An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

Bilal Hussain, Nadeem Javaid, Qadeer Ul Hasan, Sakeena Javaid, Asif Khan and Shahzad A. Malik
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Bilal Hussain: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Qadeer Ul Hasan: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Sakeena Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Asif Khan: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Shahzad A. Malik: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan

Energies, 2018, vol. 11, issue 11, 1-40

Abstract: A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.

Keywords: eco-efficient home energy management; dispatch of renewables and energy storage systems; load-shedding-compensating dispatchable generators; optimization using surface fitting techniques; multi-objective genetic algorithm; Pareto optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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