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This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization. A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

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Integrating renewable energy resources (RER) into the power grid may jeopardize the whole power system if the penetration level or solar PV uncertainty is not thoroughly managed. The critical impact on the behaviour of power system can be observed during the line outage in a power grid connected with large-scale RER. Therefore, contingency analysis (CA) is crucial to assess such hybrid power grid. This paper proposes a framework based on CA to assess the simultaneous effect of large-scale solar photovoltaic (PV) power plants integrated with the existing power grid, particularly, in the aspects of implementing effective measuring indices. Simulation studies have been carried out on a practical power system, which was modelled by considering the probability of solar irradiance at different locations in Sarawak. The study presented in this paper can provide an insight to identify the level of insecurity for a large-scale deployment of solar PV systems in Sarawak.