Stochastic expansion planning of transmission system and energy hubs in the presence of correlated uncertain variables

Abstract

Abstract Energy hubs (EHs), with interconnecting different energy carriers, technologies, and sectors, improve energy efficiency and enhance the flexibility of energy management. EHs can be an alternative to the upgradation of the transmission system infrastructures. Then, it is necessary to evaluate accurately the impact of integrating EHs across the power system on the expansion planning of the transmission system. This paper proposes an expansion planning of the transmission system and integrated EHs across the power system in deterministic and stochastic environments. In addition, the correlation among the uncertain variables may affect the expansion planning and scheduling decisions as well as the resulting costs. Therefore, the stochastic modelling of the proposed planning approach is developed considering the correlation among the uncertain variables. The uncertainties related to the generation of the renewable energy sources (RESs), energy demands, and electrical/thermal/cooling/gas price are addressed with a stochastic scenario‐based scheme. For this purpose, numerous scenarios are generated by implementing the well‐known Monte Carlo simulation (MCS) technique. Then the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. Next, the k‐means method as an efficient scheme of data clustering is used to reduce the initial scenarios to some limited ones.

Keywords

• conventional power plants
• correlation
• district thermal and cooling markets
• energy hub
• renewable energy resources
• TEP