Demand shaping to achieve steady electricity consumption with load balancing in a smart grid

The purpose of this paper is to study conflicting objectives between the grid operator and consumers in a future smart grid. Traditionally, customers in electricity grids have different demand profiles and it is generally assumed that the grid has to match and satisfy the demand profiles of all its users. However, for system operators and electricity producers, it is usually most desirable, convenient and cost effective to keep electricity production at a constant rate. The temporal variability of electricity demand forces power generators, especially load following and peaking plants to constantly manipulate electricity production away from a steady operating point. These deviations from the steady operating point usually impose additional costs to the system. In this work, we assume that the grid may propose certain incentives to customers who are willing to be flexible with their demand profiles which can aid in the allowance of generating plant to operate at a steady state. In this paper we aim to compare the tradeoffs that may occur between these two stakeholders. From the customers' perspectives, adhering to the proposed scheduling scheme might lead to some inconvenience. We thus quantify the customers inconvenience versus the deviations from an optimal set by the grid. Finally we try to investigate the trade-off between a grid load balancing objective and the customers' preferences.

[1]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[2]  Robert Schober,et al.  Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[3]  George Kesidis,et al.  Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[4]  Gintaras V. Reklaitis,et al.  Quantifying System-Level Benefits from Distributed Solar and Energy Storage , 2012 .

[5]  Georgios B. Giannakis,et al.  Cooperative multi-residence demand response scheduling , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[6]  Gang Xiong,et al.  Smart (in-home) power scheduling for demand response on the smart grid , 2011, ISGT 2011.

[7]  Steven E. Collier,et al.  Ten steps to a smarter grid , 2009, 2009 IEEE Rural Electric Power Conference.

[8]  Thomas Garrity,et al.  Getting Smart , 2008, IEEE Power and Energy Magazine.

[9]  Amr M. Youssef,et al.  A Water-Filling Based Scheduling Algorithm for the Smart Grid , 2012, IEEE Transactions on Smart Grid.

[10]  Rajamani Krishnan,et al.  Meters of tomorrow [In My View] , 2008 .