Abstract
It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system loss is taken as single-objective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm.
Highlights
Energy can be known as a key factor for the social and economic growth
Most global energy is supplied from fossil fuels such as oil, coal and natural gas that are used in most worldwide power stations
In this way, during the last years there has been a trend to cover the increase in electrical load demand by integrating renewable energy sources (RES) in distribution networks as these sources are clean and more sustainable compared to fossil fuels [8]–[11]
Summary
Energy can be known as a key factor for the social and economic growth. Most global energy is supplied from fossil fuels such as oil, coal and natural gas that are used in most worldwide power stations. In [33], [34], Genetic algorithm (GA) has been applied for determining the optimal sizing of BES and PV in distribution network with the aim of minimizing the cost of energy generated, voltage deviation and system loss. Grey Wolf Optimizer algorithm (GWO) has been used for determining the sizing of battery with PV and WT for minimizing annual energy loss in [39], and for obtaining the size of batteries in RDS for minimizing the annual cost of the system in [40]. The optimal sizing of inverter with WT and electric vehicle using GWO algorithm for minimizing system energy loss has been presented in [43]. The optimal allocation of inverter-based PV, WT, and BESS is obtained using the stochastic nature of WT and PV generation with variable load demand.
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