arXiv subjects

German Morales-Espana

Publications and source records attributed to German Morales-Espana.

4 recordsLinked to original sources

arXiv2025

Hull Clustering with Blended Representative Periods for Energy System Optimization Models

The growing integration of renewable energy sources into power systems requires planning models to account for not only demand variability but also fluctuations in renewable availability during operational periods. Capturing this temporal detail over long planning horizons can be computationally demanding or even intractable. A common approach to address this challenge is to approximate the problem using a reduced set of selected time periods, known as representative periods (RPs). However, using too few RPs can significantly degrade solution quality. In this paper, we propose the method of hull clustering with blended RPs to enhance traditional clustering-based RP approaches in two key ways. First, instead of selecting typical cluster centers (e.g., centroids or medoids) as RPs, our method is based on extreme points, which are more likely to be constraint-binding. Second, it represents base periods as weighted combinations of RPs (e.g., convex or conic blends), approximating the full time horizon more accurately and with fewer RPs. Through two case studies based on data from the European network operators, we demonstrate that hull clustering with blended RPs outperforms traditional RP techniques in both regret and computational efficiency.

math.OC↗

arXiv2025

Debunking the Speed-Fidelity Trade-Off: Speeding-up Large-Scale Energy Models while Keeping Fidelity

Energy system models are essential for planning and supporting the energy transition. However, increasing temporal, spatial, and sectoral resolutions have led to large-scale linear programming (LP) models that are often (over)simplified to remain computationally tractable-frequently at the expense of model fidelity. This paper challenges the common belief that LP formulations cannot be improved without sacrificing their accuracy. Inspired by graph theory, we propose to model energy systems using energy assets (vertices), as a single building-block, and flows to connect between them. This reduces the need for additional components such as nodes and connections. The resulting formulation is more compact, without sacrificing accuracy, and leverages the inherent graph structure of energy systems. To evaluate performance, we implemented and compared four common modelling approaches varying in their use of building blocks and flow representations. We conducted experiments using TulipaEnergyModel.jl and applied them to a multi-sector case study with varying problem sizes. Results show that our single-building-block (1BB-1F) approach reduces variables and constraints by 26 and 35 percentage, respectively, and achieves a 1.27 times average speedup in solving time without any loss in model fidelity. The speedup increases with problem size, making this approach particularly advantageous for large-scale models. Our findings demonstrate that not all LPs are equal in quality and that better reformulations can lead to substantial computational benefits. This paper also aims to raise awareness of model quality considerations in energy system optimisation and promote more efficient formulations without compromising fidelity.

math.OC↗

arXiv2022

Classification, potential role, and modeling of power-to-heat and thermal energy storage in energy systems: A review

Most of the power-to-heat and thermal energy storage technologies are mature and impact the European energy transition. However, detailed models of these technologies are usually very complex, making it challenging to implement them in large-scale energy models, where simplicity, e.g., linearity and appropriate accuracy, are desirable due to computational limitations. In the literature, the main power-to-heat and thermal energy storage technologies across all sectors have not been clearly identified and characterized. Their potential roles have not been fully discussed from the European perspective, and their mathematical modeling equations have not been presented in a compiled form. This paper contributes to the research gap in three main parts. First, it identifies and classifies the major power-to-heat and thermal energy storage technologies that are climate-neutral, efficient, and technologically matured to supplement or substitute the current fossil fuel-based heating. The second part presents the technology readiness levels of the identified technologies and discusses their potential role in a sustainable European energy system. The third part presents the mathematical modeling equations for the technologies in large-scale optimization energy models. We identified electric heat pumps, electric boilers, electric resistance heaters, and hybrid heating systems as the most promising power-to-heat options. We grouped the most promising thermal energy storage technologies under four major categories. Low-temperature electric heat pumps, electric boilers, electric resistance heaters, and sensible and latent heat storage show high technology readiness levels to facilitate a large share of the heat demand. Finally, the mathematical formulations capture the main effects of the identified technologies.

physics.soc-ph↗

arXiv2018

Modelling the Hidden Flexibility of Clustered Unit Commitment

This paper proposes a Clustered Unit Commitment (CUC) formulation to accurately model flexibility requirements such as ramping, reserve, and startup/shutdown constraints. The CUC is commonly applied in large and long-term planning models to approximate the units' operational flexibility in power systems due to its computational advantages. However, the classic CUC intrinsically and hiddenly overestimates the individual unit's flexibility, thus being unable to replicate the result of the individual UC. This paper then present a set of constraints to correctly represent the units' hidden flexibility within the cluster, mainly defined by the individual unit's ramping and startup/shutdown capabilities, including up/down reserves. Different case studies show that the proposed CUC replicates the objective function of the individual UC while solving significantly faster, between 5 to 311 times faster. Therefore, the proposed CUC correctly represents the individual unit's ramping and reserve flexibility within the cluster and could be directly applied to long-term planning models without significantly increasing their computational burden.

math.OC↗