The P2P-based optimization framework for interconnected natural gas and electricity networks considering the electrical distance and both grids' constraints

The role of gas-fired power generators in capturing the intermittency of renewable energy resources is crucial. Also, sustaining the natural gas network performance while transferring the unpredictability of renewable energy resources to the natural gas grid caused the integrated operation of power and natural gas grids to be essential in future smart grids. On the other hand, current studies that have examined the combined operation of electricity and natural gas systems have mainly focused on the operational constraints of both networks, and a few considered market-based frameworks such as the peer-to-peer (P2P) energy trading method for the integrated operation of both grids. Employing the P2P energy trading market framework can improve the integration of different distributed energy resources (DERs), such as gas-fired generators with power systems, through its competitive market-clearing price. Also, paradigms such as the P2P energy markets are conceptualized to improve the power system performance (voltage security) by introducing local energy transactions. In this regard, considering the power grid constraints in the P2P energy trading market is crucial. Using the natural gas network to support power grid operation may reduce gas network reliability. Hence, considering natural gas grid limitation (nodes gas pressure) in integrated energy systems operation is fundamental. This paper presents the P2P energy-sharing optimization model for the integrated operation of natural gas and power networks, considering the natural gas network steady-state model, the power grid limitations (the AC power flow), and the distribution power grid’s usage fees. Employing the P2P energy transaction framework facilitates customers' (gas turbines and microgrids) energy trading with each other. The presented P2P optimization is equipped with a Thevenin impedance distance (TID) model to reflect the power distribution usage in customers' energy transactions as a grid usage price. The simulation results reveal that the presented method can considerably reduce the total operating cost, improve customers' social welfare, and enhance both grids' performance.