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This edition of the Global Energy and Climate Outlook (GECO) analyses the role of electrification in global transition pathways to a low Greenhouse Gas (GHG) emissions economy. Electricity is found to be an increasingly important energy carrier in final energy consumption already in the absence of stronger climate policies than those currently in p...
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In recent years, the development of energy prices in Germany has been frequently accompanied by criticism and warnings of socio-economic disruptions. Especially with respect to the electricity sector, the debate on increasing energy bills was strongly correlated with the energy system transition. However, whereas fossil fuels have rapidly increased...
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... Achieving a 1.5 °C-compatible trajectory will require scaling up initial JETP goals' ambition. Beyond its own emissions, the power sector decarbonization represents a fundamental step to decarbonizing the whole energy system via electrification of end uses 6,19,20 . Scaling up energy efficiency investments, keeping captive coal capacities in check (Supplementary Section 4) and transforming coal-intensive industries (for example, steel manufacturing) will be important elements to align with a Paris trajectory. ...
Recent climate diplomacy efforts have resulted in Just Energy Transition Partnerships (JETPs) with South Africa, Indonesia and Vietnam, mobilizing financial support for ambitious decarbonization targets. Here, to assess JETPs’ alignment with global climate targets, we conduct a model-based assessment of JETPs’ energy and emissions targets. Results show greater alignment with a global 1.5 °C trajectory, indicating a promising route for international collaboration to keep Paris Agreement goals within reach.
... The U.S. National Academies of Science, Engineering, and Medicine recently called for electrification of heating in new construction in much of the United States by 2030, in order for the country to reach net-zero carbon emissions by 2050, and avert the worst consequences of climate change [2]. The European Commission concluded that averting global temperature rise of more than 1.5 • C by 2100 would likely require electrification of more than 60% of energy end uses in buildings by 2050, and that a similar degree of electrification would be required in buildings in China in order to avert global temperature rise of more than 2 • C. [3]. Given worldwide trends towards urbanization, it is expected that 68% of people will be living in cities by 2050 [4]. ...
... Based on the work of [13], a threshold for the CV-RMSE of the objective function values for the swarm relative to that of the best objective function thus far was implemented as a convergence criterion. 3 An additional criterion for termination of the algorithm was implemented, based on "stall", under which the algorithm will terminate if the global best value of the objective function has not improved for a certain number of iterations. In this implementation of PSO, the maximum number of iterations was set at 500, based on the results of several test evaluations. ...
In this work, a topology optimization framework for district thermal energy systems is presented. The framework seeks to address the questions, for a given district, "What is the best subset of buildings to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?" A particle swarm optimization approach is validated to address the selection of the subset of buildings, and a graph theory-based heuristic is validated for selection of the network topology for any candidate subset of buildings. The framework is applied to a prototypical urban district for illustrative purposes. Modeling of prototypical districts revealed reductions in source energy use intensity for heating and cooling of 21-25% through the use of advanced district energy systems relative to code-compliant, building level systems. The framework identifies solutions with life cycle cost values 14% to 72% lower than that of base case scenarios based on conventional design approaches, depending on the base case scenario selected. Analysis of the search space indicates that topology optimization facilitates reductions in life cycle cost, source energy use intensity, and carbon emissions.
... Today, this trend is further strengthened by the possibility of incorporating less carbon-intensive technologies in the electricity supply mix, with, for instance, wind and photovoltaic production. In complement, an electrification process at the demand-side is currently boosted in the European Union, where novel electricity-based solu- [4] tions in the transportation and residential heating sectors (e.g., electric cars or electric heat pumps) are currently strongly encouraged [7]. illustrates the shift initiated in the electricity supply mix, where the European electricity production from renewable energy sources is continuously rising since the 1990s. ...
... For instance, the system imbalance is strongly related to human activity, which thus results in daily and weekly periodicities. By nature, calendar-based features are categorical variables, e.g., the hour of the day h = {0, 1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22, 23}, for which both rescaling procedures (i.e., standardization and normalization) are not adequate for handling their discontinuities. Indeed, the relative importance between time data is not easily quantified by a numerical value. ...
The European Union aims at reaching climate neutrality at horizon 2050 by achieving an economy with net-zero greenhouse gas emissions. Such ambitious policy target has implied and will continue to imply big changes in European energy systems, which accounted for 74% of European greenhouse gas emission in 2019. In this line, the electricity sector is currently rapidly shifting its supply mix towards less carbon-intensive energy sources, which conducts to growing shares of weather-dependent renewable energy sources (e.g., wind and photovoltaic power). The generation profile of these technologies differs from conventional ones (e.g., gas-fired units) by their high intermittency and uncertainty, which rises the difficulty of ensuring a continued, real-time balance between the electricity demand and supply. This balancing requirement is vital when operating electricity systems since a mismatch between demand and supply automatically deteriorates the system frequency, which may trigger the disconnection of system components, and ultimately, lead to power blackouts. In the unbundled European electricity markets, the balancing management is supported via the balancing markets, which establish (amongst other) the market rules for the real-time trading of energy. While originally designed at national level, the European balancing markets are currently undergoing a harmonization process for fostering the cooperation between European countries. In this process, the favored option for pricing the real-time energy imbalances is the single price imbalance settlement, which provides financial incentives for market actors to adopt an imbalance position in the opposite direction of the total net system imbalance. When appropriately provided, this real-time balancing service is beneficial for the whole system as it reduces the total net system imbalance, which requires thus less costly corrective balancing actions.
This work is focused on developing novel forecast-driven strategies for fostering the provision of such real-time balancing services in European electricity markets. Practically, these strategies are studied using an integrated approach, where the entire value chain, i.e., from forecasting to the decision-making processes, is modeled for optimizing close-to-real-time the imbalance position of a market actor. In this setting, the methodological contributions principally concern the modeling of uncertainty and risk in their operational strategies. More specifically, novel probabilistic forecasting methods based on deep learning techniques have been proposed, aiming at better capturing the high volatility of the total net system imbalance. In complement, for exploiting at best the predicted probabilistic information, tailored stochastic decision-support tools (i.e., stochastic programming and robust optimization method) are developed, for which a new data-driven approach was designed for continuously adjusting the risk policy of the market actor. The implementation of the developed approaches on real-world market data from the Belgian power system corroborates the key goal of the single price imbalance settlement, by showing that the market actor can increase its operating profit by optimizing its imbalance position, while reducing the total net system imbalance. Additionally, advanced neural networks architectures based on attention mechanisms demonstrate top forecasting performance for predicting the total net system imbalance. Finally, the data-driven approach for continuously adjusting the risk policy shows promising economic benefits in comparison with a static (determined once and for all) risk policy. The final research efforts of this work are devoted on interpretability of deep learning-based forecasting methods, which aim at accurately identifying the most important input features of the model and their interaction when returning the prediction. Combining the predictive power of deep neural models with interpretable outcomes is an essential step for fostering their practical adoption in the energy industry. To achieve interpretability in both feature and time dimensions, the attention-based neural architecture is here augmented with subnetworks dedicated to endogenously quantify the relative importance of each input feature. Outcomes using the data from the Belgian power systems show that adding interpretable components within the neural architecture does not hinder their prediction performance, while shedding light on its most important drivers.
... With the increasing demand for energy, it is estimated that the annual global power generation will exceed 38, 000 TWH by 2040. In order to meet this demand, long-distance largecapacity transmission technology is particularly important [1][2][3][4]. e stability and reliability of converter transformer is the key to the smooth progress of high voltage transmission, and the failure of UHV converter transformer valve-side bushing is one of the main reasons affecting the stability and reliability of converter transformer [5][6][7]. erefore, when the bushing fails, it should be replaced in time. ...
In this paper, a structure design scheme of intelligent replacement device for the ultrahigh voltage (UHV) converter transformer valve-side bushing is put forward, and its size is determined according to the actual size of domestic converter station valve hall and UHV converter transformer valve-side bushing. Moreover, the weak links in its working state are analyzed by finite element method to ensure the safety and reliability of the structure. Based on the spinor theory, the forward kinematics and Jacobian matrix model of the manipulator are established, and the analytical solution of inverse kinematics is derived. In order to analyze the accuracy of the intelligent replacement manipulator for the UHV converter transformer valve-side bushing, considering that the end actuator of the robot arm is under heavy load, the absolute positioning accuracy and repeated positioning accuracy are analyzed. In addition, the corresponding error model is established, the least square method is proposed to identify the error model, and the influence of the error caused by the load on the repetition accuracy is analyzed. Finally, the whole process simulation in ROS provides data support for the calculation of repetitive precision and verifies the feasibility of the intelligent replacement device for the UHV converter valve-side bushing.
... This means that the system offers an overall round trip efficiency (COP) of over 150%. Given the importance of electricity production compared to heat and cooling generation in smart energy systems, due to the current electrification trend of various energy sectors [18], researchers proposed a wise strategy to increase the electricity efficiency of TCAES, that is the combination of the TCAES with an organic Rankine cycle (ORC) [19]. The article proposing this hybrid configuration concluded that the electricity efficiency of the hybrid system might increase by about 20% in this way, reaching a peak value of about 45%. ...
... Keramidas et al. [18] Energy storage To present the importance of electricity production Alsagri et al. [19] TCAES-ORC To improve the electricity efficiency of the TCAES system Wang et al. [20] CAES-ORC To review the various combinations of CAES and ORC Alsagri et al. [21] TCAES To investigate the impact of the partial load operation Zhang et al. [22] CAES To study variable configuration and off-design conditions Zhao et al. [23] CAES To study the hybrid CAES system in the off-design conditions Wang et al. [24] CAES-ORC To present the performance of the hybrid system in the off-design conditions listed in Table 2. Based on the information given in Table 2, the peak pressure of the air in the cavern is 125 bar and the minimum allowable pressure of the air in the cavern is 25 bar. Also, Table 2 lists the primary design parameters of the ORC unit. ...
Trigeneration compressed air energy storage (TCAES) is one of the emerging solutions that will most likely find its market as a popular energy storage technology for sector coupling. The combination of a TCAES with an organic Rankine cycle (ORC) for waste heat recovery has also been found much effective for enhanced round trip efficiency and is thus to be preferred over conventional TCAES designs. The combined configuration is claimed to offer a very high coefficient of performance (COP) exceeding 1.5. This work aims to quantify realistic performance expectations from a combined TCAES-ORC subject to real operating conditions accompanied for instance by a wind turbine. The system is dynamically modeled, and its performance is analyzed regarding energetic/exergetic efficiency, environment, and economy. To make the investigations close to real-life conditions, a medium-sized 5 MW capacity TCAES-ORC unit is considered integrated with a wind farm for off-peak electricity utilization for storage and tri-generation of heat, cooling, and electricity when charging or discharging. The location of the use case is considered West Denmark, for which wind power production and pricing profiles are available. The results show that there is a considerable collapse in the cooling and power production when the system comes to low operating loads, while heat production potential is not significantly affected. The COP factor decreases from 1.5 in nominal mode to 1.26 in the off-design mode for a sample dynamic load, where the exergetic efficiency is reduced from 64% to 58%. With such a technical operation degradation, the levelized cost of storage (LCOS) is weakened from 141 €/MWh to 153.7 €/MWh, and the potential emission reduction will fall from 4163 to 3640 tonnes of equivalent CO2 per year.
... The 1973 oil crisis sparked public interest in energy issues, which are one of the most complex and controversial topics in society [1] and remain so today, especially when considering ambitious climate change mitigation targets that require radical changes in all economic sectors. There seems to be a consensus that, to achieve set climate targets, a rapid electrification of the economy as well as the decarbonization of the power supply are needed [2,3]. Energy planning models are commonly used to identify energy sector development pathways to reach set national/regional and environmental goals [4,5]. ...
In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel shift using the proposed approach is more gradual than the traditional one. Diesel cars are phased out by 2050 versus 2040. Furthermore, the proposed approach provided more realistic CO2 emissions, 7% lower emissions for 2018 than estimated based on statistical data, while traditional approach was 27% lower.