the conventional energy grid is being replaced with the new emerging smart grid infras- tructure. this can be attributed to the fact that it only supports unidirectional energy ow, i.e., energy is transmitted from the producer to the consumer. smart grid addresses issues such as grid reliability, blackouts, global warming, etc, by implementing various renewable energy sources readily available for consumer use. the clean electric power can be produced from local neighbourhoods, individual houses, to large industrial businesses. therefore, with the im- plementation of alternative energy sources readily available, users connected to the smart grid can purchase electric power, enabling groups and individuals to generate a profitable income. however, challenges persist attributed to user cost, and power management, resulting in active work to investigate optimization techniques between users in p2p energy trading to enhance the performance of how users trade energy among each other. among the various energy trading mechanisms, auction-based models have demonstrated excellent performance, targetting desir- able properties for p2p energy trading. in this work, we present three different auction-based models that can be utilized for practical energy trading. the prosumers (producers and con- sumers) of energy, play the role as sellers or buyers depending on the current supply and demand. sellers with renewable energy sources participate to sell their excess of energy to generate a profit and satisfy the buyers' demand. we model the interaction with as single-sided and double-sided auctions, explicitly taking the dynamic nature of both the sellers and buyers into account. we further propose a profit maximization algorithm that considers power line cost, transmission capacity, and energy distribution. with theoretical analysis and simulations, we demonstrate that the proposed auctions are individually rational, truthful, computationally efficient, and budget-balanced.