Please use this identifier to cite or link to this item: doi:10.22028/D291-30130
Title: Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)
Author(s): Minhas, Daud Mustafa
Frey, Georg
Language: English
Title: IEEE Access
Volume: 8
Startpage: 2052
Endpage: 2072
Publisher/Platform: IEEE
Year of Publication: 2019
DDC notations: 620 Engineering and machine engineering
Publikation type: Journal Article
Abstract: Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home.
DOI of the first publication: 10.1109/ACCESS.2019.2962660
Link to this record: urn:nbn:de:bsz:291--ds-301307
hdl:20.500.11880/28582
http://dx.doi.org/10.22028/D291-30130
ISSN: 2169-3536
Date of registration: 16-Jan-2020
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Georg Frey
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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