Optimizing integrated energy systems with a robust MISOCP model and C&CG algorithm for enhanced grid efficiency and profitability

With the increasing integration of renewable energy sources (RES), and energy storage systems (ESS), energy management has become a critical issue. Grid owners are already considering systems such as power-to-gas (P2G), which allow excess electricity from renewables or batteries to be converted to gas and sold back to the gas grid. The presence of distributed gas production systems and P2G leads to interaction between electricity and gas networks. This turns energy management and profit optimization into a complex task that requires a combined approach. This study examines the optimization of integrated energy systems to find the efficient solutions. Considering the uncertainty in renewable energy production and energy prices, a robust min-max-min approach has been adopted. Demand-side management (DSM) is also critical for optimal operation of the power grid. It significantly reducing peak loads and increasing profits. This paper presents a two-stage mixed integer second order cone programming (MISOCP) model for the joint optimization of electricity and gas networks, including distributed generation (DG), P2G systems, ESS, electric vehicles (EVs), and DSM. To address the computational challenges, this study uses the column generation and constraint (C&CG) algorithm. The proposed model is tested on a distribution grid of IEEE-33 bus system and shows its effectiveness in different scenarios. Key findings include: 1) DSM successfully reduces grid peak loads, 2) renewables and batteries enable the conversion of excess electricity into gas for sale in the marketplace, and 3) DG buys energy from the upstream grid resulting in a 21 % cost reduction.