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

The Journal of Engineering

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[1] Department of Electronics and Telecommunications Engineering, School of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania;Department of System Cybernetics, Graduate school of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, 739-8527, Japan;Department of Telecommunications and Communications Networks, School of Informatics, College of Informatics and Virtual Education, University of Dodoma, P.O. Box 490, Dodoma, Tanzania;

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

DOI  :  10.1049/joe.2014.0199

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY