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Conventionally, base stations of off-grid telecommunication (telecom) towers are powered by diesel generator, which results in high operating cost and carbon emission. Hence, the photovoltaic (PV) wind battery based hybrid renewable energy system (HRES) is proposed for off-grid telecom towers in rural areas. However, owing to high capital cost and intermittent nature of renewable energy resources, optimal sizing of HRES is inevitable to have economical and reliable power supply. An index named excess energy generation (EEG) is developed to reduce the surplus renewable energy, which cannot be sold to grid in off-grid towers. Therefore, three objective functions are formulated viz., loss of power-supply probability, cost of electricity, and EEG, which are incommensurate and contradictory in nature. Thus, a discrete multiobjective grey wolf optimization (DMGWO) algorithm is developed for the optimal sizing of HRES. DMGWO utilizes the concept of internal archive to store nondominated solutions and enables fast convergence rate without premature termination as a result of inherent leader selection mechanism. A practical approach is adopted in optimal sizing by considering wind capacity as discrete variable, which is handled by the corrective algorithm embedded in DMGWO. The intermittency in source and variability in telecom load is incorporated using probability distribution functions. The Euclidean distance approach is adopted to find the solution (configuration of PV panels, wind turbines, and battery) from the optimal Pareto front obtained from DMGWO. Also, sensitivity analysis is carried out to observe the effect of the design variables on the objective functions. [ABSTRACT FROM AUTHOR]

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs' arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs' dynamic scheduling scheme to minimize the grid's charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods. [ABSTRACT FROM AUTHOR]