A simple dynamic optimization-based approach for sizing thermal energy storage using process data

Abstract

Thermal energy storage (TES) can increase waste heat utilization in district heating (DH) by storing excess energy to be used later to compensate for energy deficit. When sizing a TES tank for DH, incorporating operational conditions can prevent suboptimal volumes and improve the utility enabled by the TES. However, the different time scales of the payback period for the tank and DH operation poses a challenge for optimizing the tank volume. We propose a method to optimally design a TES tank considering operational conditions of a DH plant using time varying waste heat. We formulate a multi-objective dynamic optimization model based on heat data for a long period, which is solved with a two-step approach. First, the data is screened by solving the model for short-term periods to detect intervals that allow for peak heating savings. Then, the model is re-solved using all selected intervals to determine an optimal tank volume. We conduct a trade-off analysis of the conflicting objectives, energy-saving and costs. The proposed method is demonstrated on a case study with historical data. Our method can explore the full feasible space of TES tank volumes and efficiently provide a trade-off curve without the need of exhaustive search.

A simple dynamic optimization-based approach for sizing thermal energy storage using process data