Nowadays, distributed energy resources are widely used to supply demand in micro grids specially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers.The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. In this research, an energy management system (EMS) based on multi-agent system (MAS) controllers is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting the reliability characteristic. In the proposed approach, a reconfigurated hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has ability to control system, even if a failure appears into decision unit. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a
Increasing of renewable power plants has raised the need for intelligent energy management systems (EMSs). The aim of management system is to reduce energy absorbed from fossil sources. In this paper a layered behavioral based architecture named subsumption is employed for energy management in PV based power plant with storage devices and active load. In the proposed architecture components of the plant including SC, PV, battery, and the grid are organized in different layers. Each layer is implemented as a behavioral rule that can independently perceive and act in the environment. There is a hierarchy in the layers where lower layers have more priority and can inhibit higher layers. The layers use a simple protocol to communicate with management unit. Using this approach, a simple, fast, extensible, and fault tolerant EMS is achieved.
Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.
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