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Geothermal energy exploration, development, and research have been ongoing in Canada for several decades. The country's cold climate and the push to develop renewable energy sources have driven interest in geothermal energy. Despite this drive, regulatory complexities and competition with other relatively inexpensive energy sources with existing infrastructure have hindered development. As such, interest has grown and waned with changes in the energy economy over several decades, leaving many projects at a standstill. As of January 2023, there are currently no operational geothermal power projects in Canada. Many hot spring pool and spa complexes remain active, and Canada is a leading country in the installation of ground source heat pumps (GSHPs; also called geo-exchange systems). However, in the last decade, the interest in deep geothermal systems has renewed, with many new projects starting up across several provinces and territories. Moreover, projects that had shown limited progress for many years—such as Mount Meager in British Columbia—have begun to renew their development efforts. Research is also expanding within prominent research groups and universities. The areas of focus include both building upon previous studies (such as thermal gradients and the heat flow in sedimentary basins) and researching new methods and resources (such as GSHPs, closed-loop systems, integrated geothermal operations, and hybrid systems, including heat storage). The development is supported by federal, provincial, and territorial governments through grants and the development of regulatory frameworks. Although challenges still remain for Canada to develop its geothermal energy resources, several power, thermal, and co-production projects, ongoing research, funding, and regulatory acts are all moving forward to support geothermal development. This paper aims to study Canada's geothermal energy update in 2023 regarding the aspects mentioned above. [ABSTRACT FROM AUTHOR]

Unconventional oil and gas formations are abundant, have become an increasingly important part of the global energy supply, and are attracting increasing attention from the industry. Predicting key reservoir properties plays a significant role in both geological science and subsurface engineering workflows. With the advent of horizontal well drilling and multiple-stage hydraulic fracturing, the Montney Shale formation is one of the most promising and productive shale plays in Canada. However, very few academic papers discuss its in situ stress, reservoir pressure, and permeability, which are essential for the development of the Montney Shale. The objective of this study is to analyze the geo-stress, the pore pressure, and several key reservoir properties by using diagnostic fracture injection test (DFIT) data from the Montney Shale. One horizontal well from the Wapiti field has been analyzed with a set of DFIT data, and its results show that the general pressure and Gdp/dG responses from Well-A indicate a signature of height recession/transverse storage. In the study, the Tangent Line method, the Compliance method, and the Variable Compliance method have been applied to estimate the key reservoir properties. As a result, the Well-A DFIT analysis estimates that the closure pressure is ranging from 34.367 to 39.344 MPa, contributing to the stress gradient from 14.09 to 16.13 KPa/m for the formation. The pore pressure is ranging from 20.82 to 24.58 MPa, contributing to the pore pressure gradient from 8.54 to 10.07 KPa/m for the formation. The porosity is ranging from 3% to 6%. These reservoir properties are contoured cross the Montney Shale formation. Using the DFIT's numerical simulation and history matching, the reservoir permeability is 0.024 md, fracture length is 13.44 m, and fracture geometries are analyzed by different models. Moreover, the physics behind the DFIT are analyzed and discussed in detail. For the first time, three different analysis methods have been applied to estimate a series of key reservoir properties for the case wells in the Montney Shale formation. This approach can not only reduce the potential prediction error caused by a single method application but also increase the persuasiveness of the assessment and save time, ensuring the efficient implementation of engineering operations. Given the significance of quantifying in situ stress and reservoir pore pressure in unconventional hydrocarbon exploration and development, this study could help the operator to quickly understand the stress regimes, the fracture geometry, and the formation properties of the Montney Shale formation in the Wapiti field. Furthermore, the interpreted results demonstrated in this paper are adding substantial business value to the asset, especially in terms of improving the hydraulic fracturing design and, thus, accelerating the cashflow from production. [ABSTRACT FROM AUTHOR]

The import demand for energy resources, including liquefied natural gas (LNG), has been steadily increasing in the Asia-Pacific region. Australia, the Middle East (Qatar), the Russian Federation, and the U.S. are the major players who compete strategically to capture this ever-growing market for LNG. The objective of this paper is to examine the potential for Canada's entry into this market as another LNG exporter and what impact that can have on the existing suppliers. Using a game-theoretic LNG export competition model, we explore the conditions under which Canada can make a profitable entry. We also investigate the effect of Canada's entry on the profitability of the four incumbent exporters. Employing a multi-leader Stackelberg model, we found that Canada's entry could be a Pareto superior outcome under certain conditions because it benefits all competing firms and consumers. Further, Canada's entry into the LNG export market always helps the low-cost incumbent firms by increasing their output and profit. However, the high-cost incumbent firms' output falls, while their profit may increase or decrease depending on the unit cost and market size parameters. With differential export costs between Canada and the U.S., the latter has an incentive to act strategically to affect the entrance of the former. [ABSTRACT FROM AUTHOR]

"Short-term load forecasting (STLF)" is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario's electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions. [ABSTRACT FROM AUTHOR]

In remote communities of Canada, diesel is the primary source of electricity and heat. Promoting sustainable and diverse means of heat and power generation is essential to providing reliable and less carbon-intensive energy supply to remote communities. Among renewable energy sources in Canada, biomass is a major source of energy, with wood pellets being a notable contributor. In this study, using wood pellets in a remote community of Canada is investigated using life cycle analysis (LCA). Furthermore, wood pellet combustion is compared with diesel combustion, the most common fossil fuel in these regions. SimaPro (version 8.4.0.0) was used with Ecoinvent 3 as the primary library because of the nature of the feedstock. Harvesting, transportation, sawmill operation, pelletization, and combustion stages are considered in LCA. In doing so, first, life cycle data related to each of these stages are collected with respect to eight impact categories of global warming, ozone depletion, carcinogenic, non-carcinogenic, smog, respiratory effects, acidification, eutrophication, ecotoxicity, and fossil fuel depletion. The results indicate that pelletization and combustion stages have the greatest environmental impact, specifically in terms of non-carcinogenic effects from pelletization and respiratory effects from pellet combustion. Additionally, when comparing wood pellets to diesel, wood pellet combustion exhibits superior performance across various impact categories, particularly in non-carcinogenic effects. [ABSTRACT FROM AUTHOR]

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers' smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions. [ABSTRACT FROM AUTHOR]