Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage

Marzband, Mousa, Alavi, Hamed, Ghazimirsaeid, Seyedeh Samaneh, Uppal, Hasan and Fernando, Terrence (2017) Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage. Sustainable Cities and Society, 28. pp. 256-264. ISSN 2210-6707

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Abstract

In recent years, increasing interest in developing small-scale fully integrated energy resources in distributed power networks and their production has led to the emergence of smart Microgrids (MG), in particular for distributed renewable energy resources integrated with wind turbine, photovoltaic and energy storage assets. In this paper, a sustainable day-ahead scheduling of the grid-connected home-type Microgrids (H-MG) with the integration of non-dispatchable/dispatchable distributed energy resources and responsive load demand is co-investigated, in particular to study the simultaneously existed uncontrollable and controllable production resources despite the existence of responsive and non-responding loads. An efficient energy management system (EMS) optimization algorithm based on mixed-integer linear programming (MILP) (termed as EMS-MILP) using the GAMS implementation for producing power optimization with minimum hourly power system operational cost and sustainable electricity generation of within a H-MG. The day-ahead scheduling feature of electric power and energy systems shared with renewable resources as a MILP problem characteristic for solving the hourly economic dispatch-constraint unit commitment is also modelled to demonstrate the ability of an EMS-MILP algorithm for a H-MG under realistic technical constraints connected to the upstream grid. Numerical simulations highlight the effectiveness of the proposed algorithmic optimization capabilities for sustainable operations of smart H-MGs connected to a variety of global loads and resources to postulate best power economization. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 21% in comparison with conventional EMS.

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