An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.
Wind is one of the world's most rapidly expanding and environmentally friendly renewable energy sources (RES). The aim of this study is to analyze the wind potential availability in Sanghar and Gwadar cities through the wind characteristics' analysis. To analyze the wind potential at the both sites, oneyear average wind speed data (including annual, monthly, day-night, and seasonal variations) for 2020 were used. A Weibull distribution parametric approach with five different technique was applied. Along with an analysis of the wind potential, an economic assessment was also out to estimate the installation cost, turbinecost, capacity factor of ten different wind turbines with different rated power were used at the selected sites. The results show that at the Sanghar site, the maximum power of 1612.82 kW was generated by Vestas V126/3300 whereas the minimum power 383.44 kW was generated by Nordex n60/1300. At the Gwadar site, Vestas V126/3300 generated the maximum power of 745.10 kW and least power of 157.98 kW was generated by Nordex n60/1300. With respect to an economic assessment, Vestas V126/3300 and Suzlon S66/1250 had the highest and lowest installation cost of turbines respectively at both sites. At the Sanghar site, the lowest value i.e., 0.
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