251 International Journal of Advancements in Mechanical and Aeronautical Engineering – IJAMAE Volume 2 : Issue 2 [ISSN : 2372-4153] Publication Date: 19 October, 2015 Demand Response for Smart Home: An Evaluation from an End-User’s Perspective Based on a High Resolution Power Demand Model Efrain Bernal, Mustafa Süslü, Jian Xie Abstract—This study investigates customer response to demand response with direct load control at the smart home by implementing home energy management HEM algorithm. An economical evaluation is done to estimated the benefices from the user’s perspective. Initially, this study outlines the development of a high-resolution smart home power demand model with the trends in photovoltaic PV, home automation systems, efficient appliances and battery support, to estimate the potential impacts of demand response programs on the residential load profiles. Finally, the results show highly annual and daily variations on the demand load profile for fixed tariff scenarios up 44% with respect to the current demand profile. Consequently, in the case of TOU rate tariff scenarios, a critical variation for the peak and off-peak transition period was found. This variation goes up 64% with respect to the current demand profile pattern and 80% with respect to the smart home demand profile pattern. Additionally in case of TOU tariff, the end-users income is much higher than that of fixed tariff. For small household only using solar panel with home automation system the net cumulative income surpluses, but using battery doesn’t reflect economically favorable conditions. In 4 or more person household, using 2kWh battery with automations, the net income is 41% higher than using only PV. Furthermore, comparing it with PV and automations of 100%, the net income increases other 15%. Keywords—home automation, HEM, smart home, demand response. I. Introduction This increasing electricity price, scarcity of fuels, green energy technology and as long as world’s environments concern, all these end up vastly increase of renewable energy in recent years over the whole world. Among them Germany is the pioneer of using renewable energy sources especially photovoltaic PV. It is very common that in winter season power supply is not enough from the PV and results needing a lot of power from the Grid or other sources to cover up the required energy of the household. Meanwhile in summer season a massive amount of power is generated from the PV and it overwhelms over the needs of household in day time. The excess power can’t be wasted, so the way is to supply the excess power to the grid. MSc. Efrain Bernal Alzate Institute of energy conversion and storage EWS, University ULM, Germany Dr. Mustafa Süslü Institute of energy conversion and storage EWS, University ULM, Germany Prof. Dr-Ing Jian Xie Institute of energy conversion and storage EWS, University ULM, Germany Therefore, to enlarge the service of the current electrical distribution grid and to reduce the need for grid expansion, utilities are promoting demand response programs that offer customers financial incentive to shift some demand to off- peak times. From the user perspective, it is not clear, if these programs in combination with automation technologies, are rentable or not. At this aim, a high resolution power demand model was initially developed [25] and it is used to estimated the economical benefic of demand response programs in Germany from the user´s perspective. The model presented in this paper improves significantly the modeling of energy consumption and demand side analysis proposed in [2-7,21], allowing the simulation of the effects of efficiency improvements on appliances, different levels of photovoltaic/battery penetration, with a home energy management HEM algorithm to model a high-resolution smart home power demand over different demand response scenarios. A detailed presentation of the model is given in Section II. In Section III, the power demand setup and the HEM algorithm are presented. The economical evaluation for future different scenarios is described in Section IV. The results are discussed in Section V and conclusions are drawn in section VI. II. Smart Home Model The methodology for the modeling of a high resolved smart home profile can be summarized as follows. A. High Resolution Model The structure of the model is presented in Fig.1. This is a bottom-up modeling technique [3,4], that generates synthetic activity patterns for each household member which are then converted into power demand for the household, taking into account the effects of technology improvements, automation algorithms and other smart home requirements for the construction of the load model. On the left side of the diagram, the reference load is established from a report on energy use of households in Germany [9] as well as an annual report of the demand load profile [10]. In this part the load model is calibrated to represent a German household. On the right side of the diagram, the considerations and trends for the different future scenarios are analyzed and brought to the model generator to create the smart home's load profile. This procedure can be repeated for an arbitrary number of households, to create a larger set of demand data. B. Generation of synthetic activity patterns Modeling individual’s behavior is a complex task, due to the stochastic nature of the activities performed. This model uses non-homogeneous Markov chain to model occupant