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Intelligent and Cost-Effective Energy Management of Smart Building Equipped with Behind the Meter Energy Storage Systems
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In recent years, smart buildings have drawn great attention in strategic energy planning discussions as one of the key players in integration of renewable energies into the smart power system. This has led to development of intelligent energy management systems that optimally schedule renewable energy generation, energy storage systems and residential energy consumption. In this dissertation, various problem statements associated with the development of intelligent energy management strategies for smart buildings have been studied. These problem statements are mainly focused on the optimization of the energy costs and maximization of self-consumption of solar generation under normal operating conditions as well as in presence of inherent uncertainties associated with solar energy generation, load consumption and Plug in Electric Vehicles (PEVs). As the first step of this work, optimum energy management of smart buildings based on day-ahead forecasting for load and generation is studied with an emphasis on cost-effective integration of PEVs and household batteries as behind the meter energy storage systems. To address the shortcomings of current methods, the concept of Energy Price Tag (EPT) is proposed which reflects the average price of the stored energy in storage systems. Then, in order to tackle the challenges imposed by uncertainties, we take a step further and utilize the EPT concept to develop a flexible and cost-effective energy management strategy in presence of deviations from day-ahead forecasts. Finally, to provide a thorough study, we focus on intelligent and cost-effective management of smart buildings’ energy transactions with other smart systems. With the desire to provide more incentives to promote deployment of smart buildings in the peer-to-peer (P2P) energy trading paradigm, a real-time operational framework for P2P energy sharing between a smart building and a PEV charging station is proposed. In the proposed framework the EPT concept is utilized to develop a dynamic pricing mechanism that is not dependent on day-ahead energy generation and load profile, hence providing an effective solution to managing uncertainties. The results obtained from different chapters of this dissertation could be useful for government agencies, policy makers and utilities in designing incentives to accelerate the integration of smart buildings as well as energy storage systems into smart power systems.
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