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The microgrid has been shown to be profitable, reliable, and efficient for military, commercial, and university-like installations. However, until now, there has been no study to show how and when a residential microgrid may be profitable. Therefore, in this paper, we present a design space exploration methodology of the microgrid by modeling all the energy resources at the residential level and conducting numerous simulations with various parameters. Moreover, a set of rules are defined to make the stakeholders in the microgrid profitable. Also, by analyzing the number of houses in the microgrid, we observe that the number of years it takes to return the capital costs invested in the microgrid may become adequately short for a certain range of the number of houses. For instance, if the aggregator owns the renewable energy resources, e.g., solar panels, it may profit in less than five years when 500 houses participate in the microgrid where each house owns 500 sf solar panels. On the other hand, if the prosumers own the renewable energy resources, e.g., solar panels, the aggregator may profit in about a year. Typically, for an apartment-block type housing area in U.S. there are more than 1000 houses, therefore the aggregator profitability may improve furthermore.

Electric Vehicle (EV) optimization involves stringent constraints on driving range and battery lifetime. Sophisticated embedded systems and huge number of computing resources have enabled researchers to implement advanced Battery Management Systems (BMS) for optimizing the driving range and battery lifetime. However, the Heating, Ventilation, and Air Conditioning (HVAC) control and BMS have not been considered together in this optimization. This paper presents a novel automotive climate control methodology that manages the HVAC power consumption to improve the battery lifetime and driving range. Our experiments demonstrate that the HVAC consumption is considerable and flexible in an EV which significantly influences the driving range and battery lifetime. Hence, this influence on the above-mentioned constraints has been modeled and analyzed precisely, then it has been considered thoroughly in the EV optimization process. Our methodology provides significant improvement in battery lifetime (on average 14%) and average power consumption (on average 39% reduction) compared to the stateof-the-art methodologies. I. INTRODUCTION AND RELATED WORKElectric Vehicles (EVs) have been accepted as sustainable solution and a new paradigm of transportation [1] to address the environmental issues caused by greenhouse gases and other pollutants coming from road transportation [2]. Despite the incentives provided by governments to promote EV deployment [3], EVs pose new challenges in the trade-off between costs and performance [4]. The driving range and battery lifetime are the challenges that have become major design objectives for EVs. The cost, volume, and weight constraints in battery pack design make them the major bottleneck restricting the amount of energy stored for driving [5]. On the other hand, the battery lifetime is directly related to the State-of-Health (SoH) which represents the battery capability to store and deliver energy. The SoH degrades over time according to the battery usage pattern and the battery will become useless when it degrades for about 20% [6]. In order to alleviate the driving range and battery lifetime issue, a Battery Management System (BMS) is typically implemented to monitor and control the battery cells [1]. The BMS prevents overcharging, overdischarging, overheating, and imbalance of battery cells to improve their energy efficiency and lifetime. By presenting Hybrid Energy Storage System (HESS) [3] that may consist of ultracapacitors accompanied with battery cells, the BMS evolved to handle the charge management for heterogeneous energy storage to improve energy efficiency and battery lifetime. Other components inside EV, e.g. power converters, inverters, electrical motor, etc. demonstrate different efficiency in various conditions. Hence, the BMS may optimize the battery or HESS usage based on the components' efficiency map. Also, [3] [7] have illustrated that the BMS may predict and optimize the energy consumption more efficiently by having the route information. In the process of optimizin...