Economic-environmental convex network-constrained decision-making for integrated multi-energy distribution systems under electrified transportation fleets

Considering the high penetration rates of electrified transportation fleets, their impact on the integrated thermal/electrical/natural-gas multi-energy distribution systems (IMEDS) will be distinguishable. This study proposes a hybrid robust-stochastic convex optimization model for optimal scheduling of an IMEDS under the network constraints. Moreover, a high penetration level of smart plug-in hybrid electric vehicle (PHEV) fleets, as well as the renewable energy sources (RES) in the active electricity distribution system (AEDS) is regarded. A multi-objective optimization approach is taken to minimize the operation expenditures and the greenhouse gas emissions. The natural gas network's (NGN) linepack storage capability along with the district heating network's (DHN) thermal flexibility are formulated with the objective to enhance the flexibility and avoid overlapping in the peak demand of various energy forms under severe uncertainty condition (e.g., considering uncertain input parameters such as RES production, electrical loads, natural gas loads, drivers' behaviour. To handle such uncertainty parameters, a scenario-based stochastic programming (SP) method is adopted, combined with robust optimization (RO) ambiguity sets, when dealing with volatilities of the market price. The network-constrained IMEDS was embodied by the standard IEEE 69-bus AEDS, 40-node NGN, 59-bus DHN systems. The proposed convex model is a second-order cone programming (SOCP) model, which was solved by the MOSEK solver. The functionality of the proposed method was confirmed through different case studies.