Activation of electrical loads under electricity price uncertainty

Minhas, Daud Mustafa; Khalid, Raja Rehan; Frey, Georg

doi:10.1109/sege.2017.8052828

Cited by 3 publications

(2 citation statements)

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“…Moreover, the pricing markets usually have complicated attributes of non-linearity and inconstancy, so price prediction can help the energy producers and purchasers to improve their respective scheduling techniques, that increase their profit benefits and minimize the electricity prices, respectively [53]. A power utility can design this day-ahead electricity price.…”

Section: A Prediction Modulementioning

confidence: 99%

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Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Minhas

Frey

2020

IEEE Access

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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 timeahead 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 gridsupplied 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. c Minimum power usage by the TCL unit (W ). P TCL c Maximum power usage by the TCL unit (W ). T TCL c User's set maximum temperature ( • C). T TCL c User's set minimum temperature ( • C).

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Section: A Prediction Modulementioning

confidence: 99%

“…DT loads are deterministic power demands predicted dayahead using the prediction module. The power limitations of DT loads is illustrated in (53) and the accumulated load demands are established in (54). Fig.…”

Section: ) Dt Demandsmentioning

confidence: 99%