The usage of electric vehicles (EV) have been spreading worldwide, not only as an alternative to achieve a low-carbon future but also to provide ancillary services to improve the power system reliability. A common problem encountered in the existing alternating current (AC) grids is low power factor, which cause several power quality problems and has worsened with the growing application of distributed generation (DG). Therefore, considering the spread of EVs usage for ancillary services and the low power factor issue in current electrical grids, this paper proposes an improved control strategy for power factor correction of a three-phase microgrid management composed of a photovoltaic (PV) array, dynamic loads, and an EV parking lot. This control strategy aims to support power factor issues using the EV charging stations, allowing the full PV generation. Different operation modes are proposed to fulfill the microgrid and the EV users’ requirements, characterizing a Multi Objective Optimization (MOO) approach. In order to achieve these optimization requests, a dynamic programming method is used to charge the vehicle while adjusting the microgrid power factor. The proposed control algorithm is verified in different scenarios, and its results indicate a suitable performance for the microgrid even during conditions of overload and high peak power surplus in generation unit. The microgrid power factor remains above the desired reference during the entire analyzed period, in which the error is approximately 4.5 less than the system without vehicles, as well as obtains an energy price reduction.

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Abstract-The most basic process in sequence analysis is sequence alignment, usually solved by dynamic programming Needleman-Wunsch algorithm. However, Needleman-Wunsch algorithm has some lack when the length of the sequence which is aligned is big enough. Because of that, sequence alignment is solved by metaheuristic algorithms. In the present, there are a lot of new metaheuristic algorithms based on natural behavior of some species, we usually call them as nature-inspired metaheuristic algorithms. Some of those algorithm that are more efficient are firefly algorithm, cuckoo search, and flower pollination algorithm. In this research, we use those algorithms to solve sequence alignment. The results show that those algorithms can be used to solve sequence alignment with good result and linear time computation.Index Terms-Nature-inspired metaheuristic algorithms, sequence alignment.

Traveling salesman problem (TSP) is a basis for many bigger problems. If we can find an efficient method (that produce a good result in a short time) to solve the TSP, then we will also be able to solve many other problems. In this research, we proposed a new heuristic algorithm for TSP. We used 80 problems from TSPLIB to test the proposed heuristic algorithm. The proposed heuristic algorithm can find the best-known distance for 36 different TSPs. The average of all Goodness Value is 99.50%.

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