Swarm‐based mean‐variance mapping optimization for optimal placement of energy storage with synthetic inertia control on a low inertia power grid

With the widespread application of drone technology, energy management of drone swarms has become a key challenge in ensuring sustained and efficient operations. To optimize the energy consumption of drone swarms, researchers have proposed the energy balance plane routing algorithm. This algorithm uses ultraviolet light for low visibility communication and has shown promising results in managing the energy of drone swarms. To address the issues of uneven cluster head distribution and reduced energy consumption in other algorithms, an improved non-uniform clustering energy balancing routing algorithm is proposed. Compared to existing algorithms, the improved non-uniform clustering energy balancing routing algorithm achieved the lowest average communication consumption among 5 nodes and prolonged node failure time. The performance of the research method has been verified through simulation experiments, which is of great significance in maintaining energy consumption balance and can improve the energy efficiency and sustainability of the network. This study can provide more effective solutions for the development and application of drone technology, promoting its widespread application and promotion in various fields.

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Parallel Social Group Optimization (Psgo): Performance and Its Application to Routing in Wmn’s

Sharma

Singh

2022

Preprint

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This paper proposes a new multi-population-based social optimization technique called Parallel Social Group Optimization (PSGO). The algorithm is inspired by the learning behaviour of humans in different groups. In this algorithm, we consider the complete process of human interaction within the group and outside the group. We implement the proposed algorithm in MATLAB and tested it on 30 standard benchmark functions. For performance analysis purposes, we compare the PSGO algorithm with other recent 16 algorithms. The PSGO algorithm outperformed the other 16 algorithms on 4 standard benchmark functions. None of the other algorithms could match this performance. In addition, for functions f1,f2, f3,f4,f5, and f7of standard benchmark functions it produced the best performance but this performance was equalled by a few other algorithms also. Further, we propose a PSGO-based dynamic route evaluation approach for Wireless Mesh Networks (WMNs). We implemented the PSGO-based routing approach in MATLAB and compared it with 9 soft computing and hard computing-based approaches namely AODV, DSR, ACO, BBO, BAT, Firefly, BBBC, GA, and SGO. On over 1500 dynamic node network situations, the PSGO-based routing technique outscored all other 9 algorithms.

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Swarm‐based mean‐variance mapping optimization for optimal placement of energy storage with synthetic inertia control on a low inertia power grid

Cited by 3 publications

References 40 publications

Flexibility of variable-speed pumped-storage unit during primary frequency control and corresponding assessment method

Flexibility of variable-speed pumped-storage unit during primary frequency control and corresponding assessment method

Application of EEUC-based inter aircraft ultraviolet communication network algorithm in energy consumption optimization of drone swarm

Parallel Social Group Optimization (Psgo): Performance and Its Application to Routing in Wmn’s

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