Network management and optimization play a crucial role in integrating renewable energy sources on the distribution network. This paper presents the optimization of the grid‐connected photovoltaic/small hydropower hybrid power system using a hybrid particle swarm optimization‐whale optimization algorithm. The exploration strength of the particle swarm optimization is hybridized with the exploitation capability of the whale optimization algorithm for improved convergence, speed, and optimal network performance. The objective function is to minimize the feeder voltage deviation and power loss. The effectiveness of the proposed approach is verified on the standard 118—bus distribution system. The simulation is carried out with step changes in irradiation and loading conditions. The optimization result from the two hybridized algorithms is compared to its standalone form, simulated under the same conditions. The results show that the proposed method is superior in the optimal solution and convergence property. The results confirm the technique's contribution to improved network management and grid‐connected hybrid renewable power systems. It significantly reduced the voltage deviation by 62.58% on multiple locations of renewable sources. The voltage is achieved within the statutory range at all hours of the daily simulation. The technique is envisaged to be very useful in distribution network operators.
Farmers are faced with challenges of producing enough food and the use of traditional methods seems not to keep pace with the ever-growing demand of the populace thus creating increased concern in food scarcity. Although it has been identified that smart tools will enhance the production pace needed in the Agricultural sector, unfortunately, most of these tools are designed for farmers without their inputs, thus creating tools that are not meeting demands. This study focused on a farmer-centered design, development, and deployment approach to improving farm productivity. The design thinking approach was used to identify the specific need of the farmers in selected areas, ideas were created using brainstorming sessions involving experts in the field, and prototypes were developed and deployed to evaluate the impact performance. The result shows that the proposed system improved the cost-benefit ratio of crop farming from 2.14 to 2.26. This is a 12% productivity increase.
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