Summary:
In this paper, we propose a new methodology to formulate storage behavior in medium- and long-term power system models that use a load duration curve. Traditionally in such models, the chronological information among individual hours is lost; information that is necessary to adequately model the operation of a storage facility. Therefore, these models are not fully capable of optimizing the actual operation of storage units, and often use pre-determined data or some sort of peak-shaving algorithm. In a rapidly changing power system, the proper characterization of storage behavior and its optimization becomes an increasingly important issue. This paper proposes a methodology to tackle the shortcomings of existing models. In particular, we employ the so-called system states framework to recover some of the chronological information within the load duration curve. This allows us to introduce a novel formulation for storage in a system states model. In a case study, we show that our method can lead to computational time reductions of over 90% while accurately replicating hourly behavior of storage levels.
Keywords: Demand blocks, power system models, renewable integration, storage, system states.
JCR Impact Factor and WoS quartile: 5,680 - Q1 (2016); 6,500 - Q1 (2023)
DOI reference:
https://doi.org/10.1109/TPWRS.2015.2471099
Published on paper: July 2016.
Published on-line: September 2015.
Citation:
S. Wogrin, D. Galbally, J. Reneses, Optimizing storage operations in medium- and long-term power system models. IEEE Transactions on Power Systems. Vol. 31, nº. 4, pp. 3129 - 3138, July 2016. [Online: September 2015]
