Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.
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Grid-connected inverters have a very significant role in the integration of renewable energy resources with utility grids. However, in recent studies, it is revealed that grid-connected inverters are vulnerable to instability when the nature of the grid changes from strong to weak, which produces uncertainty and performance degradation. An increase in grid impedance decreases stability margins, tremendously increases total harmonic distortion after a certain limit, and amplifies the voltage harmonics in the grid. A cascaded reduced switch symmetrical multilevel inverter along with an adaptive hybrid control technique is proposed for injecting power generated from distributed energy resources efficiently and stably to the utility grid. This research contributes twofold: a multilevel inverter topology and the other is its control method. The multilevel inverter reduces total harmonic distortion and size of the filter while increasing power handling capability. The control unit of the proposed system further consists of two parts: one is the synchronous frame current controller, and the other is stationary frame adaptive harmonic compensators. The grid current controller which is working in a synchronous reference frame ensures regulated current injection to the grid. It is not favorable to implement a harmonic compensator in a synchronous reference frame due to computation complexities. Therefore, the stationary reference frame controllers are used for harmonic compensations. But the resultant harmonic compensators have narrow bandwidth. Thus, these are not robust against variation in grid frequency. In this research, this problem is resolved by adding the adaptive features within the harmonic compensators which shift its passing band according to the frequency of the grid while remaining with the same bandwidth. The proposed design of the hybrid frame controller is validated by considering a nine-level inverter connected with a weak grid.INDEX TERMS Adaptive harmonic compensators, Grid-connected inverters, harmonic compensators, multilevel inverters, phase disposition level shift carrier pulse width modulation, reduced switch multilevel inverters, total harmonic distortion, weak grid. I. INTRODUCTIONG RID connected inverters(GCIs) play an important role in enabling the use of renewable energy resources. It is used to connect Distributed Generators(DGs) with the existing grid or within a microgrid. Most of the renewable energy resources are intermittent in nature [1] [2]. Thus, the energy produced is affected by environmental conditions like weather, temperature, sunlight, speed of the wind and
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