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A building energy management (BEM) system is a core hardware/software platform that enables demand response applications for building operators in the smart grid environment. This paper presents the BEM algorithm that is designed to be robust against communication failures and data errors. It has been implemented in the smart building located at Yildiz Technical University in Istanbul, Turkey, and results are reported herein. Appliance usage profiles and customer preferences used in the BEM demonstration were derived from a survey of Turkish customers. Both summer and winter usage profiles were used to validate the efficacy of the proposed algorithm in a real-world smart building environment. The paper also discusses lessons learned from field implementation.

Abstract:The fast depletion of fossil fuels, climate change, and global warming have become major worldwide problems and alternatives for conventional transportation have been actively researched in the last decade. Compared to available conventional vehicles, electric vehicles have a leading position due to their environmentally friendly transportation.Recent electric vehicle penetration brings the necessity of a high number of charging stations, which are considered to be established in community areas such as shopping centers, hospitals, commercial areas, university campuses, residential areas, and streets. Deployment planning of charging stations is very important for driver expectations and social and economic impacts of electric vehicles. In this regard, optimizing the number of charging stations and their locations is very important for wide-scale use of electric vehicles. Including a new criterion into the existing charging station planning methods is difficult. Due to their structural characteristics, a new flexible planning model of charging stations is needed.In the new model, the queue theory and the analytic hierarchy process are used together to develop a mathematical model for both optimizing the number of charging stations and choosing the best charger locations in a selected region.A case study is performed to show the suitability of the proposed model for finding the optimum location and number of charging stations by evaluating survey data, which reflects drivers' habits within a university campus.

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