Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.

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.

This paper presents a multi-objective optimization framework for the optimal integration of Renewable Energy Sources (RES) into Distribution Power Systems (DPS), aiming to simultaneously maximize RES utilization, minimize operational costs, and reduce greenhouse gas emissions. To address the complexity and nonlinearity of the problem, an improved Artificial Bee Colony (ABC) algorithm is proposed, incorporating a refined search strategy that effectively balances global exploration and local exploitation. The optimization model considers practical constraints such as generation capacity limits, voltage regulations, and power balance requirements. Extensive simulations on the IEEE 33-bus and 69-bus benchmark systems demonstrate that the proposed method outperforms conventional metaheuristic algorithms, achieving an average of 26.7% reduction in power losses, 22.5% decrease in CO2 emissions, and 57.9% DG penetration. These results underscore the proposed method's robustness and practical applicability for sustainable and intelligent energy planning in modern distribution networks.

This paper presents a multi-objective optimization approach for optimal Distributed Generation (DG) placement and sizing, optimizing power loss reduction, cost efficiency, voltage stability, and Renewable Energy Source (RES) absorption. The Gray Wolf Optimizer (GWO) was chosen for its strong global search, fast convergence, and ability to avoid local optima. Simulations on IEEE 33-bus and IEEE 69-bus systems compared GWO against the Cuckoo Search Algorithm (CSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm (GA). The results showed that GWO achieved the least power loss and highest RES absorption, enhancing efficiency, stability, and sustainability. This study demonstrates the effectiveness of nature-inspired optimization in DG planning and RES integration.

This paper proposes a method for optimizing the placement and capacity of Distributed Generators (DGs) in distribution systems based on Deep Reinforcement Learning (DRL). The objective of the method is to minimize power losses, investment costs, voltage deviations, and CO2 emissions while ensuring strict compliance with system operating constraints. The proposed approach leverages the robust capabilities of DRL to handle nonlinear and complex-constrained problems, making it highly adaptable to various operational scenarios. Experimental results on standard distribution systems demonstrate that the proposed method outperforms traditional algorithms, significantly improving operational efficiency and enhancing the integration of renewable energy sources. This contributes to the development of smart grid systems and promotes sustainable energy solutions.