Modeling optimal redispatch for the European Transmission grid

Linnemann, Christian; Echternacht, David; Breuer, Christopher K.; Moser, Albert

doi:10.1109/ptc.2011.6019442

Cited by 33 publications

(10 citation statements)

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“…In contrast to many existing works [14,15,17,22,23], we extend the power grid model from the extra-high voltage (EHV with U 0 ≥ 220 kV) level with the consideration of the high voltage (HV with U 0 = 110 kV) level. The HV level has only recently gained more attraction in academia, mainly due to a lack of valid datasets.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

Wienholt

Müller

Bartels³

2018

Energies

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The paradigm shift of large power systems to renewable and decentralized generation raises the question of future transmission and flexibility requirements. In this work, the German power system is brought to focus through a power transmission grid model in a high spatial resolution considering the high voltage (110 kV) level. The fundamental questions of location, type, and size of future storage units are addressed through a linear optimal power flow using today’s power grid capacities and a generation portfolio allowing a 66% generation share of renewable energy. The results of the optimization indicate that for reaching a renewable energy generation share of 53% with this set-up, a few central storage units with a relatively low overall additional storage capacity of around 1.6 GW are required. By adding a constraint of achieving a renewable generation share of at least 66%, storage capacities increase to almost eight times the original capacity. A comparison with the German grid development plan, which provided the basis for the power generation data, showed that despite the non-consideration of transmission grid extension, moderate additional storage capacities lead to a feasible power system. However, the achievement of a comparable renewable generation share provokes a significant investment in additional storage capacities.

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Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

Wienholt

Müller

Bartels³

2018

Energies

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“…Location-dependent generation costs are determined with a fundamental power generation planning model, which optimizes the scheduled operation of the power plants by minimizing generation costs [7]. Fundamental models assume that in principle the market price is based on marginal costs of the associated power generation mix as a fundamental price component.…”

Section: B Market Exogenous Locational Signalsmentioning

confidence: 99%

Search algorithm for optimal locations of generation units in liberalized power systems

Schuster

Sprey

Moser

2012

2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-La)

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A location decision for a generation unit in a preliberalized monopoly energy market was made due to generation costs and transmission costs for electrical energy. A location decision in a liberalized energy market bases on locationdependent investment costs and generation costs without consideration of a location-dependent impact on transmission costs as long as no location signals for generation units are implemented.Location signals create incentives for macro economical efficient locations for generation units. Market exogenous locational signals therefore require a basis for parameterization. It is necessary to identify macro economical efficient location for generation units to create efficient incentives. This paper presents an optimization approach to identify optimal locations for generation units based on a macro economical assessment of locations for generation units. The assessment method of locations for generation units considers the location-dependent impact on transmission costs as an efficient combination of power redispatch and transmission network reinforcement measures. The assessment method also considers the location-dependent optimal generation dispatch. The search algorithm for optimal locations for generation units bases on a particle swarm optimization approach.Index Terms -power system planning, allocation signals, particle swarm optimization

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“…Both technologies share the intermittency issues inherent to renewables; hence, forecasting becomes a critical process to integrate all these new sources while keeping the grid stable. There are different studies regarding the importance of forecasting in the grid integration like [3] or [4], which show, with modeling performed at the European level, how relevant the forecasting processes are, for short-, mid-and long-term, to assure grid stability [5].…”

Section: Introductionmentioning

confidence: 99%

“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

2019

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To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.

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Modeling optimal redispatch for the European Transmission grid

Cited by 33 publications

References 1 publication

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

Search algorithm for optimal locations of generation units in liberalized power systems

“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

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