Abstract
ABSTRACT One of the key strategies for reducing the rate of environmental pollution is decarbonizing the power industry. In this work, we investigate the effects of energy storage systems (ESS) and fluctuations in renewable energy on climate change mitigation in a grid-connected microgrid. This analysis has been carried out by utilizing an improved energy management system (EMS) and optimal economic dispatch using computational models of mixed-integer linear programming (MILP). In addition to battery deterioration analysis, long short-term memory (LSTM) is developed to estimate photovoltaic and wind power renewable production, energy price, and load requirement. A sensitivity assessment is also performed to evaluate the influence of different input constraints on the model. The output results demonstrated that the EMS could schedule power effectively while considering electricity pricing. By up to 1636.96 $/hr. in day-ahead revenue with the degradation effect and 1811.96 $/hr. without the degradation effect, the analysis confirmed the usefulness of the proposed framework. Through the case studies explained, the new objective function observed minimum power costs with battery degradation by up to 1.10% less as compared to without battery degradation effect. Furthermore, the second case analysis indicates the significance of considering forecasted electrical parameters for realistic microgrid power dispatch.
Published Version
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