Optimal scheduling and management of grid-connected distributed resources using improved decomposition-based many-objective evolutionary algorithm

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi-objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition-based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable-based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE-33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. The allocation of distributed resources is a multi-objective mixed integer non-linear problem. It poses a challenge to the existing multi-objective evolutionary algorithms due to their small feasible search space and constraints in both objective and control variables space. To address this lacuna, this paper proposes an improved decomposition-based evolutionary algorithm (I-DBEA) employed with the epsilon constraint method to handle constraints and solve highly complex multi-objective problems. image