Energies

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. Full article

The individual debates on housing poverty, energy poverty, and mobility poverty for the most part overlook the interwoven nature of all three cost burdens, especially for low-income households. This study examines how the three cost factors interact on a household level, the consequences for those affected by cost burdens, and how they cope and negotiate their expenses. Our research comprises two sets of semi-structured interviews, one before and one during the energy crisis, to gain insight into household experiences and constraints. We found that the freedom to choose where and how to live largely determines how households heat their homes and organize their mobility. The housing crisis together with housing market mechanisms appears to be the main driver of this cost trap; and from here, a complex interplay of causal factors unfolds. Location, often conceived of in terms of an urban–rural divide, seems to be of secondary importance. The intersection of cost burdens results in stress, anxiety, and social exclusion, further limiting the capacity for coping. Full article

The European Union is committed to a 55% reduction in greenhouse gas emissions by 2030, as outlined in the Green Deal and Climate Law initiatives. In response to geopolitical events, the RePowerEU initiative aims to enhance energy self-sufficiency, reduce reliance on Russian natural gas, and promote hydrogen utilization. Hydrogen valleys, localized ecosystems integrating various hydrogen supply chain elements, play a key role in this transition, particularly benefiting isolated regions like islands. This manuscript focuses on optimizing a Centralized Green Hydrogen Production Facility (CGHPF) on the island of Crete. A mixed-integer linear programming framework is proposed to optimize the CGHPF, considering factors such as land area, wind and solar potential, costs, and efficiency. Additionally, an in-depth sensitivity analysis is conducted to explore the impact of key factors on the economic feasibility of hydrogen investments. The findings suggest that hydrogen can be sold in Crete at prices as low as 3.5 EUR/kg. Specifically, it was found in the base scenario that, selling hydrogen at 3.5 EUR/kg, the net profit of the investment could be as high as EUR 6.19 million, while the capacity of the solar and wind installation supplying the grid hydrogen facility would be 23.51 MW and 52.97 MW, respectively. It is noted that the high profitability is justified by the extraordinary renewable potential of Crete. Finally, based on our study, a policy recommendation to allow a maximum of 20% direct penetration of renewable sources of green hydrogen facilities into the grid is suggested to encourage and accelerate green hydrogen expansion. Full article

The establishment of a long-term, cross-border market in which forward market coupling and bilateral contracts are developed in an integrated approach is instrumental for the European internal electricity market. We propose the joint energy and transmission right auction (JETRA) mechanism, developed by O’Neill et al., as a solution for long-term cross-border markets in Europe. The main contribution of this research lies in its examination of the underlying market structures for effective JETRA implementation. We compare the institutional setting, market rules, and grid modeling under nodal and zonal pricing systems, adapting JETRA to the flow-based market coupling (FBMC) mechanism that is currently implemented in the European day-ahead market. This adaptation reveals the inherent limitations of FBMC in supporting JETRA, in particular in the long-term auction. We also identify constraints posed by existing European market rules, particularly those that affect the application of multi-settlement rules and the effective timeframe of hedging instruments. In conclusion, our research suggests that transitioning from zonal to nodal pricing is essential for JETRA’s effective implementation. Furthermore, a comprehensive market reform is required to seamlessly integrate long- and short-term markets. This paper is expanded from the previous research work of Huang and Deconinck. Full article

Hydrothermal geothermal energy, as a widely distributed, large reserve and easily exploitable renewable source, can be used for both power generation and building heating. In this study, a numerical simulation of heat extraction performance is conducted based on monitoring well temperature data in a geothermal field in Xiong’an New Area. The effects of the reinjection temperature, injection flow rate, and reinjection rate on the outlet temperature and the reservoir temperature during a long-term operation are analyzed. The results indicate that the lower reinjection temperature can generate a critical disturbance scope for the thermal reservoir during the exploitation process. The impact scope on the thermal reservoir can reach 210.3 m at a reinjection temperature of 25 °C, which is not conducive to maintaining the outlet temperature of production wells in the long-term. The reinjection rate significantly affects both the horizontal and vertical temperature fields of the thermal reservoir. Under reinjection conditions of 30 °C and 40 kg/s, the horizontal and vertical impact scope of the thermal reservoir are 262.3 and 588.5 m, respectively. The reinjection rate is inversely related to the outlet temperature. A decrease from 100% to 70% in the reinjection rate can increase the outlet temperature by 4.21%. However, a decrease in the reinjection rate will lead to a decline in the groundwater level. Therefore, balancing the variation in outlet temperature and groundwater level is crucial in practical engineering. Full article

Large-scale fluctuating and intermittent new energy power generation in a new power system is gradually connected to the grid. In view of the impact of the uncertainty of wind power on the spinning reserve capacity of thermal power units in the new power system’s day-ahead dispatching and reserve auxiliary service market, the original dispatching mode and intensity can no longer meet the system demand. To address this problem, the establishment of a wind power grid-connected new power system’s standby auxiliary service market reward and punishment assessment mechanism is undertaken to fundamentally reduce the demand for auxiliary services of the new power system pressure. In the first part of this paper, a two-stage optimal scheduling strategy is proposed for the first day of the year that takes into account the operational risk and standby economics. First, a data-driven method is used to generate the forecast value of the wind power interval before the day, and a unit start–stop optimization model (the first-stage optimization model) is established by taking into account the CvaR (conditional value at risk) theory to optimize the risk loss of wind abandonment and loss of load and the fuel cost of each unit, and an optimization algorithm is used to carry out the three scenarios and the corresponding four scenarios to optimize the configuration of the start–stop state and power output of each unit. The optimization algorithm is used to optimize the starting and stopping status and output of each unit for three circumstances and four corresponding scenarios. Then, in the second stage, a standby auxiliary service market incentive and penalty assessment model is established to effectively coordinate the sharing of rotating standby capacity and cost among thermal power units through the incentive and penalty mechanism so as to make a reasonable and efficient allocation of wind power output, curtailable load, and synchronized standby capacity. The new power system with improved IEEE30 nodes is simulated and verified, and it is found that the two-stage optimization model obtains a scheduling strategy that takes into account the system operating cost, standby economy, and reliability, and at the same time, through the standby auxiliary service market incentive and penalty assessment mechanism, the extra cost caused by standby cost mismatch can be avoided. This evaluation model provides a reference for the safe, efficient, flexible, and nimble operation of the new power system, improves the economic efficiency and improves the auxiliary service market mechanism. Full article

Dual-fuel low-temperature combustion is a possible solution for alleviating the tradeoff between oxides of nitrogen and soot emissions in conventional diesel combustion, albeit with poor combustion stability, high carbon monoxide, and unburned hydrocarbon emissions at low engine loads. The present work compares emissions and combustion (heat release and other metrics) of both diesel and poly-oxy methylene dimethyl ether as high-reactivity fuels to ignite natural gas while leveraging spray-targeted reactivity stratification, which involved multiple injections of the high-reactivity fuels. The experiments included six parametric sweeps of: (1) start of first injection, (2) start of second injection, (3) percentage of energy substitution of natural gas, (4) commanded injection duration ratio, (5) rail pressure, and (6) intake pressure. The experiments were performed on a 1.8 L heavy-duty single-cylinder research engine operating at a medium speed of 1339 rev/min. Not-to-exceed limits for the indicated oxides of nitrogen emissions, maximum pressure rise rate, and the coefficient of variation of the indicated mean effective pressure were set to 1 g/kWh, 10 bar/CAD, and 10%, respectively. The indicated emissions decreased and combustion improved significantly for both fueling combinations when the experimental procedure was applied. Full article

The LLC resonant converter, which is extensively utilized across various industrial fields, significantly depends on its parameters for performance optimization. This paper establishes a time-domain analytical model for the LLC resonant converter under Pulse Frequency Modulation (PFM) and proposes a multi-objective parameter optimization design method with stepwise constraints. The proposed method limits the resonant capacitor voltage while ensuring that the converter meets the voltage gain requirement and realizes Zero-Voltage Switching (ZVS). The converter’s performance is then optimized with the objective of minimizing the switching frequency range, the resonant inductor current, and the RMS value of the switching current on the secondary side. Compared with the existing methods, the proposed method has the advantages of comprehensive consideration and wide application scenarios. Finally, a 1200 W experimental prototype was fabricated, with experimental results verifying the feasibility of the proposed optimization design method and demonstrating that the prototype’s maximum efficiency reaches 96.54%. Full article

Hydrothermal carbonization (HTC) has recently emerged as a promising technology for converting diverse forms of waste with a high moisture content into value-added products such as biofuel, biochar, and activated carbon. With an increasing demand for sustainable and carbon-neutral energy sources, HTC has attracted considerable attention in the literature. However, a successful transition from laboratory-scale to large-scale industrial applications entails notable challenges. This review critically assesses the upscaling of hydrothermal carbonization processes, emphasizing the challenges, innovations, and environmental implications associated with this transition. The challenges inherent in upscaling HTC are comprehensively discussed, including aspects such as reactor design, process optimization, and the current treatment technology for process water. This review presents recent innovations and technological advancements that address these challenges and explores integrated solutions to enhancing hydrothermal carbonization’s scalability. Additionally, this review highlights key companies that have developed and implemented HTC plants for commercial purposes. By overcoming the obstacles and achieving advancements in the upscaling of hydrothermal carbonization, this review contributes to the ongoing efforts to realize the full potential of HTC as a sustainable and scalable biomass conversion technology and proposes future directions. Full article

In this paper, the application of the method of affinely adjustable robust optimization to a planning model of an energy system under uncertain parameters is presented, and the total scheduling costs in comparison with the deterministic model are evaluated. First, the basics of optimization under uncertain data are recapped, and it is described how these methods can be used in different applications for energy systems. This is followed by the methodology of adjustable robust optimization by defining the affinely adjustable robust counterpart. Finally, a numerical case study is conducted to compare the adjustable robust method with a rolling deterministic scheduling method. Both are implemented on a model of an energy system and compared with each other by simulation using real-world data. By calculating the total operating costs for both methods, it can be concluded that the adjustable robust optimization provides a significantly more cost-effective solution to the scheduling problem. Full article

Accurate well productivity prediction plays a significant role in formulating reservoir development plans. However, traditional well productivity prediction methods lack accuracy in tight gas reservoirs; therefore, this paper quantitatively evaluates the correlations between absolute open flow and the critical parameters for Linxing tight gas reservoirs through statistical analysis. Dominant control factors are obtained by considering reservoir engineering theories, and a novel machine learning-based well productivity prediction method is proposed for tight gas reservoirs. The adaptability of the productivity prediction model is assessed through machine learning and field data analysis. Combined with the typical decline curve analysis, the estimated ultimate recovery (EUR) of a single well in the tight gas reservoir is forecasted in an appropriate range. The results of the study include 10 parameters (such as gas saturation) identified as the dominant controlling factors for well productivity and geological factors that impact the productivity in this area compared to fracturing parameters. According to the prediction results of the three models, the R² of Support Vector Regression (SVR), Back Propagation (BP), and Random Forest (RF) models are 0.72, 0.87, and 0.91, respectively. The results indicate that RF has a more accurate prediction. In addition, the RF model is more suitable for medium and high-production wells based on the actual field data. Based on this model, it is verified that the productivity of low-producing wells is affected by water production. This study confirms the model’s reliability and application value by predicting recoverable reserves for a single well. Full article

Nowadays, the automotive market offers cars equipped with various types of drive systems, including a classic system in which the internal combustion engine burns fuel to power the vehicle, a hybrid system in which the internal combustion engine is supported by an electric machine (MHEV), and an electrical system in which energy to power the vehicle is drawn from the battery (BEV). Hybrid and electric vehicles have the ability to recover energy during deceleration and braking. Choosing a car with the right drive system is not easy for the average vehicle user. The article presents a comparison of vehicles with various drive systems. For this purpose, a diagnostic scanner was created that allows reading data from the car’s CAN network. The comparison of the obtained data allows for the presentation of more information about vehicles equipped with different drive systems. In particular, parameters such as energy consumption, the possibility of its recovery during braking and the instantaneous power of electric machines were subjected to comparative analysis. Vehicle tests were carried out on a chassis dynamometer in accordance with the WLTP test. Full article

Carbon capture and storage technologies play a crucial role in mitigating climate change by capturing and storing carbon dioxide emissions underground. Saline aquifers, among other geological formations, hold promise for long-term CO₂ storage. However, accurately assessing their storage capacity and CO₂ behavior underground necessitates advanced numerical simulation and modeling techniques. In this study, we introduce an approach based on a solubility thermodynamic model that leverages cubic equations of state offline from the simulator. This approach enables the precise prediction of CO₂–brine equilibrium properties and facilitates the conversion of compositional data into black oil PVT data suitable for black oil simulations. By incorporating industry-scale saline aquifer properties, we simulate a carbon storage scheme using the black oil model technique, significantly reducing computation time by at least four times while preserving the essential physical phenomena observed in underground carbon storage operations. A comparative analysis between black oil and compositional simulations reveals consistent results for reservoir pressure, CO₂ saturation distributions, and mass fraction of trapping mechanisms, with differences of less than 4%. This validation underscores the reliability and efficiency of integrating the black oil model technique into carbon storage simulations in saline aquifer formations, offering tangible benefits to industry operators and regulators by striking a balance between accuracy and efficiency. The capability of this approach to extend to temperatures of up to 300 °C and pressures of up to 600 bars broadens its applicability beyond conventional CCS applications, serving as a valuable tool for optimizing decision-making processes in CCS projects, particularly in scenarios where profitability may be marginal. Full article

Electric vehicles (EVs) are propelled by electric traction drive systems (ETDSs), which consist of various components including an electric motor, power electronic converter, and gear box. During manufacturing, end-of-line testing is the ultimate step for ensuring the quality and performance of electric motors in electric vehicle (EV) traction drive systems (ETDSs). The outcome of end-of-line testing of electric motors is significantly influenced by the tolerances of their structural parameters, such as stator inner and outer diameters, magnet dimensions, air gaps, and other geometric parameters. The existing literature provides insights into parametric sensitivity, offering guidance for enhancing the reliability of end-of-line testing. In this manuscript, the importance of end-of-line testing and the role of manufacturing tolerances of e-motor structural parameters in the manufacturing process of ETDSs are discussed. The impact of tolerances of e-motor structural parameters on the test results, such as torque, efficiency, back EMF, and e-NV (noise and vibration), is investigated. Finally, key challenges and research gaps in this area are identified, and recommendations for future research to mitigate the drawbacks of end-of-line testing are provided. Full article

This paper introduces a new concept in transmission expansion planning based on unconventional lines, termed “smart transmission expansion planning”. Traditionally, the domains of transmission expansion planning (TEP) and transmission line design are separate entities. TEP planners typically rely on the electrical specifications of a limited set of standard conventional line designs to evaluate planning scenarios, ultimately leading to the construction of the selected candidate line. In this context, it is noted that cost-effective scenarios often diverge from meeting the technical criteria of load flow analysis. To address this discrepancy, this paper proposes an alternative approach wherein TEP is conducted based on the specific requirements of the system earmarked for expansion. The transmission expansion planner initiates the process by determining optimal line parameter values that not only meet the operational criteria but also ensure cost-effectiveness. Subsequently, a line is designed to embody these optimal parameters. A detailed comparative analysis is conducted in this study, comparing the outcomes of TEP analyses conducted with conventional lines, unconventional lines, and lines featuring optimal parameters. Through extensive load flow analysis performed under normal and all single-contingency scenarios across three distinct loading conditions (peak load, dominant load representing 60% of peak load, and light load representing 40% of peak load), the results reveal that transmission lines engineered with optimal parameters demonstrate effective operation, with fewer transmission lines required to meet identical demands compared to other approaches. Full article

This work presents a production management platform developed to minimize the costs of the capacity charge, part of the electricity bill associated with the cost of maintaining grid capacity during periods of high, fluctuating loads. After a summary of the regulatory solutions on the capacity market in Poland, a capacity charge management system is presented, specifically designed for production facilities within the Energy-Intensive Industry sector. The proposed platform combines hardware data collection, a simulation tool analyzing the electrical energy demand profile to predict the future impact on the capacity charge, and a cloud-based user interface providing real-time recommendations to the plant operators regarding the corrective actions needed to minimize the cost of operation. It was pilot tested in collaboration with a large production facility in Poland, for which the capacity charge was among the main components of the electricity distribution costs. Pilot tests were conducted in the period from January 2022 to September 2023. The tested platform allowed us to shorten the time span of elevated capacity charges from 33% in the year 2022 to only 7% in the year 2023. It also reduced the benchmark capacity charge indicator by more than 11%, from 4.02% to −7.56%, over the duration of the experiments. This improvement was achieved without major changes to the organization and planning of the work. Full article

The purpose of this study is to examine a productive efficiency index that incorporates two new production factors of sustainability—an environmental variable as an undesirable output and a well-being indicator as a desirable output—for 12 years of data from 2007 to 2018 pertaining to 47 prefectures in Japan. This study proposes a combination of a new data envelopment analysis (DEA) intermediate approach with the DEA super-efficiency model to measure the integrated productive efficiency. The approach incorporates CO₂ emissions and a well-being indicator into the conventional productivity index. A three-stage analysis is conducted by sequentially adding new factors, CO₂ emissions, and a well-being indicator. We also conduct a club convergence analysis of the productive efficiency and observe how clubs are formed, what their characteristics are, and how the efficiency changes over time. Through these approaches, we examine the practicality of the new efficiency measure and discuss regional policy implications. We found that higher labor productivity and carbon productivity in major industries caused increased productive efficiency. Adding sustainability factors to the conventional production factors in efficiency measurement widened the efficiency gap among prefectures. Full article

In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids. Full article

For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested. Full article