access icon openaccess Distributed demand-side management optimisation for multi-residential users with energy production and storage strategies

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 06/08/2014, Accepted 27/10/2014, Revised 24/10/2014, Published 29/10/2014

This study considers load control in a multi-residential setup where energy scheduler (ES) devices installed in smart meters are employed for demand-side management (DSM). Several residential end-users share the same energy source and each residential user has non-adjustable loads and adjustable loads. In addition, residential users may have storage devices and renewable energy sources such as wind turbines or solar as well as dispatchable generators. The ES devices exchange information automatically by executing an iterative distributed algorithm to locate the optimal energy schedule for each end-user. This will reduce the total energy cost and the peak-to-average ratio (PAR) in energy demand in the electric power distribution. Users possessing storage devices and dispatchable generators strategically utilise their resources to minimise the total energy cost together with the PAR. Simulation results are provided to evaluate the performance of the proposed game theoretic-based distributed DSM technique.

Inspec keywords: smart meters; building management systems; power generation economics; iterative methods; power generation scheduling; power generation dispatch; renewable energy sources; demand side management; distributed power generation; energy storage; cost reduction; optimisation; distributed algorithms

Other keywords: load control; optimal energy scheduling; energy storage strategy; iterative distributed algorithm; energy scheduler devices; multiresidential users; electric power distribution; distributed demand side management optimisation; total energy cost reduction; ES devices; information exchange; smart meters; energy production; renewable energy sources; peak-to-average ratio; dispatchable generators; PAR; distributed DSM technique

Subjects: Power system management, operation and economics; Interpolation and function approximation (numerical analysis); Distributed power generation; Optimisation techniques

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