Transmission network expansion planning: towards enhanced renewable integration

Michi, L.; Migliori, Michela; Bugliari, A. Caldarulo; Aluisio, Benedetto; Giannuzzi, Giorgio Maria; Carlini, Enrico Maria

doi:10.23919/aeit.2018.8577446

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…If the thermal threshold of a component is exceeded this component is congested. This threshold is, considering lines, effected by dynamic line rating [10]. In addition contingencies in the context of N-1 security have to be considered [9], [10] for lines and transformers.…”

Section: Model Description a Theoretical Backgroundmentioning

confidence: 99%

“…This threshold is, considering lines, effected by dynamic line rating [10]. In addition contingencies in the context of N-1 security have to be considered [9], [10] for lines and transformers. So both aspects have to be implemented in a congestion forecast [9].…”

Section: Model Description a Theoretical Backgroundmentioning

confidence: 99%

“…In literature, approaches exist describing the prediction of curtailment for long term scenarios in the context of expansion planning [7]- [10] and during RE integration planning [11] or for congestion management in real time grid operation [12], [13]. Short-term predictions of congestions are essentially required for participants of energy markets in the context of redispatch [2] and locational marginal prices [14].…”

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confidence: 99%

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Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Memmel¹,

Schlüters²,

Völker³

et al. 2021

IEEE Access

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Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.

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Section: Model Description a Theoretical Backgroundmentioning

confidence: 99%

Section: Model Description a Theoretical Backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

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Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Memmel¹,

Schlüters²,

Völker³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The SNSP index has been selected from a specific set of feasible indicators assessing flexibility requirements for the power system [36] as the most representative of PEIG integration. Recent studies conducted by the Italian TSO have evaluated the average value of the SNSP index for the whole Italian power system [37] and for a specific critical section of the Italian power system [38].…”

Section: ) Maximum Level Of System Non-synchronous Penetrationmentioning

confidence: 99%

Technical and Economic Impact of the Inertia Constraints on Power Plant Unit Commitment

Mosca

Bompard

Chicco

et al. 2020

IEEE Open J. Power Energy

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The whole interconnected European network is involved in the energy transition towards power systems based on renewable power electronics interfaced generation. In this context, the major concerns for both network planning and operation are the inertia reduction and the frequency control due to the progressive decommissioning of thermal power plants with synchronous generators. This paper investigates the impact of different frequency control constraints on the unit commitment of power plants resulting from market simulations. The market outputs are compared in terms of system costs, and of frequency stability performance evaluated on the basis of the rate of change of frequency and the maximum frequency excursion. The best compromise solution is found using a multiple-criteria decision analysis method, depending on the choice of the decision maker's weighting factors. The proposed approach is tested on a real case taken from one of the most relevant future scenarios of the Italian transmission system operator. The results show how the best compromise solution that can be found depends on the decision maker preference towards cost-based or frequency stability-based criteria. Index Terms-frequency stability, inertia constraints, multiple-criteria decision analysis, power system inertia, rate of change of frequency, unit commitment.

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“…Following requirements for congestion prediction are addressed by the proposed models of this study: Detailed information of node-injections into the 110 kV grid level [21], [22], consideration of n-1 security (which means that one grid component can fail and grid security is still maintained) [23], [24], and the amount of required RE curtailment should become determinable. As it is aimed to develop a method applicable for grid operation, it should be validated against actual grid data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

Memmel¹,

Steens²,

Schlüters³

et al. 2023

IEEE Access

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The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management.

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Transmission network expansion planning: towards enhanced renewable integration

Cited by 9 publications

References 9 publications

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Technical and Economic Impact of the Inertia Constraints on Power Plant Unit Commitment

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

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