In this study, a heuristics-based optimisation methodology for a day-ahead unit commitment (UC) model in microgrids is proposed. The model aims to schedule the power among the different microgrid units while minimising the operating costs together with the CO(2)emissions produced. A storage device is added where the charge and discharge schedule is calculated according to both objectives. In addition, as a part of the demand side participation strategy, a charging schedule was determined for the electric vehicles (EV) in order to increase the system security and further reduce the costs and emissions. A congestion management approach is also introduced, which eliminates congestions by effective unit scheduling according to congestion signals provided by the distribution system operators. The complete day-ahead time horizon is divided in 96 time steps (each with a 15 min time span), which makes the UC problem more complicated. The studied system includes renewable energy resources, a storage unit, two microturbines, a fuel cell and EVs. The results demonstrate that the proposed model is robust and is able to reduce the microgrid operating costs and emissions by optimal scheduling of the microgrid units, and is able to take into account local congestion problems

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Microgrids are small-scale power systems including distributed generation (DG) units, storage devices and controllable loads, and can operate either connected or isolated from the utility grid. Ensuring an efficient, reliable, economic and environmentally friendly microgrid operation, an environomic power dispatch system is needed. In this paper, a formulation of an environomic scheduling approach in microgrids is proposed using multi-objective decision making method. The application aims to fulfill the time-varying energy demand while minimizing the costs and emissions of the internal production and imported energy from the utility grid. Operational constraints such as generator limits, operation and maintenance costs and the intermittency renewable energy sources (RES) will be satisfied. A representative microgrid structure, including measurement data, is studied as an example and some simulation results are presented to demonstrate the performance of the environomic scheduling approach.

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