Performance optimization of energy cost and transmission losses in peer-to-peer energy sharing for distribution system

• Local generation combined with peer-to-peer (P2P) energy sharing improves voltage profiles and significantly reduces system losses. • Incorporating the transmission loss component into electricity billing is crucial in P2P transactions. Therefore, Bialek's power tracing method is employed to fairly allocate losses among consumers. • Optimizing only transmission losses improves efficiency but may not lower energy costs, while cost-based optimization can increase losses due to distant sellers. Hence, jointly optimizing both is essential for the best community outcome. The transition toward renewable energy and decentralized generation has encouraged the development of peer-to-peer (P2P) energy sharing models. However, integrating diverse load types and distributed energy sources within such frameworks presents significant challenges for maintaining operational efficiency and grid reliability. The characteristics of connected loads play a critical role in determining system stability, voltage profiles and overall power quality in decentralized energy system. Strategic interventions on both supply and demand side are essential to improve performance. The paper demonstrates how integrating multiple generating sources within the distribution network, along with optimized local energy sharing, improves voltage profiles and reduces transmission losses. This is validated through case studies analyzing different source and load locations and load sharing patterns. Power loss tracing algorithm is used to allocate losses to loads and energy costs are determined based on the energy availability in specific time slots. This study uses Random Optimization method to show that rescheduling energy use during low-price periods and reallocating prosumer energy through nearby P2P transactions can reduce transmission losses and energy cost while meeting local demand. Results offer insights into systems with renewables and loads, suggesting advanced strategies to tackle modern power network complexities.