IGDT-Based Robust Optimization for Multicarrier Energy System Management

In this paper, the information-gap decision theory (IGDT)-based robust problem model for multicarrier energy system (MES) management is presented. It is noted that in the proposed problem, electrical and gas energies are inputs of MES or energy hub, and electrical and heating energies are outputs of MES. The output electrical energy of hub provided by renewable energy sources (RESs), storage system, combined heat and power (CHP) system is managed by hub manager, and they do not participate in the market and input electrical energy. Also, it is considered that input electrical energy is bought from the PoolCo and bilateral contract markets. Moreover, the CHP is only connecting element for different energies. Therefore, the deterministic problem model expresses in the first step, where its objective function is minimized of hubs energy cost. Also, the constraints of problem are power flow equations and indexes limit in electrical, natural gas and district heating networks, PoolCo and bilateral contract market models, RES, storage system and CHP equations. This problem is as mixed integer nonlinear programming, where in the next step, the equivalent mixed integer linear programming model is presented because this new model can obtain the global optimal solution with low calculation error and time. In addition, the different loads, energy price in PoolCo market and RESs output power are uncertain parameters. Hence, this paper for considering uncertainty parameters presents the IGDT-based robust optimization for proposed problem. Finally, this problem is applied to standard test network with GAMS software, and thus the capability of the proposed problem investigates.