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.
Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the duration of rain vs nonrain. Here we present a two-step analysis utilising current machine learning techniques for imputing precipitation data sampled at 30-minute intervals by devolving the task into (a) the classification of rain or non-rain samples, and (b) regressing the absolute values of predicted rain samples. Investigating 37 weather stations in the UK, this machine learning process produces more accurate predictions for recovering precipitation data than an established surface fitting technique utilising neighbouring rain gauges. Increasing available features for the training of machine learning algorithms increases performance with the integration of weather data at the target site with externally sourced rain gauges providing the highest performance. This method informs machine learning models by utilising information in concurrently collected environmental data to make accurate predictions of missing rain data. Capturing complex nonlinear relationships from weakly correlated variables is critical for data recovery at sub-hourly resolutions. Such pipelines for data recovery can be developed and deployed for highly automated and near instantaneous imputation of missing values in ongoing datasets at high temporal resolutions. Keywords: machine learning, data imputation, gradient boosted trees, environmental sensor networks, precipitation, soil moisture 1. Introduction Precipitation data is of critical importance across multiple lines of enquiry, informing statistical models and analysis relating to weather forecasting, extreme weather events, climate change, water-resource management, droughts, flooding, agricultural impact, and hydroelectric power. Historical rainfall data can reveal long term trends in environmental hydrological issues with real-time data input allowing for immediate forecasting of future conditions. Distributed networks of rain gauges are typically used to provide precipitation data at the earth's surface at varying temporal resolutions and can cover large geographical areas (Kidd, 2001). As is the case in many databases, particularly those utilising physical sensors, the problem of missing data arises. Missing data can be a result of sensor failure, data storage/transmission failure, or post-collection quality control procedures resulting in removal of identified problem data (Blenkinsop et al., 2017). Missing data in precipitation databases represents a serious limitation for the effective use of the data. Given the global scale and importance of precipitation and meteorological data (Sun et al., 2018), developing solutions to missing data is of paramount importance for maximising information gain.
The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In this context, the short-term electricity markets and in particular the intraday market are considered a suitable trading floor for these exchanges to occur. A key component for the successful renewable energy sources integration is the usage of energy storage. In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book. The goal of the storage device operator is the maximization of the profits received over the entire trading horizon, while taking into account the operational constraints of the unit. The sequential decision-making problem of trading in the intraday market is modelled as a Markov Decision Process. An asynchronous distributed version of the fitted Q iteration algorithm is chosen for solving this problem due to its sample efficiency. The large and variable number of the existing orders in the order book motivates the use of high-level actions and an alternative state representation. Historical data are used for the generation of a large number of artificial trajectories in order to address exploration issues during the learning process. The resulting policy is back-tested and compared against a benchmark strategy that is the current industrial standard. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
We consider the challenging practical problem of optimizing the power production of a complex of hydroelectric power plants, which involves control over three continuous action variables, uncertainty in the amount of water inflows and a variety of constraints that need to be satisfied. We propose a policy-search-based approach coupled with predictive modelling to address this problem. This approach has some key advantages compared to other alternatives, such as dynamic programming: the policy representation and search algorithm can conveniently incorporate domain knowledge; the resulting policies are easy to interpret, and the algorithm is naturally parallelizable. Our algorithm obtains a policy which outperforms the solution found by dynamic programming both quantitatively and qualitatively. Papers published at the Neural Information Processing Systems Conference.
--Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water . The recent advances in Information and Communication T echnologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. S power generation from renewable sources is increasingly seen as a fundamental component in a joint effort to support decarbonization strategies, hydroelectric power generation is experiencing a new golden age. In fact, hydropower has a number of advantages compared to other types of power generation from renewable sources. Most notably, hydropower generation can be ramped up and down, which provides a valuable source of flexibility for the power grid, for instance, to support the integration of power generation from other renewable energy sources, like wind and solar. In addition, water in hydropower plants' large reservoirs may be seen as an energy storage resource in low-demand periods and transformed into electricity when needed , .
H3 Dynamics has partnered with Curitiba-based EPH Engineering in Brazil, a firm that specializes in hydropower design, dam inspections and safety plans, to launch a turnkey dam inspection solution that combines AI-enabled damage assessment and HYCOPTER fuel cell drones capable of flying 3.5 hours at a time. With over 5,000 dams submitted to the Brazilian Dam Safety Plan, and two recent collapse incidents causing more than 300 deaths and major environmental damage, Brazilian authorities have tightened inspection and upkeep requirements in the country. "Many accident reports show that problems were not detected by instrumentation but by visual observation. Drones can help, but due to the large dimensions of these structures we need much longer flight times." Some of the dams are so large that they would require months of battery-powered drone flights to fully scan their surfaces.
Rocky Mountain Power said yesterday that an artificial intelligence (AI) Home Energy Reports solution helped 330,000 of its customers saved over 41 gigawatt-hours (GWhs) of energy since being introduced less than a year ago. The solution produced the savings at an average of approximately four cents per kilowatt hour, a roughly 25 percent cost reduction as compared to conventional Home Energy Reports. "We were searching for the next wave of customer engagement and a way to drive customers toward a digital, two-way dialogue with us," Clay Monroe, director of customer relations for Rocky Mountain Power, said. "With AI reports we are able to quickly shift from conventional methods of reporting, using general peer comparisons, to true energy empowerment with itemized energy bills and personalized savings tips, while at the same time moving customers to digital reports." In 2018, Rocky Mountain Power replaced its existing Home Energy Reports program with AI-powered reports called iHERs. Approximately 330,000 customers in Utah, Idaho, and Wyoming received itemized energy reports for the first time, and more than 50 percent of these customers moved to digital reports with the help of the iHER solution.
In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electric grid fault detection, classification, subgroup discovery, and visualization. In the first stage, a computationally efficient anomaly detection method based on quartiles detects the presence of a fault in real time. In the second stage, the fault is classified into one of nine pre-defined disaster scenarios. The time series data are first mapped to highly discriminative features by applying dimensionality reduction based on temporal autocorrelation. The features are then mapped through one of three classification techniques: support vector machine, random forest, and artificial neural network. Finally in the third stage, intra-class clustering based on dynamic time warping is used to characterize the fault with further granularity. Results on the Bonneville Power Administration electric grid data show that i) the proposed anomaly detector is both fast and accurate; ii) dimensionality reduction leads to dramatic improvement in classification accuracy and speed; iii) the random forest method offers the most accurate, consistent, and robust fault classification; and iv) time series within a given class naturally separate into five distinct clusters which correspond closely to the geographical distribution of electric grid buses.
The global energy sector is going through a paradigm shift in the way it produces and distributes power. There is a massive demand for reliable, clean, and cost-effective energy, and AI (artificial intelligence) is bound to play a vital role in meeting this future global demand. While it is clear that solar, wind, nuclear, and hydroelectric energy will gradually replace the traditional coal-fired power plants, one of the current hurdles in this path is the inconsistency and unpredictability of renewable power. A windless day or a cloudy afternoon can restrain the generation of renewable energy and lead to temporary power shortfalls. Similarly, an abnormally windy or sunny weather can help generate excess energy, which can be wasteful or costly to store.
On the Sunday morning after the weather cleared, a pair of NASA researchers loaded onto a small plane at the Mammoth Yosemite Airport, a single-runway operation that stretches out before the pyramid peak of Mount Morrison. After final safety checks, the pilots lifted off, marking the Airborne Snow Observatory's inaugural flight of the season. The ASO is a twin-turboprop Beechcraft King Air 90, equipped with a pair of sensors pointing through a glass cutout on the bottom of the plane. The lidar measures the volume of the mountain snowpack while a spectrometer gauges its reflectivity, together providing a highly accurate estimate of how much water will run off the mountain in the spring and when it will flow through California's warren of dams, reservoirs, and aqueducts. The data allows water authorities to more carefully manage the water charging hydroelectric power plants, feeding towns and cities, and nourishing one of the United States' most productive agricultural regions.