Dynamic optimization of natural gas networks under customer demand uncertainties

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty. A planning strategy for natural gas transmission networks under future demand uncertainty is addressed, which involves coupling of a gas transmission network dynamic simulator with the stochastic optimization framework. Loads from a gas-fired power plant are studied where the loads are characterized by a number of uncertain parameters, and unscented transform is utilized for uncertainty propagation. This algorithm is compared to deterministic approaches in terms of energy consumption, supply flow rate and line-pack manipulations by means of an illustrative example where 1% increase in energy requirement is observed for stochastic formulation compared to expected condition while 10% extra energy is required for the worst case scenario. It is also shown that preparation for uncertain future loads can be done with appropriate management of the available compression units, without further natural gas supplies. Stochastic programming provides an optimal way to determine the minimum pressure buffer required at delivery points for safe operation of the network under customer demand uncertainties. (C) 2017 Elsevier Ltd. All rights reserved.