The research developed a non-linear programming (NLP) model in Python for the Industrial Multi-Energy System (IMES) to implement Peer-to-Peer (P2P) trading for electricity and thermal energy in a hybrid market model. The model proposes that the grand coalition of all peers working together in the scheme is the optimal trading coalition. The distributed single-buyer model with grid integration is proposed for the electricity market, and a fully decentralised market for the thermal energy market. The net-metering scheme is implemented for the P2P trade of electricity. The Alternate Direction Methods of Multipliers (ADMM) algorithm is implemented for the P2P trade of thermal energy to clear the market and optimise trading decisions under constraints such as storage and network load. All peers involved in the P2P trading scheme reduced their overall total annual costs (TAC) by an average of 25.20 %. Peer A achieved an overall cost reduction of 28.91 %. Peer B and C achieved overall cost reductions of 15.34 % and 31.43 %. The average energy cost savings across all peers are 45.80 % for electricity and 92.20 % for thermal energy. The higher cost savings for thermal energy can be attributed to the fully decentralised network, enabling the full implementation of the ADMM algorithm that optimises the trading decision.
