查看更多>>摘要：Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage power uncertainty effectively in demand planning. Traditional reconciliation methods rely on in-sample errors for forecast reconciliation, which may not generalize well to future performance. Additionally, conventional aggregation structures do not always align with the decision-making requirements in practice, and evaluation metrics often neglect the economic impact of forecast errors. To address these challenges, this paper explores advanced cross-temporal forecasting models and their potential to enhance forecasting accuracy and decisions. First, we propose a novel approach that leverages validation errors, rather than traditional in-sample errors, for covariance matrix estimation and forecast reconciliation. Second, we introduce decision-based aggregation levels for forecasting and reconciliation, where certain horizons are tailored to the specific decisions required in operational settings. Third, we assess model performance not only by traditional accuracy metrics but also by their ability to reduce decision costs, such as penalties in ancillary services. Our results show that using validation errors improves the accuracy by more than 7 % across different temporal levels. We also demonstrate that statistical-based hierarchies tend to adopt less conservative forecasts and reduce revenue losses. On the other hand, decision-based reconciliation offers a more balanced compromise between accuracy and decision cost, while saving computational time by 2 %-3 % for simpler models and up to 93 % for more advanced models, making them attractive for practical use.
查看更多>>摘要：In the wake of escalating interest in sustainable transportation, electric vehicles (EVs) have emerged as a pivotal solution. With their rising prominence, the need for expeditious and effective charging solutions has intensified. However, current paradigms fail to deal with fast charging system integration issues into the existing power grid. EV fast charging, power mismatch amelioration, and pioneering optimization are studied in the paper. In the case when the local grid's power capacity is not enough to support the total power demand of many fast-charging stations, power mismatch is addressed. Whereas the complexities of battery longevity and how fast they are all studied. As a means to equitably distribute load among the stations, we study the problem of peer-to-peer (P2P) charging localization. Using dynamic power allocation, we apply the Dragonfly algorithm to improve P2P localization and cope with power mismatch. At the same time, a new Deep Q Network (DQN) model modifies charging strategies upon real-time conditions of the grid. DA is empirically studied on different sizes and commonly used shapes of charging stations with different power mismatch levels, and it is demonstrated that the DA can considerably eliminate the power mismatches, optimize the charging station allocation, and enhance the grid resiliency. Additionally, the DQN model adapts to the fluid grid dynamics and improves charging efficiency. As a result of these innovations, a charging optimization framework beyond conventional charging optimization and a charging optimization scheme that is consistent with the grid infrastructure are provided. Through this new paradigm, we supply our contribution to sustainable energy transportation based on efficient EV charging, lowered grid stress, and maximal power utilization.
查看更多>>摘要：The solar photovoltaic (PV) conversion efficiency decreases as the solar cell temperature rises, at a reduction rate of approximately 0.5 %/degrees C. Phase change materials (PCMs) have emerged as a promising solution for passive thermal management of solar PV panels, providing effective and uniform cooling to enhance PV conversion efficiency. In current research on PV-PCM systems, organic solid-liquid PCMs (SLPCMs), particularly paraffin, are the primary focus. However, their application is limited by issues such as liquid leakage and large volume changes. To address these issues, solid-solid PCMs (SSPCMs) are successfully fabricated and pioneeringly introduced for effective cooling of solar PV panels in this paper. SSPCMs utilizing polyethylene glycol (PEG) with varying molecular weights are prepared through a simple and reliable two-step method, demonstrating outstanding self-healing properties. The test results indicate that the prepared PEG-based SSPCMs exhibit excellent leak-proof characteristics, with no leakage observed at 80 degrees C within 2 h. Their phase transition temperature ranges from 55 degrees C to 67 degrees C, with latent heat between 137 J/g and 151 J/g, and a thermal conductivity of approximately 0.34 W/(m & sdot;K) at 25 degrees C. The experimental results of cooling PV panels reveal that, compared to bare PV, PV panel using only SSPCM can achieve a temperature decrease of up to 11.2 degrees C. Moreover, PV panel with aluminum fin-enhanced SSPCM can achieve a greater decrease in temperature of 16.5 degrees C. The electrical performance test results demonstrate that finned PV-SSPCM effectively enhances power output. These findings suggest that aluminum fin-enhanced SSPCM presents a competitive option for PV cooling.
查看更多>>摘要：As a strategic pathway to decarbonization, electrification provides an effective solution for achieving low or even net-zero carbon emissions in transportation systems. Static wireless charging (SWC) presents a promising charging strategy by utilizing the dwell time of battery electric buses (BEBs) at bus stations. This approach can effectively mitigate operational challenges such as limited range and prolonged charging times for connectedbattery electric buses (C-BEBs). This study proposes a collaborative optimization model for C-BEB scheduling and charging considering arterial coordination under connected environment. The model uses mixed integer linear programming (MILP) to develop an arterial signal coordination control approach that considers the operation efficiency of regular vehicles (RVs) and the constraints that C-BEBs passing through signalized intersection without stopping. Additionally, a collaborative optimization model for bus scheduling and charging is constructed, which considers bus headway, bunching, passenger demand, non-stop constraints for C-BEBs, and speed and stop duration limits. This model can enable C-BEBs to flexibly adjust stop time for wireless static charging while maintaining good operational efficiency and planning. Simulation experiment based on real bus lines in Hangzhou shows that the proposed model can charge buses that lack power during operation with almost no impact on overall efficiency. This not only enhances the operational stability of the bus system but also reduces the need for additional backup buses prepared for situations where buses terminate mid-route due to power shortages, thereby lowering the purchasing costs for bus operators.
查看更多>>摘要：Urban carbon emissions have garnered significant attention following the establishment and implementation of global carbon neutrality goals. Accurate carbon accounting in urban areas is crucial for formulating effective emission reduction strategies and assessing their effectiveness. However, the mixed-use nature of urban land presents significant challenges to precise carbon accounting. This study adopts the perspective of urban community living circles and leverages optimization algorithms and the inventory method to propose a novel carbon accounting method that addresses these challenges. Furthermore, this method's reliability was validated through an analysis of eight land parcels with varying spatial configurations in the Beijing area, along with comprehensive sensitivity analyses, while its practical applications were also explored. The findings are as follows: (1) The urban block-scale carbon accounting method optimizes population allocation across industries and incorporates land-use policies into objective function constraints, enhancing both accuracy and applicability. (2) In Beijing's functional core area, annual carbon emissions within the 5-15-min standard-scale living circles were 8.43 ktC, 24.95 ktC, and 151.95 ktC, respectively, with an emission intensity of 810.42 tC/ha in the 10-15-min zone. (3) Urban functional and residential land collectively accounted for approximately 97 % of total emissions, with urban functional land exerting a greater impact. This study presents a reliable, robust, and systematic urban block-scale carbon accounting method that integrates carbon management with land-use policies, demonstrating significant practical value.
查看更多>>摘要：As distributed photovoltaic (PV) penetration in distribution networks (DNs) is increasing, it is essential to assess the PV hosting capacity (PVHC) to ensure the safe operation of DNs. This paper proposes a data-driven distributionally robust joint chance constrained (DRJCC) distribution networks PVHC assessment framework. Firstly, the spatiotemporal attention, projection, supervision, and Transformer architecture-based generative adversarial blocks are introduced to develop an augmented time series generative adversarial network (ATS-GAN), which, by integrating both supervised and unsupervised learning during the joint training process, better captures the spatiotemporal characteristics of PV and load power. Subsequently, leveraging the ATS-GAN, a Wasserstein metrics-based ambiguity set of PV and load power probability distributions is constructed, centered on the distributions induced by the generator neural network. Secondly, the DRJCC PVHC assessment model is proposed. A combination of the Bonferroni inequality and conditional value-at-risk approximation is adopted to transform the multivariate DRJCC model into a tractable conic formulation for efficient computation. Numerical results demonstrate that the proposed method effectively captures the spatiotemporal characteristics and uncertainties of multivariate distributions under multiple constraints, significantly reducing the conservatism typically associated with distributionally robust individual chance constraints.
查看更多>>摘要：Despite the significant advantages of fuel cell (FC) vehicles in reducing urban air pollution and extending driving range, effectively managing their internal energy systems remains a major challenge. To maximize the operational efficiency and lifespan of the FC system without compromising fuel economy, this paper proposes a novel predictive energy management paradigm guided by deep reinforcement learning. This strategy innovatively integrates driving intention speed prediction and health-aware control. Specifically, we developed a multi-input bi-directional long short-term memory (BiLSTM) predictor incorporating driving intentions (DI-BiLSTM) using the fuzzy C-means algorithm to enhance the prediction accuracy of future vehicle state trajectories. Downstream control decisions are executed through an improved deep deterministic policy gradient (DDPG) algorithm, which optimizes action space selection based on the degradation characteristics of the FC system. Additionally, during the training and validation phases of the energy management strategy (EMS), we utilized high-quality driving data collected from real bus routes using a high-performance Beidou integrated navigation system, replacing conventional standard driving cycles to enhance the strategy's generalization ability across different scenarios. The results indicate that, compared with conventional prediction model relying solely on historical speed data, the DI-BiLSTM improves prediction accuracy by at least 7.86 % over 3 s, 5 s, and 8 s prediction horizons. Compared with conventional DDPG-based EMS, the proposed EMS increases the average efficiency of the FC system by 32.18 % and extends its lifespan by 16.50 %. In terms of overall driving costs, the proposed EMS improves driving economy by 9.97 % compared with conventional DDPG-based EMS.
查看更多>>摘要：The integration of renewable energy sources and multi-energy networks in integrated energy systems (IES) introduces significant challenges related to energy degradation, driven by exergy losses during energy conversion/ transmission and uncertainty-induced usability reduction. To address these issues, this study proposes a novel entropy state calculation model and analytical framework for assessing energy quality degradation within IES. By unifying thermodynamic entropy (quantifying physical exergy loss) and information entropy (capturing uncertainty-driven energy mismatch), the model integrates physical and information systems into a cohesive entropy state framework. The methodology is validated through a case study on a real-world IES in Tianjin, China (TJBC), demonstrating its capability to reveal entropy state distributions across subsystems under varying network structures and operational modes. Results highlight the dominance of energy conversion processes (e.g., combined heat and power units) in system-wide entropy increase and the critical role of renewable uncertainty in local energy quality degradation. The proposed framework provides a unified metric for optimizing energy efficiency, guiding infrastructure planning, and mitigating energy degradation in high-renewable-penetration IES, contributing to sustainable and high-quality energy system development.
查看更多>>摘要：The performance and durability of proton exchange membrane water electrolysis (PEMWE) are influenced not only by the intrinsic properties of components but also by various operational conditions. The porous transport layer (PTL), a key component in water management, has received limited research attention regarding its flowinduced degradation, particularly from a corrosion perspective. Due to the inherent challenges of in situ characterization, this study systematically investigates the degradation of sintered titanium fiber felt under ex-situ conditions, replicating flowing PEMWE anodic environment as closely as possible. The results demonstrate that increasing stirring speeds significantly improves corrosion resistance, as evidenced by reduced irregular corrosion during potentiodynamic polarization. This improvement is primarily attributed to enhanced passivation - facilitated by accelerated point defects dynamics and incorporation of reactive species-which overweights the dissolution effect associated with increased F- concentration. During potentiostatic polarization, a higher current response and fewer current transients under increasing stirring speeds indicate a more intensified passivation process. Notably, the passive film formed under the static condition exhibits stronger F-adsorption, which is mitigated under stirring conditions.
查看更多>>摘要：Simultaneous charging of electric vehicles (EVs) increases peak demand, potentially causing higher electricity prices and increased procurement costs for charging, making EVs less economically appealing. Smart charging addresses this challenge by utilising EVs as flexible assets, adjusting their charging behaviour in response to both power system conditions and user requirements. In our paper, we take the perspective of an energy provider using smart charging algorithms to reduce their electricity procurement costs (EPC) by charging the EVs when the electricity prices are lower. However, EV usage uncertainties introduce variability in the flexibility EVs provide and subsequently impact the energy providers' EPC when trading in electricity markets. Our paper considers uncertainties arising due to variable driving patterns and charging preferences. Within the charging preferences, we specifically focus on two charging preferences such as a minimum state of charge (SOCmin) requirement-the percentage of the battery up to which EV needs to be charged immediately at full power when connected to the charging point; and the frequency of EV connection to the charging point-how often EV users connect their EV to the charging point. We develop a flexibility model that quantifies the flexibility in terms of energy and power as a function of time. To calculate the energy provider's EPC, we develop a scenario-based robust optimisation model, minimising the energy provider's EPC while trading in German day-ahead and intraday markets. As expected, an increase in SOCmin requirements and a decrease in frequency of EV connections results in reduced EV flexibility and subsequently increases the EPC. However, our cost sensitivity analysis reveals that even with an 80 % SOCmin, EPC can be reduced by up to 33.5 % and 36.9 % for the years 2022 and 2023, respectively, compared to fully uncontrolled charging. When EVs offer full flexibility (0 % SOCmin ), the cost reduction is only slightly higher, at around 43.6 % and 49.6 % for the years 2022 and 2023, respectively. Flexible EV charging, even with low flexibility, thus possesses high economic value, allowing energy providers to achieve substantial monetary gains with minimal impact on user convenience.
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