Abstract:A comprehensive supervisor control for a hybrid system that comprises wind and photovoltaic generation subsystems, a battery bank, and an ac load is developed in this paper. The objectives of the supervisor control are, primarily, to satisfy the load power demand and, second, to maintain the state of charge of the battery bank to prevent blackout and to extend the life of the batteries. For these purposes, the supervisor controller determines online the operation mode of both generation subsystems, switching f… Show more
“…The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”
Section: Centralized Control Schemementioning
confidence: 99%
“…Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution. Often, MO problems do not have a single solution but rather a set of non-dominated solutions, called a Pareto set, which include alternatives representing potential compromises among The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system Energies 2017, 10, 620 5 of 25 operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.
“…The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”
Section: Centralized Control Schemementioning
confidence: 99%
“…Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution. Often, MO problems do not have a single solution but rather a set of non-dominated solutions, called a Pareto set, which include alternatives representing potential compromises among The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system Energies 2017, 10, 620 5 of 25 operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.
“…In HRES the supervisory control system is responsible for determining the reference power that must be generated by/stored in the ESS. Then the converters associated with the renewable sources and EES are controlled, so that the energy sources work as required by the supervisory control system [6], [7].…”
Recent year's renewable energy sources plays important role in power generation system. Due to intermittent nature of renewable sources such as wind and solar, the energy storage systems (ESS) are required in renewable power generating systems. This paper deals with power management of the ESS based on hydrogen storage (electrolyzer, hydrogen tank, fuel cell) and battery integrated in hybrid renewable energy systems. The supervisory controller based on neuro-fuzzy inference system determines the power that must be generated by or stored in hydrogen and battery. The solar photovoltaic panels and wind turbine used as primary energy sources. The dc-dc converters connect all the energy sources to central dc bus. The model for each process component is developed in a MATLAB/Simulink environment.
“…This system uses the PV power optimally by diverting the excess power for production of hydrogen through electrolysis. Another HDGS system with wind, PV, and battery sources has been discussed by Valenciaga et al, [7], where they propose a control technique that not only maintains the load demand but also the state-of-charge of the battery. Blaabjerg et al, [16] have given an elaborate review of the various control and grid synchronization techniques used in HDGS systems.…”
Abstract-A new, hybrid integrated topology, fed by photovoltaic (PV) and fuel cell (FC) sources and suitable for distributed generation applications, is proposed. It works as an uninterruptible power source that is able to feed a certain minimum amount of power into the grid under all conditions. PV is used as the primary source of power operating near maximum power point (MPP), with the FC section (block), acting as a current source, feeding only the deficit power. The unique "integrated" approach obviates the need for dedicated communication between the two sources for coordination and eliminates the use of a separate, conventional dc/dc boost converter stage required for PV power processing, resulting in a reduction of the number of devices, components, and sensors. Presence of the FC source in parallel (with the PV source) improves the quality of power fed into the grid by minimizing the voltage dips in the PV output. Another desirable feature is that even a small amount of PV power (e.g., during low insolation), can be fed into the grid. On the other hand, excess power is diverted for auxiliary functions like electrolysis, resulting in an optimal use of the energy sources. The other advantages of the proposed system include low cost, compact structure, and high reliability, which render the system suitable for modular assemblies and "plug-n-play" type applications. All the analytical, simulation, and experimental results of this research are presented.
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