The British Energy Market Reform: Carbon Prices, Retail Tariffs, and Cost Pass-through

The United Kingdom’s (UK’s) Climate Change Act 2008 sets a “net zero” target on greenhouse gas emissions by 2050. The Act has triggered the Electricity Market Reform (EMR) started in 2013, which aims at decarbonising electricity supply, ensuring the security of supply, and minimising the cost of energy to consumers. This thesis focus on policies and instruments that support the EMR. Chapter 1 provides a review of the UK’s Climate Change Act, with a focus on the energy sector. Chapters 2 and 3 focus on the British decarbonisation policy levied on the electricity sector. Chapter 4 studies competition in the wholesale electricity market, to investigate the existence of market power. Chapter 5 examines the impact of dynamic retail tariffs and demand response, which are possible instruments to secure electricity supply. Chapter 6 concludes. Decarbonisation requires increasing the capacity of renewable energy to phase out carbon insensitive fossil plants, and the British Carbon Price Support (CPS, a carbon tax) gives adequate, credible and sufficiently durable carbon price signals for low-carbon investments. Chapter 2 studies the impact of the CPS on the CO2 emission reduction of wind in the Great Britain’s (GB’s) electricity market. We show how to measure the Marginal Displacement Factor (MDF, tonnes CO2/MWh) of wind. The short-run (SR) MDF is estimated econometrically while the long-run (LR) MDF is calculated from a unit commitment model of the GB system in 2015. We examine counter-factual fuel and carbon price scenarios. The CPS lowered the SR-MDF by 7% in 2015 but raised the LR-MDF (for a 25% increase in wind capacity) by 18%. We discuss reasons for the modest differences in the SR- and LR-MDFs. Being a unilateral carbon tax, the CPS can distort electricity trade with external markets. Chapter 3 shows how to estimate the deadweight cost of the distortion and possible external global benefits from reduced emissions, and investigate econometrically the impact of the CPS on GB’s cross-border electricity trade with France and The Netherlands. Over 2015-2018 the CPS raised GB day-ahead electricity price by about €11/MWh, after allowing for replacement by cheaper imports. It raised French wholesale prices by 3.5% and Dutch wholesale prices by 2.8%. The CPS increased GB imports by 12 TWh/yr, thereby reducing carbon tax revenue by €100 m/yr. Congestion income increased by €150 m/yr, half transferred to foreign interconnector owners. The unilateral CPS created €80 m/yr deadweight loss, about 32% of the initial social value created by the interconnector, or 4% of the global emissions benefit of the CPS at €2 bn/yr. About 0.9% of the CO2 emission reduction is undone by France and The Netherlands, the monetary loss of which is about €18 m/yr. Cost pass-through rates give a useful perspective of market competition, which determines whether consumers are overwhelmed by the market power. Chapter 4 studies how generation costs are passed through to electricity wholesale prices in GB between 2015 and 2018. Our empirical results fail to reject 100% pass-through rates for gas prices, carbon prices, and exchange rates, indicating a competitive GB wholesale electricity market. We observe higher pass-through rates in peak compared to off-peak periods, because generators bidding at a lower rate during off-peak periods to supply at minimum load to avoid the cost of shutting down and starting up. We extend the argument by assessing generators’ bidding behaviour. The study also considers how two key events occurred during the examined period – the drastic decline in the GBP/EUR exchange rate since the Brexit referendum, and major reformation of the EU Emissions Trading System – have affected the electricity bill to a typical domestic household, showing that they have increased the average annual bills by €49(£41)/year/household, or a 7% rise. Finally, one possible solution to the security of electricity supply is demand response, which is usually achieved through dynamic tariffs by offering consumers financial incentives to shift or reduce peak load to off-peak periods. In Chapter 5, we construct an agent-based model in which the retailer sets dynamic tariffs to maximize profit, and consumers respond to the prices. The model suggests that in the baseline scenario, the dynamic tariff would generate for the retailer an additional €7.35 of annual profit from the average household. For a firm equal in size to British Gas in 2017, this is equivalent to €40 million of total benefit. With market regulations, the dynamic tariff will benefit consumers and retailers alike, resulting in a win–win condition. We also find that the interaction between demand-side management stimuli and market regulation can further reduce consumer-level electricity demand, increase retail profit, and lower consumers’ electricity bills

Smartphone data usage : downlink and uplink asymmetry

Mobile phone usage has changed significantly over the past few years and smartphone data usage is still not well understood on a statistically significant scale. This Letter analyses 2.1 million smartphone usage data values and explore the current wireless downlink–uplink demand asymmetry for different time periods and across different radio access networks. The current data demand over 2G networks remains largely symmetric with strong temporal variations, whereas the demand over 3G networks is asymmetric with surprisingly weak temporal variations is shown here

Structural evolution and mechanism of multi-phase rift basins: A case study of the Panyu 4 Sag in the Zhu Ⅰ Depression, Pearl River Mouth Basin, South China Sea

The study of changes in normal fault systems during different rift stages is important to understand the genesis and evolution of multi-phase rift basins, such as the Panyu 4 Sag in the Zhu Ⅰ Depression. Using 2D and 3D seismic data and analogue modelling, the Zhu Ⅰ Depression was characterized as a series of half-grabens bounded by NE-NEE-trending normal faults, it was found to have undergone two phases of the extension during the Paleogene. The Zhu Ⅰ Depression exhibited four fault sets with different strikes, including NNE, NE-NEE, EW, and NWW. The main controlling faults were NE-trending and EW-trending with high activity rates during Rift Phase 1 and Rift Phase 2, respectively. The average azimuths of the dominant strikes for type Ⅰa, type Ⅰb, and type Ⅱ faults were 75°, 85°, and 90°, which revealed that the minimum principal stress (σ3) directions during the rift phases 1 and 2 of the Zhu Ⅰ Depression were SSE (∼165°) and near-EW (∼180°), respectively. Two phases of structural-sedimentary evolution, with different directions and analogue modelling results, illustrated that the Panyu 4 Sag was formed as a superimposed basin under multi-phase anisotropic extension. The structural evolution of the Panyu 4 Sag since the Paleogene was mainly controlled by the combined effects of the Pacific, Eurasian, and Indian plates. Since the orientation of subduction of the Pacific plate changed from NNW to NWW, the stress field shifted from NW-SE-trending tension to S-N-trending tension, causing the superimposition of late near-E-W-oriented structural pattern on the early NE-oriented structural pattern

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Quantitative prediction of palaeo-uplift reservoir control and favorable reservoir formation zones in Lufeng Depression

In this paper, taking the Lufeng Depression as the study object, the distribution characteristics and reservoir-controlling conditions of palaeo-uplift are analyzed from both qualitative and quantitative perspectives. The distribution characteristics of the three-level palaeo-uplift structural pattern are elucidated, which show that the palaeo-uplifts went through three structural evolutionary stages: Eocene, Early-Middle Miocene, and Late Miocene, with long-term inherited development characteristics. Palaeo-uplift controls the distribution of hydrocarbon planes, the direction of dominant hydrocarbon transport, the development of various traps, and the types of hydrocarbon reservoirs. Applying the principle and method of “multi-element matching reservoir formation model”, the corresponding geological and mathematical models are established, which indicate that 86.29% of the number of reservoirs are distributed on the top and slope of the palaeo-uplift, and the reserves and number decrease with the distance to the top of the palaeo-uplift. Based on the palaeo-uplift control model, four high-probability areas for palaeo-uplift control in the Wenchang and Enping Fms are predicted, which are mainly located in the Lufeng middle-low uplift, the Dongsha uplift, and uplifts within the depression.Cited as: Guo, B., Yu, F., Wang, Y., Li, H., Li, H., Wu, Z. Quantitative prediction of palaeo-uplift reservoir control and favorable reservoir formation zones in Lufeng Depression. Advances in Geo-Energy Research, 2022, 6(5): 426-437. https://doi.org/10.46690/ager.2022.05.0

Criteria and favorable distribution area prediction of Paleogene effective sandstone reservoirs in the Lufeng Sag, Pearl River Mouth Basin

As the focus of conventional oil and gas exploration is changing from shallow to deep layers, the identification of deep effective reservoirs is crucial to exploration and development. In this paper, based on the geological anatomy of oil and gas reservoirs, a new discriminatory criterion and evaluation method for effective reservoirs is proposed in combination with the analysis of reservoir formation dynamics mechanism. The results show that the hydrocarbon properties of the reservoir vary with the ratio of the capillary force between the sandstone reservoir and its surrounding rock. The effective reservoir is discriminated and the reservoir quality is evaluated based on the capillary force and depth of the surrounding media and the sandstone reservoir for adjacent plates. When the capillary force ratio is greater than 0.6, fewer effective reservoirs are developed. The effective reservoir is determined by the capillary force ratio of the sandstone reservoir and the surrounding rock medium to mechanically explain the geological phenomenon that low-porosity reservoirs can also accumulate hydrocarbons. Our findings have significant guiding value for Paleogene oil and gas exploration in the Zhu I depression of Pearl River Mouth Basin.Cited as: Yu, S., Wang, C., Chen, D., Guo, B., Cai, Z., Xu, Z. Criteria and favorable distribution area prediction of Paleogene effective sandstone reservoirs in the Lufeng Sag, Pearl River Mouth Basin. Advances in Geo-Energy Research, 2022, 6(5): 388-401. https://doi.org/10.46690/ager.2022.05.0

Significance of ERβ expression in different molecular subtypes of breast cancer

PURPOSE: This study is to investigate the estrogen receptor β (ERβ) expression in molecular subtypes of breast cancer and clinic significance of ERβ expression. METHOD: The ERβ expression was detected in 730 cases of breast cancer tissue specimens by immunohistochemistry. Twenty-one patients were censored during 2–10 years follow-up. The difference in ERβ expression was analyzed by Pearson Chi-square Test. Its correlation with estrogen receptor α (ERα), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her-2) was analyzed by Spearman rank correlation. The accumulative tumor-free survival rate was calculated by Kaplan-Meier method and difference in survival rate was analyzed by Log-rank test. Cox regression was used for multi-factor analysis. RESULT: The ERβ expression was significantly different among the molecular subtypes of breast cancer (P < 0.05). The ERβ expression in breast cancer was positively correlated with Her-2 (P < 0.05) while it had no correlation with ERα and Her-2. The expression of ERα was negatively correlated with Her-2 (P < 0.01) whereas positively correlated with PR (P < 0.01). The expression of PR was negatively correlated with Her-2 (P < 0.05). The tumor-free survival rate in patients with positive ERβ expression was significantly lower than that in patients with negative ERβ expression. CONCLUSION: Positive ERβ expression is a poor prognostic factor of breast cancer. VIRTUAL SLIDES: The virtual slides for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/108455758610683

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot semantic segmentation methods first pre-train the models on `seen' classes, and then evaluate their generalization performance on `unseen' classes. However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes. To tackle these issues, we propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and a further training-based variant, TFS3D-T. Without any learnable parameters, TFS3D extracts dense representations by trigonometric positional encodings, and achieves comparable performance to previous training-based methods. Due to the elimination of pre-training, TFS3D can alleviate the domain gap issue and save a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to train a lightweight query-support transferring attention (QUEST), which enhances the interaction between the few-shot query and support data. Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by +6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure