Abstract
Unbalanced power demand across time slots causes overload in a specific time zone. Various studies have proved that this can be mitigated through smart grid and price policy, but research on time preference is insufficient. This study proposed a real-time pricing model on a smart grid through a two-stage Stackelberg game model based on a utility function that reflects the user’s time preference. In the first step, the suppliers determine the profit-maximizing price, and then, the users decide the electricity usage schedule according to the given price. Nash equilibrium and comparative analysis of the proposed game explain the relationship between time preference, price, and usage. Additionally, a Monte Carlo simulation demonstrated the effect of the change in time preference distribution. The experimental results confirmed that the proposed real-time pricing method lowers peak-to-average ratio (PAR) and increases overall social welfare. This study is meaningful in that it presents a pricing method that considers both users’ and suppliers’ strategies with time preference. It is expected that the proposed method would contribute to a reduction in the need for additional power generation facilities through efficient operation of the smart grid.
Highlights
With global warming caused by greenhouse gases, countries around the world have long been contemplating policies to develop new and renewable energy and increase energy efficiency [1,2,3,4].One of the most representative and favorable approaches is the smart grid
The effectiveness of the proposed real-time pricing (RTP) model is quantitatively verified by comparing it with the results of a system that does not adjust prices in real time
We proposed a two-stage Stackelberg game model between an energy supplier and users in this paper
Summary
With global warming caused by greenhouse gases, countries around the world have long been contemplating policies to develop new and renewable energy and increase energy efficiency [1,2,3,4]. Samadi et al [21] solved the problem of finding energy prices to maximize social welfare In this study, they used a distributed algorithm to obtain optimal electricity usage to maximize social welfare, which is defined as the difference in users’ utility and the power supply costs. This study aims to propose a two-stage Stackelberg game model that reflects the time preference of electricity use Through this model, we want to determine the electricity usage schedule and rate policy that maximizes the utility of users and profits of the supplier. The effectiveness of the proposed real-time pricing (RTP) model is quantitatively verified by comparing it with the results of a system that does not adjust prices in real time It varies the distribution of consumers’ time preferences and examines the impact on the balance of power demand, power supply cost, usage fee, and social welfare. This paper attempts to attain a practical contribution in that it examines how the responses of consumers and suppliers differ by performing a simulation by changing time preferences
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