Optimized Energy Management System to Reduce Fuel Consumption in Remote Military Microgrids

Abstract

Abstract-This paper presents an optimized energy management system (OEMS) to control the microgrid of a remote temporary military base featuring the diesel generators, the battery energy storage system (BESS) and photovoltaic panels (PV). The information of the expected electric demand is suitably used to improve the sizing and management of the BESS. The OEMS includes power electronics to charge the batteries from either the PV source or the diesel generators, it can function as a current source when it is supplementing the power from one of the generators or as a voltage source when it is the sole source of power for the loads. The novelty in the overall optimization procedure lies (i) in using Special Ordered Sets (SOSs) for the semicontinuous function handling and (ii) in integrating economic evaluations, by properly taking into account how the size of BESS affects its charge/discharge cycle, thus the lifetime. Results from optimization are employed by the OEMS to coordinate the energy sources and match the critical and non critical loads with the available supply. Fuel savings of ≈ 30% (and ≈ 50% adding the PV source) can be achieved with respect to the already improved, but not optimal, solution of a previous work.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

“…These formulations are often aimed at minimizing operating costs [5][6][7][8][9][10][11][12][13] or at minimizing both the operating cost and emissions [14][15][16][17][18]. Sometimes objectives such as load curtailment index [19], voltage deviation [20], power losses [21], fuel consumption [22], and grid power profile fluctuations [23] are also considered as the objective function of MGEM problem. Although the objective function of the energy management problem in [24] includes several objectives, such as minimizing grid voltage deviations, power losses, security margins and energy imported from the main grid; and the objective function presented in [25], includes four objectives of minimizing customer's costs, emissions, load peak and load curve fluctuations, but the proposed MG configuration only consist of renewable sources and electrical vehicles, and controllable DGs or ESS are not considered.…”

“…Different optimization techniques have been used to solve the CEM problem in MGs [4]. These techniques include classical methods (linear programming [5,18,22,26,27], nonlinear programming [20,24,25], dynamic programming [3] and stochastic programming [16,28,29], Heuristic approach [17,30], evolutionary approach [6,7,14,19,31], model predictive control approach [9,12,29], and robust optimization [10,11,15]. A generalized architecture proposed for energy management in microgrids [6] based on multi agent system.…”

Energy Management of Microgrids for Smart Cities: A Review

“…Anglani and Braun [50] presented an economically optimized energy management system (OEMs) for a microgrid which minimized the fuel consumption of a remote temporary military base (FOB) featuring diesel generators. The linear programming method was used to optimize the cost-effectiveness of a diesel generator fuel consumption.…”

OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

“…Ergo, they are not quite deemed suitable for real-time operation but rather for cases of long-term planning and assessment. Simpler, deterministic approaches, such as LP/MILP have been identified as more suited for real-site demonstration [9,13,26,51,52].…”