Abstract
Network microgrids (NMG) has a great potential to improve energy performance, power system reliability, and sustainability. A smart distribution grid faces difficulties dealing with operational issues due to the use of renewable energy resources (RERs) and severe alternations in power demand. Hence, stochastic algorithms are highly useful in providing reliable solutions, particularly when it comes to operational issues. An energy management system has been suggested to address the diurnal optimum planning issue of NMGs taking into account alternating behavior in production and consumption. In this study, two demand response programs were combined into a scheduling model, based on real-time pricing and time of use. The resulting scheme is then resolved by a hybrid genetic algorithm under the uncertainty of the RERs and load. Comparing the simulation outcomes with results from stochastic optimization, the suggested model appears to be effective.
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