The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
The biophotovoltaic cell (BPV) is deemed to be a potent green energy device as it demonstrates the generation of renewable energy from microalgae; however, inadequate electron generation from microalgae is a significant impediment for functional employment of these cells. The photosynthetic process is not only affected by the temperature, CO2 concentration and light intensity but also the spectrum of light. Thus, a detailed understanding of the influences of light spectrum is essential. Accordingly, we developed spectrally optimized light using programmable LED arrays (PLA)s to study the effect on algae growth and bioelectricity generation. Chlorella is a green microalga and contains chlorophyll-a (chl-a), which is the major light harvesting pigment that absorbs light in the blue and red spectrum. In this study, Chlorella is grown under a PLA which can optimally simulate the absorption spectrum of the pigments in Chlorella. This experiment investigated the growth, photosynthetic performance and bioelectricity generation of Chlorella when exposed to an optimally-tuned light spectrum. The algal BPV performed better under PLA with a peak power output of 0.581 mW m-2 for immobilized BPV device on day 8, which is an increase of 188% compared to operation under a conventional white LED light source. The photosynthetic performance, as measured using pulse amplitude modulation (PAM) fluorometry, showed that the optimized spectrum from the PLA gave an increase of 72% in the rETRmax value (190.5 μmol electrons m-2 s-1), compared with the conventional white light source. Highest algal biomass (1100 mg L-1) was achieved in the immobilized system on day eight, which translates to a carbon fixation of 550 mg carbon L-1. When artificial light is used for the BPV system, it should be optimized with the light spectrum and intensity best suited to the absorption capability of the pigments in the cells. Optimum artificial light source with algal BPV device can be integrated into a power management system for low power application (eg. environment sensor for indoor agriculture system).