article · IEEE Access
An economic analysis and optimal energy management framework has been formulated for grid-connected microgrids that incorporate renewable generation and varied battery storage technologies. The model explicitly accounts for diverse battery characteristics, including initial charge states, depths of discharge, and total cycle lifetimes. The primary objective is to reduce total operating costs by curbing distributed generator maintenance expenditure, reducing battery investment and replacement costs, and maximising the benefits derived from energy storage. Operational boundaries such as generator capacities, grid import and export limits, load balancing, and battery constraints are strictly observed. Optimization is executed via the General Algebraic Modeling System, integrating stochastic programming to manage market price volatility and information gap decision theory to address electric load demand uncertainty. The method demonstrates robust performance when compared with alternative optimization techniques from existing literature.
Managing microgrids that rely on fluctuating renewable energy and battery storage can be costly and technically challenging. By factoring in battery degradation, market pricing variations, and consumer demand shifts, this approach provides a reliable mathematical strategy to cut operating expenses and extend equipment lifespans in modern power systems.
This work could inform operational control software for microgrid operators, renewable energy project planners, and utility companies seeking to reduce energy storage replacement costs. The findings represent applied computational research evaluated against established literature models, meaning further development and real-world trials on operational hardware would be needed before commercial deployment.
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This paper presents the economic analysis and optimal energy management of a grid-connected MG that comprises renewable energy resources and different battery storage technologies with different characteristics such as initial charge, depth of discharge, and the number of charging/discharging cycles to minimize the total operating cost of the system by maximizing the benefits of BSS, minimizing the investment and replacement cost of BSS, and minimizing the operation and maintenance cost of DGs. Several constraints are considered, such as the output power limits of the distributed generators, the limits of power imported from or exported to the grid, load balance, and other sets of battery storage constraints. The general algebraic modeling system (GAMS) is used to solve the deterministic optimization problem. Second, stochastic optimization is used to solve the deterministic problem with market price uncertainty. Third, robust optimization using the information gap decision theory is presented to model the electric load uncertainty. The validity and effectiveness of the proposed solution are explained by comparing the results obtained by GAMS to the results obtained by other optimization techniques presented in the literature.
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DOI: 10.1109/access.2020.2981697
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