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

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. View Full-Text Keywords: machine learning; power systems; harmonic distortion; power qualitypublishedVersio

The value of multiple data sources in machine learning models for power system event prediction

We describe a method for assessing the value of additional data sources used in the prediction of unwanted events (voltage dips, earth faults) in the power system. Using this method, machine learning models for event prediction using (combinations of) different data sources are developed. The value of each data source is the improvement in model performance it brings. In addition, feature importance is retrieved using SHapley Additive exPlanations (SHAP). The methodology is applied to models that predict faults based on power quality and weather data. We find that models that combine sources outperform models using either in isolation. They predict ground faults and voltage dips with AUCs (Area Under Curve) of 0.74 and 0.80, respectively. Meteorological data appears more valuable than power quality data and the most important features are dew point, month of the year, and the power spectral density at 4.7 HzacceptedVersio

Optimal Production Balance with Wind Power

Maintaining a continuous balance between generation and load is crucial for the safeguarding of the power system. To efficiently deal with uncertainties and unexpected events the TSOs procure balancing services through the so-called balancing markets.The variability and low predictability of the wind speed makes handling of balances a difficult task for wind power producers. Literature regarding the development of the balancing market and Elbas is presented. This research has found that the volatility in the balancing market are expected to increase as a result of cross-border integration of such markets. The low liquidity in the Norwegian Elbas market are expected to rise, as the need of an intraday market becomes more imminent. With increased investments in renewable energy and cross- border capacity the balancing markets are expected to change. With higher volatility in the markets the balancing of production will come at a higher cost. In this report the current wind power balancing done by TrønderEnergi is presented and some possible improvements to better handle the balance, are drafted.The wind power production error and the price in the balancing market are modeled. A Monte Carlo analysis is carried out for three different alternatives:1. Settling the imbalances in the balancing market.2. Settling the imbalances in Elbas.3. Settling the imbalances by using the re-bidding procedure.The parameters are modeled stochastically, so the simulations are carried out a large number of times to get conclusive results.It is the findings of this thesis that the implemented two-price system in the production balance leads to a large deficit when balancing the wind power production. The procedure currently used at TrønderEnergi saves the company a significant amount per annum, but as Elbas matures, this market should be exploited for reducing the costs of balancing and possibly profit seeking operations

Optimal Production Balance with Wind Power

Maintaining a continuous balance between generation and load is crucial for the safeguarding of the power system. To efficiently deal with uncertainties and unexpected events the TSOs procure balancing services through the so-called balancing markets.The variability and low predictability of the wind speed makes handling of balances a difficult task for wind power producers. Literature regarding the development of the balancing market and Elbas is presented. This research has found that the volatility in the balancing market are expected to increase as a result of cross-border integration of such markets. The low liquidity in the Norwegian Elbas market are expected to rise, as the need of an intraday market becomes more imminent. With increased investments in renewable energy and cross- border capacity the balancing markets are expected to change. With higher volatility in the markets the balancing of production will come at a higher cost. In this report the current wind power balancing done by TrønderEnergi is presented and some possible improvements to better handle the balance, are drafted.The wind power production error and the price in the balancing market are modeled. A Monte Carlo analysis is carried out for three different alternatives:1. Settling the imbalances in the balancing market.2. Settling the imbalances in Elbas.3. Settling the imbalances by using the re-bidding procedure.The parameters are modeled stochastically, so the simulations are carried out a large number of times to get conclusive results.It is the findings of this thesis that the implemented two-price system in the production balance leads to a large deficit when balancing the wind power production. The procedure currently used at TrønderEnergi saves the company a significant amount per annum, but as Elbas matures, this market should be exploited for reducing the costs of balancing and possibly profit seeking operations

Deep Reinforcement Learning for Long Term Hydropower Production Scheduling

We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the yearly revenue given week-by-week inflows to the reservoir and electricity prices. The challenge is to decide between immediate water release at the spot price of electricity and storing the water for later power production at an unknown price, given constraints on the system. We successfully train a soft actor-critic algorithm on a simplified scenario with historical data from the Nordic power market. The presented model is not ready to substitute traditional optimisation tools but demonstrates the complementary potential of reinforcement learning in the data-rich field of hydropower scheduling.acceptedVersio

Deep Reinforcement Learning for Long Term Hydropower Production Scheduling

We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the yearly revenue given week-by-week inflows to the reservoir and electricity prices. The challenge is to decide between immediate water release at the spot price of electricity and storing the water for later power production at an unknown price, given constraints on the system. We successfully train a soft actor-critic algorithm on a simplified scenario with historical data from the Nordic power market. The presented model is not ready to substitute traditional optimisation tools but demonstrates the complementary potential of reinforcement learning in the data-rich field of hydropower scheduling

Clustering and Dimensionality-reduction Techniques Applied on Power Quality Measurement Data

The power system is changing rapidly, and new tools for predicting unwanted events are needed to keep a high level of security of supply. Large volumes of data from the Norwegian power grid have been collected over several years, and unwanted events as interruptions, earth faults, voltage dips and rapid voltage changes have been logged. This paper demonstrates the application of clustering and dimensionality-reduction techniques for the purpose of predicting unwanted events. Several techniques have been applied to reduce the dimensionality of the datasets and to cluster events based on analytical features, to separate events containing faults from a normal situation. The paper shows that the developed predictive model has some predictive capability when using balanced datasets containing similar muber of fault events and non-fault events. One of the main findings, however, is that this predictive capability is significantly reduced when using unbalanced datasets. Thus, the development of an accurate predictive model based on normal power system conditions, i.e. an unbalanced dataset of events and non-events, is a topic for further research.acceptedVersio

Exploring household's flexibility of smart shifting atomic loads to improve power grid operation and cost efficiency

This paper explores the possibilities of shifting certain household consumer-based loads in time, to reduce unnecessary load peaks to the grid which again can cause challenges for Distribution System Operators (DSOs). Historical measured data on household consumption and the consumption profile of certain household appliances (dishwashers, washing machines and dryers) that can be shiftable in time are being used in an optimization model to investigate the potential that shifting these loads may have on the overall grid consumption of aggregated groups of households. The results indicate that such a model is a viable approach to effectively lower the peak load with respect to these appliances, even with consumer behavior and the inconvenience to perform these shifts accounted for. However, the contribution of the considered household appliances is arguably modest with respect to the total load of the household. household flexibility, consumer demand, shiftable atomic loads, smart grid, optimization modelNorges forskningsråd; 255209 Norges forskningsråd: 257626acceptedVersio

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

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. View Full-Text Keywords: machine learning; power systems; harmonic distortion; power qualit

Exploring household's flexibility of smart shifting atomic loads to improve power grid operation and cost efficiency

This paper explores the possibilities of shifting certain household consumer-based loads in time, to reduce unnecessary load peaks to the grid which again can cause challenges for Distribution System Operators (DSOs). Historical measured data on household consumption and the consumption profile of certain household appliances (dishwashers, washing machines and dryers) that can be shiftable in time are being used in an optimization model to investigate the potential that shifting these loads may have on the overall grid consumption of aggregated groups of households. The results indicate that such a model is a viable approach to effectively lower the peak load with respect to these appliances, even with consumer behavior and the inconvenience to perform these shifts accounted for. However, the contribution of the considered household appliances is arguably modest with respect to the total load of the household. household flexibility, consumer demand, shiftable atomic loads, smart grid, optimization mode