arXiv subjects

Germán Morales-España

Publications and source records attributed to Germán Morales-España.

7 recordsLinked to original sources

arXiv2026

Tight Formulations for Unit Commitment with Different Levels of Details -- Part I: Models and Theoretical Insights

The unit commitment (UC) problem is paramount for optimal operation of power systems, but it faces computational limitations in large-scale settings, especially in investment or stochastic models, because of the binary variables that it contains. A lot of research has attempted to improve the computational performance of UC models, either by reducing model size, resulting in lower fidelity and accuracy, or by improving the tightness of the formulation. Tightness and model size are the best a priori indicators of the computational performance of UC models, but there is no clear overview of what the best formulation is for different generators. In this research, we define models with different levels of detail, and present a formulation for each level that is based on the convex hull. We show new proofs on the tightness of well-known formulations for ramping, for start-up and shut-down costs and capabilities, and for UC with investment. These models, with a different level of detail, can be incorporated into large-scale problems to reduce the computational burden, as demonstrated in Part II.

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arXiv2025

Tight MIP Formulations for Optimal Operation and Investment of Storage Including Reserves

Fast and accurate large-scale energy system models are needed to investigate the potential of storage to complement the fluctuating energy production of renewable energy systems. However, standard Mixed-Integer Programming (MIP) models that describe optimal investment and operation of these storage units, including the optional capacity to provide up/down reserves, do not scale well. To improve scalability, the integrality constraints are often relaxed, resulting in Linear Programming (LP) relaxations that allow simultaneous charging and discharging, while this is not feasible in practice. To address this, we derive the convex hull of the solutions for the optimal operation of storage for one time period, as well as for problems including investments and reserves, guaranteeing that no tighter MIP formulation or better LP approximation exists for one time period. When incorporating this convex hull into a multi-period formulation and including it in large-scale energy system models, the improved LP relaxations can better prevent simultaneous charging and discharging, and the tighter MIP could positively affect the solving time. We demonstrate this with illustrative case studies of a unit commitment problem and a transmission expansion planning problem.

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arXiv2025

Vintage-Based Formulations in Multi-Year Investment Modelling for Energy Systems

This paper reviews two established formulations for modelling multi-year energy investments: the simple method, which aggregates all capacity regardless of commissioning year, and the vintage method, which explicitly tracks investments by year to capture differences in technical parameters over time. While the vintage method improves modelling fidelity, it significantly increases model size. To address this, we propose a novel compact formulation that maintains the ability to represent year-specific characteristics while reducing the dimensionality of the model. The proposed compact formulation is implemented in the open-source model TulipaEnergyModel.jl and offers a tractable alternative for detailed long-term energy system planning.

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arXiv2023

TulipaProfileFitting.jl: A Julia package for fitting renewable energy time series profiles

This paper introduces the TulipaProfileFitting.jl package, a tool developed in Julia for generating renewable energy profiles that fit a specified capacity factor. It addresses the limitations of naive methods in adjusting existing profiles to match improved technology efficiency, particularly in scenarios lacking detailed weather data or technology specifications. By formulating the problem mathematically, the package provides a computationally efficient solution for creating realistic renewable energy profiles based on existing data. It ensures that the adjusted profiles realistically reflect the improvements in technology efficiency, making it an essential tool for energy modellers in analyzing $CO_2$-neutral energy systems.

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arXiv2023

Impact of large-scale hydrogen electrification and retrofitting of natural gas infrastructure on the European power system

In this paper, we aim to analyse the impact of hydrogen production decarbonisation and electrification scenarios on the infrastructure development, generation mix, $CO_{2}$ emissions, and system costs of the European power system, considering the retrofit of the natural gas infrastructure. We define a reference scenario for the European power system in 2050 and use scenario variants to obtain additional insights by breaking down the effects of different assumptions. The scenarios were analysed using the European electricity market model COMPETES, including a proposed formulation to consider retrofitting existing natural gas networks to transport hydrogen instead of methane. According to the results, 60% of the EU's hydrogen demand is electrified, and approximately 30% of the total electricity demand will be to cover that hydrogen demand. The primary source of this electricity would be non-polluting technologies. Moreover, hydrogen flexibility significantly increases variable renewable energy investment and production, and reduces $CO_{2}$ emissions. In contrast, relying on only electricity transmission increases costs and $CO_{2}$ emissions, emphasising the importance of investing in an $H_{2}$ network through retrofitting or new pipelines. In conclusion, this paper shows that electrifying hydrogen is necessary and cost-effective to achieve the EU's objective of reducing long-term emissions.

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arXiv2023

Tulipa Energy Model: Mathematical Formulation

Tulipa Energy Model aims to optimise the investment and operation of the electricity market, considering its coupling with other sectors, such as hydrogen and heat, that can also be electrified. The problem is analysed from the perspective of a central planner who determines the expansion plan that is most beneficial for the system as a whole, either by maximising social welfare or by minimising total costs. The formulation provides a general description of the objective function and constraints in the optimisation model based on the concept of energy assets representing any element in the model. The model uses subsets and specific methods to determine the constraints that apply to a particular technology or network, allowing more flexibility in the code to consider new technologies and constraints with different levels of detail in the future.

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arXiv2019

Power-Based Generation Expansion Planning for Flexibility Requirements

Flexibility requirements are becoming more relevant in power system planning due to the integration of variable Renewable Energy Sources (vRES). In order to consider these requirements Generation Expansion Planning (GEP) models have recently incorporated Unit Commitment (UC) constraints, using traditional energy-based formulations. However, recent studies have shown that energy-based UC formulations overestimate the actual flexibility of the system. Instead, power-based UC models overcome these problems by correctly modeling ramping constraints and operating reserves. This paper proposes a power-based GEP-UC model that improves the existing models. The proposed model optimizes investment decisions on vRES, Energy Storage Systems (ESS), and thermal technologies. In addition, it includes real-time flexibility requirements, and the flexibility provided by ESS, as well as other UC constraints, e.g., minimum up/down times, startup and shutdown power trajectories, network constraints. The results show that power-based model uses the installed investments more effectively than the energy-based models because it more accurately represents flexibility capabilities and system requirements. For instance, the power-based model obtains less investment (6-12%) and yet it uses more efficiently this investment because operating cost is also lower (2-8%) in a real-time validation. We also propose a semi-relaxed power-based GEP-UC model, which is at least 10 times faster than its full-integer version and without significantly losing accuracy in the results (less than 0.2% error).

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