Chance-constrained optimization for multi-energy hub system with dynamic thermal rating

Abstract

The energy hub is an essential concept for coordinating multiple distributed energy resources at the local level in future sustainable networks. However, the increasing penetration of renewable generations bring severe uncertainty to the energy hub modelling and dispatch. This paper resolves the optimal operation of energy hubs with notable amount of uncertain renewable energy resources, aiming to reduce the network investment by exploiting the potential of dynamic thermal rating (DTR) methodology for interconnection branches. It will first model the generation and demand uncertainties within the energy hubs that are weather dependent (i.e. wind, solar, temperature). The DTR model is developed based on real-time weather measurement across a year. Then, a chance-constrained optimal power flow is proposed to fully consider both the positive and negative impact of weather uncertainties on the energy transmission between adjacent energy hubs. The problem is formulated as a multi-period stochastic problem with the objective to minimize the total generation cost of an interconnected energy hub system. The proposed stochastic problem is transformed into an equivalent deterministic problem with the chance constraint translating to deterministic constraint by adopting the Cornish-Fisher expansion method. The developed model is extensively illustrated on a three-hub system with the interior-point method, and it also demonstrates that the proposed method has the ability to significantly improve the efficiency for multi energy integration at the lowest costs.