Multi-Objective Optimization of Emissions and Green Accounting Costs in Smart Distribution Networks with Distributed Generation

Abstract

This paper presents a multi-objective optimization framework for Distributed Generation (DG) planning in smart Distribution Networks (DNs), with the aim of minimizing carbon dioxide emissions (CO2) and Green Accounting Costs (GAC). A hybrid model is formulated to incorporate power flow constraints, lifecycle emission factors, and environmental cost components under a green accounting perspective. To solve the proposed problem, an enhanced Grey Wolf Optimizer (GWO), termed Eco-Aware Multi-Objective Grey Wolf Optimizer (EMOGWO), is developed by introducing environmentally guided leader selection, adaptive convergence control, and a fuzzy-based decision mechanism. The algorithm is tested on the IEEE 33-bus and IEEE 69-bus systems and benchmarked against Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Arithmetic Optimization Algorithm (AOA), and the standard GWO. Simulation results demonstrate that EMOGWO consistently achieves the lowest power loss, emissions, and environmental cost across all cases, while providing the highest Emission Reduction Ratio (ERR) and Green Cost Savings (GCS). The proposed approach offers a practical and effective tool for sustainable DG planning aligned with environmental and economic objectives.