This paper presents an optimized energy management system (OEMS) to control the microgrid of a remote temporary military base (FOB) featuring diesel generators, a battery energy storage system (BESS), and photovoltaic (PV) panels. The information of the expected electric demand is suitably used to improve the sizing and management of the BESS, according to the days of operation. The OEMS includes power electronics to charge the batteries from either the PV source or the diesel generators, and 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 new contribution of this paper includes the optimization of a FOB's microgrid, where critical loads must be serviced at all times. The proposed optimization, which uses Special Order Sets for the semicontinuous function handling, also integrates economic evaluations by properly taking into account how the size of BESS affects its charge/discharge cycle; thus, the FOBs' battery lifetime, in addition to its fuel consumption. Results from optimization are employed by the OEMS to coordinate the energy sources, and match the critical and noncritical 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.

This paper presents a novel power flow control system for a remote military microgrid with hybrid energy storage. A combination of batteries and supercapacitors (SCs) is managed by the novel control system to increase the battery life by redirecting the higher frequency current that would have to flow in the battery if SCs weren't present. This work offers a practical solution to manage the SC current and ensure that the SCs are never overcharged or commanded to support the system when they are discharged to the lower operating limit chosen. The new controller allows the independent selection of the low pass filter parameter and the number of SCs. By making the most out of these two degrees of freedom, we investigate different configurations, identifying the one achieving the highest cash flow for the overall system. Modeling, simulations and experimental verification are presented and linked to the sensitivity analysis of the economics of the military microgrid.

This paper proposes a Resistance perturbation based maximum power point tracking (MPPT) with an adaptive control limit algorithm to extract the maximum power from solar photovoltaic (PV) array. This algorithm consists of two main functions, namely 1) resistance perturbation & observation (RP&O) and 2) adaptive resistance control (ARC) limit. The RP&O operates the PV array at maximum power point (MPP), and the ARC limit continuously monitors the resistance of the PV R _pv to determine the operating limit of MPP. The ultimate aim of proposing this algorithm is to reduce the oscillations and improve MPP's tracking performance for sudden variation in temperature and irradiance conditions. Furthermore, it does not require an expensive pyranometer or temperature sensor to track the MPP of the PV array. This paper also compares the proposed and conventional MPPT algorithm's performance. Its validation results in both MATLAB/Simulink and experimental studies are presented under constant and sudden changes in irradiance conditions.

This paper presents a novel power flow control strategy, combined with an economic analysis, for an energy management system (EMS) involving a hybrid energy storage. The EMS operates a remote microgrid and directs the power flow to either batteries or supercapacitors to increase the life of the batteries. This paper demonstrates how the use of supercapacitors increases the lifetime of the batteries and ultimately how it affects the economics of the system. The proposed EMS controller also compensates for the 120-Hz ripple on the dc bus. Modeling, simulations, and experimental verification are presented together with the procedure to perform the assessment of the battery lifetime, according to the tuning parameters of the controller.

This paper presents an original two-steps methodology to size DERs (Distributed Energy Resources) in stand-alone microgrids, to be installed in different areas, featuring different meteorological conditions, but same kind of loads. Design examples are simulated to analyze how an increased level of resilience, considered in terms of number of days of autonomy after an initial incident, affects the sizing of a PV field and its storage. A practical tool to support strategic choices is methodologically illustrated and applied to two case studies to find the best configuration, which is identified by a trade-off among fuel consumption, sizes of PV arrays and resilience. Key design parameters help in designing the best system according to the location, by focusing on the newly identified key performance indicator $NPV^{s}$ , the simplified net present value of specific scenarios of interest, where a penalty is introduced to account for less than the ideal target of autonomy. The model-based design used to create the microgrid simulations is validated by experimental measurements on a test-bed hybrid microgrid.

This research is focused on microgrid design supporting tools and presents an innovative framework for renewable energy (RE) sources’ site selection, integrating multicriteria decision-making (MCDM) methods with resilience considerations and alignment to the Sustainable Development Goals (SDGs). It addresses present climatic challenges, identifies key causes of possible power failures, and develops strategies to mitigate their effects, while providing tools for energy managers and decision-makers to select suitable RE sources/technologies, based on geographical and sustainability criteria. The framework categorizes criteria into quantitative and qualitative types, adopting a cost ($\mathcal{C}$)- and benefit ($\mathcal{B}$)-based approach. The Analytic Hierarchy Process (AHP) calculates criteria weights to ensure accuracy and compatibility in decision-making, integrating SDG objectives into the evaluation process. This study focuses on five major RE options, photovoltaic (PV), wind, wave, tidal, and geothermal, analyzing more than 50 criteria for each energy type. This evaluation incorporates the expertise of over 50 experts and case studies, making it one of the most extensive research efforts in RE site selection. By systematically addressing resilience challenges and linking them with SDG priorities, this study provides a robust framework for evaluating and optimizing RE options. Its methodologies offer significant contributions to advancing sustainable energy development and enhancing energy systems’ resilience to climate and infrastructural challenges.