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In this study, a Pareto efficient incentive-based real-time pricing model was designed for balanced energy consumption scheduling (ECS) in a smart grid. In this model, the energy consumption of each subscriber is monitored and updated in real-time by an individual smart meter, and a cost-effective ECS is determined. The most recent research has not considered a balanced distribution of costs and profits to the participants. In general, there is a trade-off between service providers and subscribers. A service provider tries to maximize its profit, and a subscriber tends to minimize its cost. Therefore, the well-adjusted cost and profit distribution of a service provider and subscribers is considered by controlling the incentive degree in a Stackelberg game. The multiobjective genetic algorithm is applied to show the Pareto efficient solutions of a service provider and subscribers. Furthermore, welfare is introduced as the third objective in proposing a practical solution. It is used to select one of the multiple Pareto efficient solutions. Our model decreases subscriber costs by 9.1&#x0025; and the peak-to-average ratio (PAR) by 33.2&#x0025;, on average, compared with non-scheduling. The model also reduces the PAR by 11.3&#x0025; and increases the provider&#x2019;s profit by 34.9&#x0025; and total welfare by 60.0&#x0025;, on average, compared with day-ahead scheduling.

Pareto Efficient Incentive-Based Real-Time Pricing Model for Balanced Smart Grids