Deep Reinforcement Learning for Long Term Hydropower Production Scheduling

Riemer-Sørensen, Signe; Rosenlund, Gjert H.

doi:10.1109/sest48500.2020.9203208

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…For example, ref. [39] feeds climate data, expected demand curves, and market conditions into a reinforcement learning system for optimal (most profitable) long-term scheduling. See also [40] for a recent review.…”

Section: Production and Assets Transmission And Distribution Consumptionmentioning

confidence: 99%

Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid

et al. 2022

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With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. Advances in machine learning and availability of data suggest great potential in using data-driven approaches, but these will only ever be as good as the data they are based on. To lay the ground-work for future data-driven modelling, we establish a baseline state by analysing the statistical distribution of voltage measurements from three sites in the Norwegian power grid (22, 66, and 300 kV). Measurements span four years, are line and phase voltages, are cycle-by-cycle, and include all (even and odd) harmonics up to the 96 order. They are based on four years of historical data from three Elspec Power Quality Analyzers (corresponding to one trillion samples), which we have extracted, processed, and analyzed. We find that: (i) the distribution of harmonics depends on phase and voltage level; (ii) there is little power beyond the 13 harmonic; (iii) there is temporal clumping of extreme values; and (iv) there is seasonality on different time-scales. For machine learning based modelling these findings suggest that: (i) models should be trained in two steps (first with data from all sites, then adapted to site-level); (ii) including harmonics beyond the 13 is unlikely to increase model performance, and that modelling should include features that (iii) encode the state of the grid, as well as (iv) seasonality.

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Section: Production and Assets Transmission And Distribution Consumptionmentioning

confidence: 99%

Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid

et al. 2022

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“…For instance, in [119], a multi-critic method (a variant of Actor-critic) for operational control of interconnected water storage tanks. Another use case of such methods is Hydropower production scheduling [126].…”

Section: ) Reinforcement Learningmentioning

confidence: 99%

Cyber-Physical Systems for Smart Water Networks: A Review

et al. 2021

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There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.

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