saving and utility improving. Beside, the fluctuation of real-time price is between highest price and lowest price, and the fluctuation is not strong.

Recommended Citation

Li, Jiangbo; Bo, Wang; Yan, Gao; and Zhang, Huizhen (2020) "Optimal Real-time Pricing Model of Smart Grid Based on Markov Decision Process," Journal of System Simulation: Vol. 28: Iss. 11, Article 16.
DOI: 10.16182/j.issn1004731x.joss.201611016
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss11/16

First Page

2756

Revised Date

2016-03-31

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201611016

Last Page

2763

CLC

TP391.9

Recommended Citation

Li Jiangbo, Wang Bo, Gao Yan, Zhang Huizhen. Optimal Real-time Pricing Model of Smart Grid Based on Markov Decision Process[J]. Journal of System Simulation, 2016, 28(11): 2756-2763.

DOI

10.16182/j.issn1004731x.joss.201611016