@Article{ee.2026.083940,
AUTHOR = {Sri Suresh Mavuri, Surender Reddy Salkuti},
TITLE = {Determination of Optimal Location and Sizing of Distributed Generators in Multi-Microgrid System Using Spider Wasps Optimization Technique},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/27524},
ISSN = {1546-0118},
ABSTRACT = {The operational flexibility and reliability of Multi-Microgrid (MMG) networks have been greatly increased by the growing adoption of Distributed Generation (DG) within the operational framework of the contemporary power infrastructure. Optimal location and size of DG units are, however, significant issues since the problem is complex, nonlinear, and multi-objective. A proposed study will develop a sophisticated optimization model using the Spider Wasp Optimization (SWO) algorithm to optimally allocate and size DGs in MMG systems. The goal of the proposed approach is to reduce essential economic goals, such as total operating cost, Levelized Cost of Energy (LCOE), and Total Present Worth (TPW), and meet the system requirements, such as power balance, voltage limits, and generation capacity. The study aims at examining the DG sizing and location in Multi-Microgrid system considering the uncertainties in load and power generation This is possible because a decrease in the Total Present Worth (TPW) of 434.25 million to 404.52 million and the Levelized Cost of Energy (LCOE) is narrowed to 0.174/kWh to use the Spider Wasps Optimization (SWO) algorithm to determine the optimal of the DG sizes and locations. This optimization method considers the fluctuation in the energy demand, the generation information, and the dynamic energy prices. The effectiveness of the suggested methodology can be explained by the fact that its results are compared to the outcomes of the implementation of the algorithms of the Grey Wolf Optimization (GWO) and Cyclonic Convergent Particle Swarm Optimization (CCPSO). The Spider Wasps Optimization (SWO) algorithm demonstrates better results in lower TPW, system size, as well as minimization of LCOE, besides converging faster than GWO, CCPSO, and PSO algorithms, which translates to an accurate and reliable algorithm.},
DOI = {10.32604/ee.2026.083940}
}