Derived Self Generative Roulette Wheel Memetic Optimization Based Bidding Strategy for Micro-grid Interfaced Renewable Energy Management
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 2 |
| Year of Publication : 2026 |
| Authors : K. Swapna, D. Godwin Immanuel |
10.14445/23488549/IJECE-V13I2P108
How to Cite?
K. Swapna, D. Godwin Immanuel, "Derived Self Generative Roulette Wheel Memetic Optimization Based Bidding Strategy for Micro-grid Interfaced Renewable Energy Management," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 2, pp. 103-114, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P108
Abstract:
Renewable Energy Sources (RES) are an alternative one for addressing growing energy demand to diminish climate change while promoting sustainable expansion. In the microgrids, energy management is defined as the statistical info and control system that provides the topologies needed to guarantee the distribution and generation systems’ electricity at the least possible operational costs. But, a technical challenge for the operation of MGs is how to find the optimal method to manage various Distributed Renewable Energy Resources (DRERs) and loads. To deal with these issues, the Derived Self Generative Roulette Wheel Memetic Optimization based Bidding Strategy (DSGRWMOBS) Model is introduced for price takers and customers with Micro-grid interfaced RES at negligible computational expenses. In the DSGRWMOBS Model, the system data, like generator data, consumer data, aggregate load, and price, are collected. The collected data are considered the initial population. After that, the memetic process variables population size, crossover rate, and mutation rate are initialized. Then, the DSGRWMOBS Model creates the initial random population of bidding coefficient rivals. Then, market-clearing prices are evaluated using the bidding coefficients. The fitness value of each population rate is determined based on generation, demand, and a dispatch schedule that meets reliability constraints to maximize profit. After determining the fitness function, the DSGRWMOBS Model performs the selection, crossover, and mutation for Micro-grid interfaced RES to achieve the ideal solution (system data with higher profit). In this way, the DSGRWMOBS Model helps the Micro-grid interlinked RES at negligible computational expenses. The simulation result shows the performance enhancement of the DSGRWMOBS Model in terms of metrics such as operation cost, energy management precision, and solar Energy. The results authenticate that the DSGRWMOBS Model obtains healthier performance by means of reaching advanced energy management accuracies and solar Energy with negligible operation cost.
Keywords:
Renewable energies, Energy management, Micro-Grid, Market clearing prices, Bidding coefficients.
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