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One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users' convenience rates, and thermal comfort level.Index Terms-Demand response, energy management system, micro-grid, smart home, thermal comfort zone. 1949-3053

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Publication informationEnergy, 73 (2014): 958-967Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6116 Publisher's statementThis is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy (VOL 73, ISSUE 2014, (2014 AbstractVirtual Power Plant (VPP) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a generation companys (GenCo) expected profit. Furthermore, the Conditional Value at Risk (CVaR) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.

In this paper, an effective energy management system (EMS) for application in integrated building and microgrid system is introduced and implemented as a multi-objective optimization problem. The proposed architecture covers different key modelling aspects such as distributed heat and electricity generation characteristics, heat transfer and thermal dynamics of sustainable residential buildings and load scheduling potentials of household appliances with associated constraints. Through various simulation studies under different working scenarios with real data, different system constraints and user's objectives, the effectiveness and applicability of the proposed model is studied and validated compared to the existing residential EMSs. The simulation results demonstrate that the proposed EMS has the capability not only to conserve energy in sustainable homes and microgrid system and to reduce energy consumption costs accordingly, but also to satisfy user's comfort level through optimal scheduling and operation management of both demand and supply sides.

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