M.B. Elgersma
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2 records found
1
Conference paper
(2025)
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Maaike B. Elgersma, Germán A. Morales-España, Karen I. Aardal, Mathijs M. de Weerdt
To achieve climate goals by 2050, accurate energy system optimization (MIP) models are needed to help decision-makers make investment plans. To increase accuracy, a high resolution in the temporal and spatial dimensions is needed, as well as many details on the operational capabilities of energy generators. However, this results in large-scale models that do not scale well. Thus, researchers often seek the right trade-off between computational tractability and accuracy. Here, we present a tighter formulation for optimal storage operation and investment problems, including reserves, along with the methodology we used to obtain it, based on the work of [2]. Additionally, we present some preliminary work aiming to provide tight and compact unit commitment models with different levels of detail. These models can be included in large-scale energy system optimization models to increase model accuracy while keeping the models computationally tractable.
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To achieve climate goals by 2050, accurate energy system optimization (MIP) models are needed to help decision-makers make investment plans. To increase accuracy, a high resolution in the temporal and spatial dimensions is needed, as well as many details on the operational capabilities of energy generators. However, this results in large-scale models that do not scale well. Thus, researchers often seek the right trade-off between computational tractability and accuracy. Here, we present a tighter formulation for optimal storage operation and investment problems, including reserves, along with the methodology we used to obtain it, based on the work of [2]. Additionally, we present some preliminary work aiming to provide tight and compact unit commitment models with different levels of detail. These models can be included in large-scale energy system optimization models to increase model accuracy while keeping the models computationally tractable.
Preprint
(2024)
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M.B. Elgersma, M.M. de Weerdt, K.I. Aardal, G.A. Morales España, Niina Helistö, Juha Kiviluoma
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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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.