This paper presents the basis for the development of an intelligent and autonomous energy management strategy for hybrid power system (HPS). Two hierarchical levels are proposed to control and manage the HPS. The low level is performed by a local control unit (DC-DC converters controller) of the different power sources. Dynamic equations describing the coupling of converters are derived, and a robust sliding mode dynamic controller is designed. The high level is performed by the online supervisor unit. This unit is designed by applying on-line Takagi-Sugeno fuzzy logic principles. As a result the robust control system gets rid of the limits of the HPS, which has the imprecision, uncertainty, strong coupling, and nonlinearity, to achieve its tractability, robustness, and low solution cost. Under the operation constraints related to each type of sources, the simulation results show that the optimal operation objective of HPS has been achieved. 1. Introduction The limited reserves of fuel oils, their pollution impact, and their unstable prices have significantly increased the interest in renewable energy sources (RES: photovoltaic modules, wind turbine, etc.) to produce electricity that is an essential factor for the development of the human societies. In this context, HPSs, which combine renewable energy and conventional energy sources with the storage systems, are very interesting in terms of environmental protection and reduction of the effects of greenhouse gas emissions. In other words, the HPSs can provide an economic, environment friendly, and reliable supply of electricity. To perform the hybrid systems a control strategy has to be designed and implemented on the system. Numerous solution methodologies have been proposed in this field in the last decade [1–4]. Many of them are based on a static approach. However, the development of robust controller is necessary to ensure stability and robustness of the multisources of the renewable energy systems. Subsequently, others advanced control strategies, such as predictive control, fuzzy logic, and neural network, have been developed and successfully applied [5–7]. However, these approaches require considerable computing resources and as a result their applicability for real-time applications is reduced. In this paper, emphases are put on the energy management and control from the viewpoint of control theory. The proposed management strategy and controller, designed by applying fuzzy logic and sliding mode principles, not only is simple, stable, and robust, but also reduces the computational resources.
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