Optimization Framework Based on Information Gap Decision Theory for Optimal Operation of Multi-energy Systems

Abstract

Uncertainty has been always one of the challenging issues in power systems. To handle uncertainty, different solutions have been presented such as forecasting technologies. Although forecasting methods have been used to predict parameters with uncertain behavior, forecasts may not be always true; therefore uncertainty modeling is necessary. Uncertainty and its relevant impacts can be analyzed within various energy systems like hub energy systems or so-called multi-energy systems. Hub energy systems containing renewable energy resources should be scheduled to have safe operation under uncertainties of different parameters. In this chapter, by using information gap decision theory (IGDT), a risk-based optimization framework is proposed for optimal operation of hub energy system with considering net price uncertainty. IGDT benefits from two immunity functions determine appropriate operational strategies for robust and optimistic operation of hub energy system against uncertain behavior of net price: robustness and opportunity functions. A mixed-integer nonlinear programming is employed to model robustness and opportunity functions of IGDT. Also, a sample grid-connected hub system is analyzed, and the results are presented to validate the effectiveness of proposed approach. According to the results in the robustness function, hub energy system has become robust against 24.6% more price, while total operation cost of system has been increased 2.8%. Also, hub system has gained 75 $ economic benefit due to the reduction of price in the opportunity function.