ABSTRACT
In interconnected microgrids, facade thermal photovoltaics (TPVs) systems have to be efficiently scaled and allocated for cost-effective building energy consumption and network operation. This paper aims at defining pertinent innovative solutions for reducing the undesired severe voltage dips and minimizing the relevant total costs of the PVs allocation within interconnected microgrids. To optimally place and size the TPVs, different meta-heuristic optimization tools are considered. Dealing with several scenarios of loads and solar energy output uncertainties, the ability of the novel modified meta-heuristic optimizer based on coronavirus herd immunity optimizer (CHIO) to capture a global optimal solution is evaluated. Using MatlabTM numerical simulations, fair comparison with grey wolf optimization, particle swarm optimization, arithmetic optimization algorithm, and chimp optimization algorithm is presented. The coronavirus herd immunity optimizer tool surpasses the other algorithms in terms of fulfilling the objective function, convergence, and the execution time for the large-scale 295–bus system, which is established of the interconnected IEEE 141–bus, IEEE 85–bus, and IEEE 69–bus subsystems. With the flexible penetration of the building facade TPVs, the voltage profile at all buses is significantly improved. Regarding the overall operational expenses, the CHIO is deemed applicable, replicable and efficient. When compared to the grey wolf optimizer, the CHIO reports expenses of 18.8M$ with savings of 59.67%. The operational voltage level of the studied distributed network is maintained properly by a resilient cluster of 491 clean energy buildings with each having facade area of 200m2.
ABSTRACT
In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system's parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50-85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system's operational data.