Detecting the linear and non-linear causal links for disturbances in the power grid

Unscheduled power disturbances cause severe consequences for customers and grid operators. To avoid suchevents, it is important to identify the causes and localize the sources of the disturbances in the power distribution network. In this work, we focus on a specific power grid in the Arctic region of Northern Norway that experiences an increased frequency of failures of unspecified origin

Rethinking the role of solar energy under location specific constraints

In this manuscript we evaluate the potential of photovoltaic systems to meet some dedicated energy demand in specific geographic locations. Our approach is based on location-specific constraints rather than on pre-established, location-independent methodologies or assumptions. First, we propose that a thorough analysis of the socio-economic and technical possibilities of a location must act as the guide to optimize the deployment of renewables. This requires detailed knowledge of the area. Second, we propose that optimizing the exploitation of renewables by focusing on a particular location can also lead to successful outcomes with global impact. With this in mind we focus our attention on the Arctic region, known for its highly seasonal solar availability, and the challenge posed by increasing cruise ship tourism and corresponding air pollution. Our study targets Tromsø city, Norway, and we show that solar energy generation could be a strong contribution for charging cruise ships in the summer with no need for generation and transmission investments. Our study opens the door to shifting to a location specific paradigm to seek sustainable energy solutions with the possibility to have a global impact

Detecting and Interpreting Faults in Vulnerable Power Grids with Machine Learning

Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the power grid of a Norwegian community in the Arctic that experiences several faults whose sources are unknown. First, we construct a data set consisting of relevant meteorological data and information about the current power quality logged by power-quality meters. Then, we adopt machine-learning techniques to predict the occurrence of faults. Experimental results show that both linear and non-linear classifiers achieve good classification performance. This indicates that the considered power quality and weather variables explain well the power disturbances. Interpreting the decision process of the classifiers provides valuable insights to understand the main causes of disturbances. Traditional features selection methods can only indicate which are the variables that, on average, mostly explain the fault occurrences in the dataset. Besides providing such a global interpretation, it is also important to identify the specific set of variables that explain each individual fault. To address this challenge, we adopt a recent technique to interpret the decision process of a deep learning model, called Integrated Gradients. The proposed approach allows gaining detailed insights on the occurr

Uncovering contributing factors to interruptions in the power grid: An Arctic case

Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of North Norway. First, we collect data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploit statistical and machine-learning techniques to predict the occurrence of failures. We interpret the variables that mostly explain the classification results to be the main driving factors of power interruption. We are able to predict 57% (F1-score 0.53) of all failures reported over a period of 1 year with a weighted support-vector machine model. Wind speed and local industry activity are found to be the main controlling parameters where the location of exposed power lines is a likely trigger. In summary, we discuss causing factors for failures in the power grid and enable the distribution system operators to implement strategies to prevent and mitigate incoming failures.Comment: 25 pages, 8 Figures. A full-length article that is under review in the Applied Energy Journa

Enhancing Decision-making in the Electric Power Sector with Machine Learning and Optimization

The electric power system infrastructure is essential for modern economies and societies, as it provides the electricity needed to power homes, businesses, and industries. It is of critical importance that the operation of the electric power system is optimized to serve the electricity demand reliably and sustainably. Advances in machine learning and optimization have enabled the potential to enhance decision-making in the electric power sector by gaining insight into the vast amount of data stored digitally. The operation of electric power systems poses many challenges, such as the rising integration of renewable energy sources, energy storage, and the aging transmission infrastructure. This thesis explores machine learning and optimization techniques to enhance decision-making concerning decarbonization targets, integration of renewable energy sources, cost savings, and reliable power supply. The first work presents a framework for predicting electricity demand. Comparing statistical and machine learning models for short- and medium-term forecasting revealed that machine learning methods provide higher accuracy and demonstrate good transferability. This highlights the importance of choosing the appropriate model to accurately predict the electricity demand, especially where historical data may be scarce. Next, we examined the electricity transmission grid using machine learning classification techniques to identify causes of power distribution network disturbances. Besides indicating variables that explain fault occurrences on average, identifying specific variables for each fault is essential. To address this challenge, we used a technique called Integrated Gradients for interpreting the decision process of a deep learning model, emphasizing the value of detailed insights into specific fault occurrences. In the third work, we adopted probabilistic forecasting to account for the the uncertainty when predicting electricity generation from wind power. As point forecasts don't account for uncertainties in the predictions, relying on probabilistic forecasts is necessary. We showed that deep learning models can provide accurate day-ahead probabilistic forecasts and discovered that including historical weather data and numerical weather predictions as exogenous variables improves forecast accuracy. In the fourth and fifth works, we modeled the electric power system using optimization techniques. The fourth work analyzed the benefit of using a low-cost thermal energy storage unit called Thermal Energy Grid Storage (TEGS) for balancing solar energy system's intermittent generation, highlighting storage's crucial role for grid reliability. In the fifth and final work, we optimized the engineering design of TEGS to minimize the cost of decarbonization in electric power systems. The findings show that TEGS enables cost-effective grid decarbonization and improves reliability compared to a baseline scenario where TEGS is not an available technology

Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case

Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploited statistical and machine-learning techniques to predict the occurrence of failures. The classification models achieve good performance, meaning that there is a significant relationship between the collected variables and fault occurrence. Thus, we interpreted the variables that mostly explain the classification results to be the main driving factors of power interruption. Wind speed of gust and local industry activity are found to be the main controlling parameters in explaining the power failure occurrences. The result could provide important information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures

Power availability of PV plus thermal batteries in real-world electric power grids

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, a real-world electricity system was modeled rather than modeling hypothetical future electric power systems where the existing electricity infrastructure are neglected. In addition, instead of modeling the general requirements of storage in terms of cost and performance, an existing thermal energy storage concept with estimated capital cost that are sufficiently low to enable large-scale deployment in the electric power system were modeled. The storage unit is coupled with a photovoltaic (PV) system and were modeled with different storage capacities, whereas each storage unit had various discharge capacities. The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO2 emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO2 reduction scenarios, as the storage unit is utilized differently. When CO2 emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit. The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems

Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case

The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Nowadays, contracts and auctions of renewable energy in a liberalized electricity market heavily rely on forecasting future power generation. The highly intermittent nature of renewable energy sources increases the uncertainty about future power generation. Since point forecast does not account for such uncertainties, it is necessary to rely on probabilistic forecasts. This work first introduces probabilistic forecasts with deep learning. Then, we show how deep learning models can be used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway. The performance, in terms of the quality of the prediction intervals, is compared to different deep learning models and sets of covariates. The findings show that the accuracy of the predictions improves by 37% when historical data on measured weather and numerical weather predictions (NWPs) were included as exogenous variables. In particular, historical data allows the model to auto-correct systematic biases in the NWPs. Finally, we observe that when using only NWPs or only measured weather as exogenous variables, worse performances are obtained. The work shows the importance of understanding which variables must be included to improve the prediction performance, which is of fundamental value for the energy market that relies on accurate forecasting capabilities

Predicting Energy Demand in Semi-Remote Arctic Locations

Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available

Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case

Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploited statistical and machine-learning techniques to predict the occurrence of failures. The classification models achieve good performance, meaning that there is a significant relationship between the collected variables and fault occurrence. Thus, we interpreted the variables that mostly explain the classification results to be the main driving factors of power interruption. Wind speed of gust and local industry activity are found to be the main controlling parameters in explaining the power failure occurrences. The result could provide important information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures