This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.
High penetration of distributed generation will be characteristic to future distribution networks. The dynamic, intermittent, uncertain and deregulated nature of distributed generation raises the need for online, distributed economic dispatch techniques. In this paper, we demonstrate the application of such approaches using population dynamics. We propose a congestion management algorithm and demonstrate the notable properties and requirements of the proposed approach.
Increasing participation of prosumers in the electricity grid calls for efficient operational strategies for utilizing the flexibility offered by Distributed Energy Resources (DER) to match supply and demand. This paper investigates the coordination performance of a recently proposed coordination scheme for deferrable loads: Forecast Mediated Market Based Control (F-MBC). Enhancements are made to the simulation setup to enable an analysis of performance in realistic scenarios, with heterogeneous loads and an open-ended simulation horizon. Operational scenarios were formulated to showcase the ability of F-MBC to schedule heterogeneous populations of deferrable loads with dynamic load profiles, supported by a mix of renewable and flexible generation. Availability patterns of devices were generated to take into account varying user preferences. Simulation results indicate that F-MBC was able to achieve good distributed scheduling performance for devices with a high initial power consumption. However, performance for devices with low initial power consumption has been found to be less satisfactory. Several directions for further improvement of the F-MBC scheme and its applications are identified.
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