With the increase of advanced and sophisticated loads on the load side, consumers of electricity are demanding higher power reliability and quality. The reinforcements of power systems for higher reliability are costly and time-consuming. In trending technology, the microgrid offers an alternative on-site power system reinforcement that requires less time and cost. Moreover, the microgrid can integrate both conventional and unconventional distributed generations (DGs) to the system at several load points. These DGs are used as backups to or in line with the grid to reduce failure rates and downtimes experienced by consumers. Some unconventional renewable sources are intermittent in nature, and it is pertinent to quantify the effects of such intermittency on the reliability of power systems. This paper studies the stochastic effects of the integration of two intermittent power sources, wind and solar, in a power system. Furthermore, three types of consumer models were studied: residential, commercial and industrial. The DGs were used as backups to the grid, and the ensuing increase of the power system's reliability was measured.
Solar PV and wind energy are the most important renewable energy sources after hydroelectric energy with regard to installed capacity, research spending and attaining grid parity. These sources, including battery energy storage systems, and well-established load modeling have been pivotal to the success of the planning and operation of electric microgrids. One of the major challenges in modeling renewable-based DGs, battery storage, and loads in microgrids is the uncertainty of modeling their stochastic nature, as the accuracy of these models is significant in the planning and operation of microgrids. There are several models in the literature that model DG and battery storage resources for microgrid applications, and selecting the appropriate model is a challenging task. Hence, this paper examines the most common models of the aforementioned distributed energy resources and loads and delineates the mathematical rigor required for characterizing the models. Several simulations are conducted to demonstrate model performance using manufacturers’ datasheets and actual atmospheric data as inputs.
This paper proposes a robust continuous nonlinear control method for grid‐tied photovoltaic (PV) inverters by combining model predictive control and integral sliding mode control (ISMC). The robustness of the proposed augmented controller is demonstrated mathematically by showing that the equivalent control signal of the ISMC system cancels disturbances to the system. Further, the proposed controller can compensate for system perturbations without knowing their characteristics, thereby simplifying controller design for the PV systems. The robustness of the proposed controller against parameter uncertainties and external disturbances was demonstrated via experimental and simulation verification.
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