This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
In this paper, we propose a data-driven Evolutionary Game-Based Model Predictive Control (EG-MPC) framework for the energy dispatch of a hybrid renewable energy system powering an autonomous ship. The system integrates solar photovoltaic and wind turbine generation with battery energy storage and diesel backup power to ensure reliable operation. Given the uncertainties in renewable generation and dynamic energy demands, an optimal dispatch strategy is crucial to minimize operational costs while maintaining system reliability. To address these challenges, we formulate a cost minimization problem that considers both battery degradation costs and diesel fuel expenses, leveraging real-world data to enhance modeling accuracy. The EG-MPC approach integrates evolutionary game dynamics within a receding-horizon optimization framework, enabling adaptive and near-optimal control solutions in real time. Simulation results based on site-specific data demonstrate that the proposed method achieves cost-effective, reliable, and adaptive energy dispatch, outperforming conventional rule-based and standard MPC approaches, particularly under uncertainty.
Mona Bielig, Lukasz Malewski, Karol Bandurski
et al.
Heating and hot water usage account for nearly 80% of household energy consumption in the European Union. In order to reach the EU New Deal goals, new policies to reduce heat energy consumption are indispensable. However, research targeting reductions concentrates either on technical building interventions without considerations of people's behavior, or psychological interventions with no technical interference. Such interventions can be promising, but their true potential for scaling up can only be realized by testing approaches that integrate behavioral and technical solutions in tandem rather than in isolation. In this research, we study a mix of psychological and technical interventions targeting heating and hot water demand among students in Polish university dormitories. We evaluate effects on building energy consumption, behavioral spillovers and on social beliefs and attitudes in a pre-post quasi-experimental mixed-method field study in three student dormitories. Our findings reveal that the most effective approaches to yield energy savings were a direct, collectively framed request to students to reduce thermostat settings for the environment, and an automated technical adjustment of the heating curve temperature. Conversely, interventions targeting domestic hot water had unintended effects, including increased energy use and negative spillovers, such as higher water consumption. Further, we find that informing students about their active, collective participation had a positive impact on perceived social norms. Our findings highlight the importance of trialing interventions in controlled real-world settings to understand the interplay between technical systems, behaviors, and social impacts to enable scalable, evidence-based policies driving an effective and sustainable energy transition.
[Introduction] In order to test the applicability of sea surface wind field data set in Yangjiang offshore wind farm area, this paper tests and evaluates the 10 m wind field of daily gridded advanced scatterometer (DASCAT), European centre for medium-range weather forecasts reanalysis v5 (ERA5) and final reanalysis data (FNL). [Method] The research was based on the 10 m wind fields at four sites in the Yangjiang offshore wind farm area in Guangdong province. [Result] The results demonstrate five pionts: (1) The wind speed correlations are above 0.8, with ERA5 the highest. The RMSE of wind speed are within 2.6 m/s, with DASCAT being the best in SWZ and ERA5 being the best in DWZ. Both FNL and ERA5 show significant underestimation of wind speed in SWZ, while DASCAT has smaller mean wind speed deviation than those of FNL and ERA5, and ERA5 mean wind speed is closer to those of observation. (2) The wind direction correlation reaches more than 0.75, with ERA5 being the highest in SWZ and FNL being the highest in DWZ. The RMSE of wind direction are within 35°, and ERA5 errors are the minimum. However, DASCAT and FNL are both closer to the observed predominant wind direction. (3) The statistics of the RMSE of wind speed in each wind speed period show that FNL is the minimum in the low wind speed period while DASCAT is the minimum in the medium and high wind speed periods in SWZ. ERA5 is the minimum in all wind speed period in DWZ. In both SWZ and DWZ, ERA5 has the highest wind speed correlation in both the low and medium wind speed periods, and FNL has the highest wind speed correlation in the high wind speed period. (4) The monthly wind speed errors of DASCAT and ERA5 are small and distributed relatively close to each other in SWZ, with peaks in April-May and October; ERA5 errors are smallest in DWZ, with a peak in July and a trough in December-January of the following year. (5) The distribution characteristics of the multi-year average wind speed for 10 m show that the wind speed increases from north to south and from west to east, and the wind speed gradient is large in SWZ. [Conclusion] Overall, the 10 m sea surface wind field data set of ERA5 performs better in the study area, and the deficiency of its systematic low mean wind speed can be corrected by DASCAT.
Mir Toufikur Rahman, Mingchen Gu, Moudud Ahmed
et al.
Abstract Inverter‐based distributed generators (IBDGs), mainly solar photovoltaic, connected in medium‐voltage (MV) networks cause challenges, such as voltage limit violations, for distribution network service providers (DNSPs), and require advanced network management strategies to mitigate these challenges. A theoretical analysis of the voltage limit‐induced barrier to IBDG connection and their export limits due to the change in network characteristics is imperative for developing new strategies. The authors formulated a relationship between the network equivalent impedance and the IBDG's connection point in the network and further explored the link between the network equivalent impedance and voltage magnitude due to the IBDG connection point. The authors also assessed the voltage limit‐induced barrier to IBDG connections in MV networks and proposed solutions to overcome issues with the dynamic export limit of IBDGs. Four representative Australian MV networks are analysed in DIgSILENT PowerFactory under different scenarios, such as variation in IBDG location and static and dynamic export limits. The authors found that an IBDG connected at the end of the network can achieve better performance in supporting the network voltage. An IBDG with a dynamic export limit can export three times more energy than the static export limit, which benefits both the DNSPs and IBDG owners.
Production of electric energy or power. Powerplants. Central stations, Energy industries. Energy policy. Fuel trade
[Introduction] Combining mesoscale numerical model and large eddy simulation (LES) model, numerical sumulation of sub-kilometer-scale project unit placement is carried out, which takes into account atmospheric boundary layer changes. It provides offshore wind turbine projects with high-efficiency power generation placement schemes. [Method] This study converted the mesoscale numerical weather simulation results into boundary conditions for the input of the LES model and introduced model parameters reflecting the operation of an actual wind farm into the LES simulation. The numerical sumulation experiments of the ambient wind field in the wind farm region was carried out under the consideration of the change of the actual atmospheric boundary layer, and the results of the refined simulation scheme of this wind field were evaluated based on the observation data collected from the wind farm. [Result] The simulation results indicate that by converting the results of the mesoscale weather model into the dynamic drive which is read by the LES model and simulating the wind field where the wind farm is located based on the model, the simulation results are able to replicate the changes in the external wind field after passing through the wind farm and the wake generated within the wind turbine fleet, as well as its impact on the internal wind field of the wind farm. The root mean square error of wind speed simulation at the hub of wind turbines is 1.54 m/s. [Conclusion] The refined wind field simulation scheme, which takes into account the variation of mesoscale meteorological elements and the impact of wind farms on the ambient wind field, can provide guidance for the design phase of actual projects.
High-pressure pipeline storage presents a promising method for widespread and efficient hydrogen transfer. However, challenges arise in mitigating pressurized hydrogen leakage due to hydrogen embrittlement issues associated with conventional pipeline materials. Experimental findings indicate that pressurized hydrogen is prone to spontaneous combustion, even at relief pressures as low as approximately 2 MPa - well below the permissible pipeline pressure in most countries. Despite this, there remains a lack of consensus regarding the mechanism of spontaneous ignition from high-pressure hydrogen leakage, and current research in this area is deemed insufficient. This study aims to analyze and discuss the presumed mechanisms of spontaneous ignition comparatively, review the progress in the study of spontaneous ignition of hydrogen in high-pressure leakage based on diffusion ignition theory, and statistically compare and discuss the influences of significant factors existing in pipelines (e.g., macro size factors and internal structure) and/or pipe failures (e.g., rupture factors) on spontaneous ignition. It is hoped that this article will provide scholars involved in the development of hydrogen energy and the theories of spontaneous combustion with a systematic understanding of these phenomena.
Fuel, Energy industries. Energy policy. Fuel trade
Hafiz Muhammad Athar Farid, Shamaila Iram, Hafiz Muhammad Shakeel
et al.
Energy efficiency in buildings is an essential aspect of sustainable development and efforts to mitigate climate change. Integrated energy management has the capacity to greatly improve energy efficiency at several levels, but it requires the sharing and analysis of energy performance data from various stakeholders. This study explores the various components of improving energy efficiency in buildings by combining and analysing the important factors highlighted in previous studies. It gives a comprehensive examination of the development of stakeholder theory and presents a concise overview of the existing literature on stakeholder engagement in the field of energy efficiency. The research emphasises the importance of stakeholders considering a number of criteria to ensure the successful implementation and sustainable profitability of energy-efficient solutions, given the complex nature of energy management and the participation of multiple potential stakeholders. This study provides realistic recommendations to stakeholders for the effective selection of these factors, grounded in a comprehensive review of the existing literature. Moreover, the study emphasises the diverse challenges and limitations linked to stakeholder involvement in the energy efficiency industry, pinpointing crucial topics for future investigation and enhancement.
Determining transportation routes is of great importance for advancing China-Russian gas cooperation, which has been hanging in the balance. This paper employs a general equilibrium model to assess the congestion cost of diversified gas transmission schemes between China and Russia on domestic long-distance pipelines, which addresses the inherent limitations of exclusively considering transport impacts in the decision-making process. Findings reveal a notable decrease in pipeline congestion incidents for the route integrated with the Shaan-Jing system, thus lending empirical credence to the feasibility of the proposed scenario involving Mongolia. In addition, key routes are defined in this paper based on congestion costs, which emphasize the challenges posed by bilateral gas cooperation to China's long-distance natural gas pipeline network, necessitating a strategic focus on critical pipelines as the top priority of pipeline optimization efforts in the future. This paper provides valuable insights into planning the second China-Russia gas pipeline and the decision-making process for the future development of long-distance pipeline infrastructure.
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy to dynamically balance the trade-off between voltage fluctuation control and renewable accommodation. To further extract multi-time-scale spatial-temporal (ST) graphical information of a DN, our strategy draws on a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST convolution to explore the node correlations in the spatial and temporal views. The continuous decision-making process of balancing such a trade-off can be modeled as a Markov decision process optimized by the deep deterministic policy gradient (DDPG) algorithm with the help of the derived ST information. We validate our strategy on the modified IEEE 33, 69, and 118-bus radial distribution systems, with outcomes significantly outperforming the optimization-based benchmarks. Simulations also reveal that our developed MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and improve its performance in stabilizing node voltage in an RER-rich DN. Moreover, our method improves the DN's robustness in the presence of generator failures.
Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi, Adita Sultana
et al.
The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. Employing the ARDL framework integrated with the STIRPAT model, the findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. Unit root tests (ADF, PP, and DF-GLS) confirm mixed integration levels among variables, and the ARDL bounds test establishes long-term co-integration. The analysis highlights that AI innovation positively correlates with CO2 reduction when environmental safeguards are in place, whereas GDP growth, energy consumption, FDI, and urbanization intensify CO2 emissions. Robustness checks using FMOLS, DOLS, and CCR validate the ARDL findings. Additionally, Pairwise Granger causality tests reveal significant one-way causal links between CO2 emissions and economic growth, AI innovation, energy use, FDI, and urbanization. These relationships emphasize the critical role of AI-driven technological advancements, sustainable investments, and green energy in fostering ecological sustainability. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.
Abstract Battery-based electric vehicles (BEVs) in the United States (U.S.) set a new sales record in 2022, driven by technology, policy, environmental, and economic objectives. However, the rapid deployment of BEVs and charging infrastructure without a careful review of their integration with the electric grid can have negative economic impacts on reliable and resilient electricity supply. Bi-directional power transfer (Bi-Di) vehicle-grid integration technologies and services such as vehicle-to-home or building (V2H/B) and vehicle-to-grid (V2G) can potentially lower local and system peak demand, improve economics for grid operators, and benefit BEV customers. Original equipment manufacturers (OEMs) in the automotive industry are exploring technologies and economics (techno-economics) for Bi-Di services. The study conducted a literature review of eleven case studies in the U.S. and Europe that featured Bi-Di demonstrations from 2005 to 2022 to highlight insights and techno-economic opportunities and challenges for OEMs. The findings should motivate the OEMs to prioritize technology innovation and business models to increase BEV sales and gain continuous revenue from Bi-Di services, which can potentially transition "car makers" to "technology solution" companies.
Lithium (Li) metal batteries (LMBs) are regarded as promising next-generation rechargeable batteries owing to the high theoretical specific capacity and the lowest potential of the Li metal anode (LMA). Nevertheless, the practical applications of LMA have been restricted by uncontrollable Li dendrite growth, enormous volume change and unstable interfaces between LMA and electrolyte. Among all the available strategies, the rational designs of artificial interphase layers (AILs) are the promising methods to solve these problems at the interfaces between LMA and electrolyte. In this review, we generally summarize the recent typical examples of in/ex-situ formed AILs for stabilizing LMA/electrolyte interfaces. Particular considerations have been taken on the components and structure characterizations in the design principle of AILs for suppressing uncontrollable Li dendrites growth, constructing stable interfaces and addressing the huge volume variation. Finally, the remaining challenges and the research direction for high performance AILs for safe and stable LMBs are provided.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Abstract Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit‐maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha−1 for switchgrass production at age 2. The mean profit‐maximizing N rate increases with age for miscanthus, peaking at 151 kg ha−1 at age 11 before declining to 114 kg ha−1 at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha−1 for middle‐aged stands and declines to 114 kg ha−1 at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Abstract In photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510.
Production of electric energy or power. Powerplants. Central stations, Energy industries. Energy policy. Fuel trade
The direct use of detailed chemical kinetics in combustion simulations is limited by the extremely high computational costs. Recently, Owoyele and Pal (Energy and AI, 2022), proposed the neural ordinary differential equations (NODE) method to accelerate calculations of chemical kinetics and proved its effectiveness in zero-dimensional calculations of hydrogen combustion considering 9 species and 21 reactions. However, its performance for more realistic high-dimensional calculations and more complex kinetic systems remains unexplored. Therefore, this study further applies the method for more complex chemical kinetics of ammonia combustion, especially with optimizations in the data sampling, model training strategies, and model application methods that remedy the problems of versatility and application to more practical simulations. The newly developed NODE models are comprehensively validated in the zero-dimensional calculations of ammonia auto-ignition, one-dimensional calculations of laminar freely-propagating ammonia-premixed flames, and two-dimensional direct numerical simulation (DNS) of ammonia-premixed flames in a temporally evolving jet. Present NODE models focus on seven chemical species, namely NH3, O2, H2, OH, H2O, N2, and NO, and the results show that, compared with the results obtained by using detailed chemical kinetics, this method is able to reduce the computational costs of the zero-dimensional auto-ignition reaction to 1/24 while reproducing the ignition delay time for a wide range of initial temperatures and equivalence ratios with relatively good accuracy. Additionally, the method is able to reduce the computational costs of the one-dimensional freely propagating flame and two-dimensional jet flame to 1/4 and 1/38 respectively, while acceptable reproduction of the laminar flame speed and temporal evolution of the gas temperature and mass fractions of the interested species can be achieved.
Fuel, Energy industries. Energy policy. Fuel trade
To realize the safe, economical and low-carbon operation of the pelagic island microgrid group, this paper develops a bi-level energy management framework in a joint energy-reserve market where the microgrid group (MG) operator and renewable and storage aggregators (RSA) are independent stakeholders with their own interests. In the upper level, MG operator determines the optimal transaction prices with aggregators to minimize MG operation cost while ensuring all safety constraints are satisfied under uncertainty. In the lower level, aggregators utilize vessels for batteries swapping and transmission among islands in addition to energy arbitrage by participating in energy and reserve market to maximize their own revenue. An upper bound tightening iterative algorithm is proposed for the formulated problem with nonlinear terms and integer variables in the lower level to improve the efficiency and reduce the gap between upper bound and lower bound compared with existing reformulation and decomposition algorithm. Case studies validate the effectiveness of the proposed approach and demonstrate its advantage of the proposed approach in terms of optimality and computation efficiency, compared with other methods.
This paper proposes a novel optimal Energy Management System (EMS) algorithm for Electric Vehicle (EV) charging in smart electric railway stations with renewable generation. As opposed to previous railway EMS methods, the proposed EMS coordinates the combined Regenerative Braking Energy (RBE), renewable generation, electric railway demand and EV charging demand at the EV parking lot of the railway station. Numerical results using a scenario-based approach on an actual railway station in Chur, Switzerland demonstrate that the proposed algorithm can effectively minimize the expected daily operating cost for the train station over an entire year.