(672h) A Clustering Decomposition Algorithm for Energy Storage Design & Operation

Intermittent availability of solar and wind energy is a challenge for renewable power systems because they are often asynchronous with consumer demand. Increasing renewable penetration requires energy storage technologies to align varying supply with demand. Moreover, energy storage options give greater control and flexibility to grid operators by making solar and wind more dispatchable power sources.

Optimization-based design & scheduling models aim to minimize the power and storage capacities of renewable power systems to lower capital and operational costs [1]. Hourly time discretization in the schedule is often used to capture solar and wind dynamics, keep track of storage inventory levels, and model time-dependent operational decisions. As a result, large time horizons are characteristic of these problems and significantly increase the computational burden of solving them [2, 3]. In addition, multiple time series data coming from resource availability, demand loads, and prices grow the complexity as well.

In this work, a decomposition algorithm based on agglomerative hierarchical clustering (AHC) is proposed to alleviate the computational burden, where the optimization is performed over representative time periods. A key advantage for AHC compared to the popular K-means clustering approach is that the clusters maintain time chronology, which is important for analyzing inter-period energy storage [4]. An example case study on dense energy carriers for energy storage, performed in collaboration with Shell, is presented to demonstrate the algorithm’s applicability.

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[2] Harjunkoski, Iiro, Christos T. Maravelias, Peter Bongers, Pedro M. Castro, Sebastian Engell, Ignacio E. Grossmann, John Hooker, Carlos Méndez, Guido Sand, and John Wassick. "Scope for industrial applications of production scheduling models and solution methods." Computers & Chemical Engineering 62 (2014): 161-193.

[3] Castro, Pedro M., Ignacio E. Grossmann, and Qi Zhang. "Expanding scope and computational challenges in process scheduling." Computers & Chemical Engineering 114 (2018): 14-42.

[4] Pineda, Salvador, and Juan M. Morales. "Chronological time-period clustering for optimal capacity expansion planning with storage." IEEE Transactions on Power Systems 33, no. 6 (2018): 7162-7170.