Machine learning analysis for a flexibility energy approach towards renewable energy integration with dynamic forecasting of electricity balancing power

Essl, Andreas; Ortner, André; Haas, Reinhard; Hettegger, Peter

doi:10.1109/eem.2017.7981877

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…The study emphasises that the sources of imbalances do not have the same impact within different control areas (TenneT, Amprion, 50Hertz), e.g., the wind generation has a lower impact in the Amprion control area than in the two others. [12] uses the Lasso to weight the sources of imbalances, which can be detrimental if two variables are highly correlated. As for reserve needs prediction, [13], [14] study the case of the German control area for a quarter of hour time step, using a neural network and quantile regression.…”

Section: A Frr Dimensioning Methodsmentioning

confidence: 99%

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Static VS dynamic FRR sizing for power systems with increasing amounts of renewables

Cauwet

Karangelos

Wehenkel

et al. 2019

2019 IEEE Milan PowerTech

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This paper investigates the sizing of the Frequency Restoration Reserve (FRR) in a context of increasing penetration of renewable generation. More precisely, we propose (i) a probabilistic method that mimics the current Belgian TSO (Elia) practices and (ii) a Monte-Carlo based procedure that evaluates the corresponding reliability of the system in terms of down/upward reserves activation, wind curtailment and load shedding. Using this method over the IEEE-RTS96 testcase, the impact of wind penetration-low, moderate, high-is studied. In particular, static (annual and seasonal) and dynamic (weekly and hourly) FRR sizing approaches are defined and compared. It turns out that the hourly sizing method is the most robust. It also appears that FRR requirements for upward reserves are almost not impacted by the high wind penetration whereas the downward reserves increase significantly with the wind penetration. Our implementations rely on Julia, Cplex and R and are available in open source.

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Section: A Frr Dimensioning Methodsmentioning

confidence: 99%

“…A key issue in this work was the sparse data regarding the power unit outages, requiring an adaptive bias correction function. Reference [12] studies the case of the Austrian control area for a day-ahead reserve dimensioning, using the Lasso and quantile regression.…”

Section: A Frr Dimensioning Methodsmentioning

confidence: 99%

Static VS dynamic FRR sizing for power systems with increasing amounts of renewables

Cauwet

Karangelos

Wehenkel

et al. 2019

2019 IEEE Milan PowerTech

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“…For instance, ML can be used to approximate or simplify existing optimization problems [75,242,311,871], find good starting points for optimization [52,196,382], identify redundant constraints [541], learn from the actions of power system control engineers [197], or do some combination of these [858]. Dynamic scheduling [225,546] and (safe) reinforcement learning (RL) could also be used to balance the electric grid in real time; in fact, some electricity system operators have started to pilot similar methods at small, test case-based scales [520].…”

Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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“…On the other hand, in [42], when using AI-based dynamic approach different reserve capacities for every quarter-hour were proposed depending on the RES forecasts, load forecasts, their gradients, calendar, and time effects. It led to lower balancing costs.…”

Section: Ai-based Imbalance Capacity Predictionmentioning

confidence: 99%

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Hameed

Hashemi

Traholt

2021

2021 22nd IEEE International Conference on Industrial Technology (ICIT)

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Rising environmental concerns are integrating more renewables in power systems. This increase introduces uncertainty in generation and makes it challenging to maintain a balance between demand and supply. To avoid balancing problems and consequent stability issues, better forecast models are needed as traditional techniques are not fully equipped to deal with these new challenges. Thus, artificial intelligence (AI) based forecast techniques are gaining potential recognition in the realm of electricity markets. This paper aims at investigating the state-of-art of AI applications for price forecasts in electricity balancing markets (EBMs). The focus of previous studies extended in this direction has been towards the dayahead markets, whereas studies targeting EBMs are rather scarce. This paper shows how AI-based forecasts support EBMs modeling, resulting in more secure grid integration of distributed technologies. The benefits driven from such forecasts by market participants like brokers and customers are also investigated.

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Machine learning analysis for a flexibility energy approach towards renewable energy integration with dynamic forecasting of electricity balancing power

Cited by 9 publications

References 17 publications

Static VS dynamic FRR sizing for power systems with increasing amounts of renewables

Static VS dynamic FRR sizing for power systems with increasing amounts of renewables

Tackling Climate Change with Machine Learning

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

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