An optimization model that incorporates all combinatorically feasible inter-plant collaboration networks is developed using P-graph. It has been theoretically proven that time-sliced-based energy planning optimization has positive impacts and is capable of achieving carbon emissions reduction goals and minimizing costs simultaneously. However, as the number of entities increased, an exponential growth in possible combinatorial feasible coalitions is anticipated. Therefore, an extension of the P-graph optimization tool that is capable of generating all possible outcomes in multi-period P2P energy trading – PEP (P-graph for energy planning) is developed. The PEP software can be effectively used in modelling complex process networks graphically and solving optimization problems with the combined advantages of combinatorial algorithms and mathematical programming. In this paper, a systematic framework for implementing P2P energy trading using PEP software is proposed and demonstrated using a real-life case study.