Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy.

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A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

Soft Computing ◽

10.1007/s00500-013-1092-9 ◽

2013 ◽

Vol 17 (12) ◽

pp. 2365-2380 ◽

Cited By ~ 62

Author(s):

Fan-Lin Meng ◽

Xiao-Jun Zeng

Keyword(s):

Smart Grid ◽

Real Time ◽

Stackelberg Game ◽

Theoretic Approach ◽

Game Theoretic ◽

Real Time Pricing ◽

Game Theoretic Approach

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Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach

Energies ◽

10.3390/en13226138 ◽

2020 ◽

Vol 13 (22) ◽

pp. 6138

Author(s):

Ri Piao ◽

Deok-Joo Lee ◽

Taegu Kim

Keyword(s):

Smart Grid ◽

Real Time ◽

Time Preference ◽

Stackelberg Game ◽

Price Policy ◽

Theoretic Approach ◽

Time Zone ◽

Efficient Operation ◽

Game Theoretic ◽

Real Time Pricing

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.

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A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach

Energies ◽

10.3390/en11102858 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2858 ◽

Cited By ~ 11

Author(s):

Tengfei Ma ◽

Junyong Wu ◽

Liangliang Hao ◽

Huaguang Yan ◽

Dezhi Li

Keyword(s):

Real Time ◽

Stackelberg Game ◽

Supply And Demand ◽

Energy System ◽

Stackelberg Equilibrium ◽

Energy Hub ◽

Pricing Scheme ◽

Real Time Pricing ◽

Aggregated Load ◽

Energy Provider

This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers.

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