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Renewable energy systems (RESs) and electric vehicles (EVs) are critical components of current power systems for lowering greenhouse gas (GHG) emissions, combating climate change, and providing alternative energy sources. However, high initial costs and a lack of infrastructure, power storage, and power quality (PQ) are potential concerns for RESs and EVs in power systems. Among the numerous power quality (PQ) devices, the unified power quality conditioner (UPQC) can compensate for voltage and current-related PQ difficulties, correct the power factor, and is well suited for coordinative operation and control with RESs uncertainty. The position, size, and dynamic VAr control of UPQC, on the other hand, are key influencing variables for enhancing the performance and PQ of electrical distribution systems (EDSs). This paper describes an upgraded UPQC configuration known as open-UPQC (UPQCO) for reducing PQ difficulties caused by the inclusion of PVs and EVs. Further, an efficient gazelle optimization algorithm (GOA) is suggested to solve the multi-objective optimal allocation of the PV-UPQC-O issue with an emphasis on voltage quality, distribution losses, and voltage stability. Simulations were performed for various scenarios using a modified IEEE 33-bus test system. According to the comparison data, the PV-UPQC-O improved the feeder performance more effectively than the standard UPQC and UPQC-O. The proposed PV-UPQC-O results for total loss reduction of 76.15% when compared to base case without EV load penetration. On the other hand, it is around 76.52% reduction when compared to base case with EV load penetration. Furthermore, the proposed GOA outperforms the prairie dog optimization (PDO), pelican optimization algorithm (POA) and coati optimization algorithm (COA) in terms of global solution and convergence. [ABSTRACT FROM AUTHOR]

In this paper, an improved variant of nature-inspired meta-heuristic algorithm inspired by the pollination process of flowering plants called improved flower pollination algorithm (IFPA) is utilized for solving the optimal allocation of capacitor banks (CBs) and distribution-static synchronous compensator (DSTATCOM) problem considering electric vehicle (EV) load growth. In IFPA, a new double-direction learning strategy to advance local searching capacity, a novel dynamic switching probability method to balance global and local searching, and a new greedy technique to increase population diversity. A multi-objective function is formulated for minimizing the real power loss and installation cost of CBs/DSTATCOM. The search space of the multiple CBs/DSTATCOMs is primarily reduced using voltage stability index (VSI) and later the best locations and sizes of CBs/DSTATCOMs are determined by implementing IFPA. The proposed hybrid VSI-FPA approach is applied to solve the DSTATCOM allocation problem in standard IEEE 33-bus radial distribution systems (RDS). The effectiveness of the proposed approach is compared with the similar types of heuristic approaches in the literature. The comparative results shown that the IFPA is outperformed than other algorithms by providing minimum losses, reduced installation cost, and consequently improved voltage profile as well as voltage stability irrespective of EV load growth via allocating the CBs/DSTATCOM optimally in the RDS. The basic network profited from CBs and DSTATCOMs by a loss reduction of 34.71% and 29.50%, respectively, according to the results. The loss, on the other hand, increases to 88.69 percent when the network is filled with 50% EV load. With CBs and DSTATCOMs, however, the higher losses are just 37.07% and 37.26%, respectively. [ABSTRACT FROM AUTHOR]