A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.

Optimal Dynamic Pricing for Trading-Off User Utility and Operator Profit in Smart Grid

Fortino, Giancarlo;
2017

Abstract

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.
Data models; Dynamic power pricing; Internet of Things (IoT); operator profit; Optimization; Power demand; Predictive models; Production; Real-time systems; smart grid; smart grid operator (SGO); Smart grids; user utility; Software; Control and Systems Engineering; Human-Computer Interaction; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/269408
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