Abstarct: Abstract In this thesis, a new flexible load-reliant cost-driven frxamework is propsoed for peer-to-peer decentralized energy trading of an industrial town. In this frxamework, demand response mechanism is modeled for all active consumers (hereinafter referred to as prosumers). In this industrial town, prosumers employ multiple photovoltaic panels, battery storage systems, and hydrogen storage systems to supply their energy consumption, and sell excess energy to other prosumers in the industrial town. The model is designed with three main objectives: (i) cost of energy trading within the industrial town; (ii) cost of energy trading between the industrial town and neighboring microgrids; and, (iii) cost of energy trading with the upstream grid. Minimizing energy exchange costs within the industrial town is achieved by optimizing the timing of charging and discharging storage systems to reduce internal costs. Minimizing energy exchange costs with neighboring microgrids aims to reduce the cost of energy exchanges between the industrial town and neighboring microgrids. Additionally, minimizing the cost of purchasing energy from the upstream network is intended to reduce the need to purchase energy from the upstream network. The proposed model includes various constraints, including voltage and feeder capacity constraints, power balance of consumption and production constraints, energy storage capacity constraints requested by prosumers, charging and discharging restrictions of the B&HESSs, logical constraints for determining the buyer or seller status, and flexible load constraints. This model is formulated as a non-convex mixed-binary linear optimization problem and solved by using a linear programming solver. The results evaluated by simulating on the IEEE 33-bus standard distribution network. The simulation indicates that the proposed model can significantly reduce energy exchange costs and improve energy efficiency. This model also has the potential to be extended to other industrial towns and microgrids and can be used as an efficient solution for optimal energy management on a larger scale.