Optimal PV siting considering carbon emission flow

As global efforts to achieve carbon neutrality intensify, optimizing the siting of photovoltaic (PV) systems to minimize powe r grid carbon emissions becomes critical. This paper proposes a novel PV siting framework based on Carbon Emission Flow (CEF) theory, aiming to establish an efficient carbon flow connection between potential PV locations and regional loads. By leveraging the Particle Swarm Optimization (PSO) algorithm, the paper addresses the nonlinear complexity of node selection in seeking optimal solutions that balance new energy consumption and grid operational economy. This paper models the carbon emission flow within diverse functional regions such as work, shopping, and residential areas. Results show that nodes in work areas and shopping areas exhibit higher selection priority due to efficient local consumption and reduced transmissio n losses. In contrast, residential areas, characterized by spatiotemporal mismatch between evening load peaks and daytime PV output, show lower priority without energy storage support. Case Studies on the IEEE 39-node system validate that the proposed framework effectively identifies optimal nodes, reducing total carbon emissions by prioritizing sites with high load-PV matching or stable load characteristics. This paper highlights the importance of spatiotemporal matching optimization in PV siting, providing a critical pathway to enhance grid efficiency and advance carbon neutrality goals through strategic node sites.