Tabu Search Optimization for Energy Management in Microgrids: A Solution to Grid-Connected and Standalone Operation Modes

As a means of supplying communities and facilities with dependable and sustainable energy in both grid-connected and standalone operation modes, microgrids are gaining importance. The intricate interconnections among renewable energy sources, energy storage systems, and loads, as well as the uncertainties and fluctuation of energy supply and demand, make it difficult to optimize the energy management of microgrids. Using the Tabu Search algorithm, a metaheuristic optimization method that can effectively search the solution space and avoid becoming stuck in local optima, this paper offers a novel approach to energy management in microgrids Under a variety of restrictions on the energy balance, storage capacity, and system stability, we define the energy management problem as a mixed-integer nonlinear programming model with the aim of reducing the total cost of energy consumption and maximizing the usage of renewables. Therefore, applying the Tabu Search method to this model shows that it is effective at locating excellent solutions quickly. The findings demonstrate that the proposed Tabu Search-based energy management system performs better in terms of solution quality, convergence speed, and robustness than alternative optimization strategies. The main assessment is that the Tabu Search algorithm's performance in grid-connected and standalone operating modes as well as its sensitivity to various parameters. This work paves the way for future study in this area and aids in the creation of sophisticated, sustainable energy management systems for microgrids.

Automated summary

By using the Tabu Search algorithm, this study proposes a novel approach to energy management in microgrids. It aims to reduce the total cost of energy consumption and maximize the utilization of renewable energy sources. The findings show that the Tabu Search algorithm performs better than alternative optimization strategies in terms of solution quality, convergence speed, and robustness.