Applied Energy最新文献

The weak spots in an integrated energy system that may jeopardize the overall reliability call for timely and efficient Inspection and Maintenance (I&M). One core step is the reasonable allocation and deployment of limited I&M personnel or apparatus to the regions or periods with higher event risks, which requires a pinpoint spatiotemporal distribution forecast of future vulnerabilities. This paper presents a hybrid forecast methodology, the Saliency-Rough Fuzzy Utility Pattern recognition ensemble, in light of space-air-ground multi-source-heterogeneous input data. A parallel learning architecture is established and identifies the critical components with higher yields to enhance efficiency. Accordingly, more reasonable quantitative and qualitative evaluations can be carried out concurrently. Potential imprecise and uncertain data scenes are handled in quantitative assessments, both the failure hazard path sets and survival function likelihood boxes are incorporated in the designed relative path-Fussell Vesely Saliency (rp-FVS) model; and in qualitative analyses, the underlying perilous components can be distinguished via a combination of the variable precision-rough model. The rp-FVS-based fuzzy inference logic configures all membership functions identically according to components’ impacts. These two parts are integrated into the rough-fuzzy Utility Measure to discover concealed component-vulnerability interconnection patterns. Finally, an empirical case study is conducted for validation.

Developing and deploying renewables in existing energy systems are pivotal in Europe’s transition to net-zero emissions. In this transition, gas turbines (GTs) will be central for balancing purposes. However, a significant hurdle in minimising emissions of GTs operating in combination with intermittent renewables arises from the reliance on unreliable meteorological forecasts. Here, we propose a hierarchical framework for decoupling this operational problem into a balancing and emissions minimisation problem. Balancing is ensured with a high-level stochastic balancing filter (SBF) based on data-driven stochastic grey-box models for the uncertain intermittent renewable. The filter utilises probabilistic forecasting and less conservative chance constraints to compute safe bounds, within which a proposed low-level economic predictive controller further minimises emissions of the GTs during operations. As GTs exhibit semi-continuous operating regions, complementarity constraints are utilised to fully exploit each GT’s allowed operational range. The proposed method is validated in simulation for a gas-balanced hybrid renewable system with batteries, three GTs with varying capacities, and a wind farm. Using real historical operational wind data, our simulation shows that the proposed framework balances the energy demand and minimises emissions with up to 4.35% compared with other conventional control strategies in simulation by minimising the GT emissions directly with complementarity constraints in the low-level controller and indirectly with less conservative chance constraints in the high-level filter. The simulations show that the computational cost of the proposed framework is well within requirements for real-time applications. Thus, the proposed operational framework enables increased renewable share in hybrid energy systems with GTs and renewable energy and subsequently contributes to de-carbonising these types of isolated or grid-connected systems onshore and offshore.

Comprehensive dispatch of integrated electric and heating systems (IEHS) has shown great potential in promoting energy utilization efficiency, where an effective modelling of district heating system (DHS) is crucial. Herein, a new frequency-domain-based model is proposed to reflect full heat transport dynamics in DHS considering variation of both temperatures and fluid flow rates. Numerical tests on a highly branched DHS demonstrate the high accuracy of the proposed model under various flow conditions. The proposed model has a general error less than 1K and outperforms the popular node method and finite difference method with less temporal sampling points. A primal-decomposition-based rolling-horizon approach is also proposed to optimize the IEHS in variable flow and variable temperature (VF-VT) mode using the new physical model. The results on a 45-node IEHS show the effectiveness of the proposed model and optimization approach, where the total operation cost is reduced by 3.4% compared with optimization without regulation of flow rates.

In response to the COVID-19 pandemic, there has been a notable shift in literature towards enhancing indoor air quality and public health via Heating, Ventilation, and Air Conditioning (HVAC) control. However, many of these studies simplify indoor dynamics using ordinary differential equations (ODEs), neglecting the complex airflow dynamics and the resulted spatial–temporal distribution of aerosol particles, gas constituents and viral pathogen, which is crucial for effective ventilation control design. We present an innovative partial differential equation (PDE)-based learning and control framework for building HVAC control. The goal is to determine the optimal airflow supply rate and supply air temperature to minimize the energy consumption while maintaining a comfortable and healthy indoor environment. In the proposed framework, the dynamics of airflow, thermal dynamics, and air quality (measured by CO${}_2$ concentration) are modeled using PDEs. We formulate both the system learning and optimal HVAC control as PDE-constrained optimization, and we propose a gradient descent approach based on the adjoint method to effectively learn the unknown PDE model parameters and optimize the building control actions. We demonstrate that the proposed approach can accurately learn the building model on both synthetic and real-world datasets. Furthermore, the proposed approach can significantly reduce energy consumption while ensuring occupants’ comfort and safety constraints compared to existing control methods such as maximum airflow policy, model predictive control (MPC) with ODE models, and reinforcement learning.

Solar energy-powered electrolytic water splitting represents a promising avenue for hydrogen production. However, current technologies for solar-driven hydrogen generation still face the challenges such as low efficiency and significant fluctuations in solar energy availability. This paper proposes a full-spectrum solar hydrogen production system integrated with spectral beam splitting technology and chemical energy storage to address these issues. The high-grade solar energy is allocated for generating electricity through photovoltaic cells, while the low-grade solar energy is utilized in the dry reforming of methane (DRM) process to produce syngas, which in turn is used for flexible electricity generation. Dispatchable electricity converting from syngas, along with intermittent electricity form photovoltaic cells, powers a solid oxide electrolysis cell (SOEC) to produce hydrogen. The results demonstrate that the energy efficiency is 32.08%. In addition, more than half (56.6%) of the electrolysis capacity can be utilized during night hours due to thermochemical energy storage (syngas). In addition, a year-long operation simulation showed that the system can diminish CO₂ emission by 25.7% to produce the same amount of hydrogen. The full-spectrum solar hydrogen production system provides a viable option for the transition from fossil energy to renewable energy.

In response to Japan's ambitious pursuit of carbon neutrality by 2050, this paper investigates the potential for tidal power generation in the Seto Inland Sea (SIS), Japan, utilising high-resolution ocean modelling and an analytic hierarchy process (AHP) combined with GIS-based spatial analysis. The analysis incorporates a 100 m resolution tidal current and water depth layers from the ocean model. The criteria used for evaluation were divided into main criteria, namely, proximity to existing infrastructure, wave height, and shipping density, and subcriteria, namely, distance from ports, power stations, highly populated areas, and coastlines. The identified optimal locations include the Naruto Strait, Akashi Strait, Matsushima Island, Hanaguri Strait, Funaori Strait, Taizaki Island, Kurushima Strait, Obatake Strait, and Tsuwaji Strait. With a focused analysis of the Obatake Strait in Yamaguchi Prefecture, analytical assessments of potential tidal power were conducted by considering two tidal turbines, Openhydro and Voith. The results indicate a power supply for approximately 100,000 households and a reduction of approximately 198,000 t-CO₂ per year in the most optimistic scenario. In a less optimistic scenario, with the installation of only 10% of the maximum capacity, a power supply for approximately 10,000 households and a reduction of approximately 19,800 t-CO₂ per year are observed. As Japan targets carbon neutrality by 2050, this research offers vital and practical insights for policymakers, energy planners, and environmentalists, identifying the Seto Inland Sea as a strategic area for tidal power development, aligning with Japan's commitment to a sustainable and resilient energy landscape.

Adding water (H₂O) to the electrolyte improves discharge capacity by enhancing the solution mechanism, but the evolution of discharge capacity with H₂O content growth remains divergent. In view of this, the contribution of H₂O to discharge capacity is revealed by modelling a lithium‑oxygen (Li-O₂) battery coupled with the H₂O reaction mechanism. With increasing H₂O content in the electrolyte, the discharge capacity first rises thanks to the alleviation of surface passivation and then declines owing to the limitation of O₂ diffusion. Although the promotion of solution mechanism is most pronounced with a small amount of H₂O (below 2000 ppm) in the dimethoxyethane (DME)-based electrolyte, the enhancement of solution mechanism in the tetraethylene glycol dimethyl ether (TEGDME)-based electrolyte is more sensitive to changes in H₂O contents (above 500 ppm) than DME- and DMSO (dimethyl sulfoxide)-based electrolytes. Hence the H₂O contents corresponding to the maximum discharge capacity (defined as “optimized water content”) of TEGDME- and DMSO-based electrolytes are 2000 ppm and 10,500 ppm based on the Super P carbon cathode, respectively. The evolutions of “optimized water content” and discharge capacity are more sensitive to changes in the porosity and initial carbon particle radius of the carbon cathode. Compared to the absence of H₂O, the discharge capacities with the “optimized water content” increase by 360% and 346% at a porosity of 0.9, as well as by 268% and 290% for TEGDME- and DMSO-based electrolytes at an initial carbon particle radius of 70 nm, respectively. In consequence, the electrolyte composition and cathode structure codetermine the “optimized water content” and the maximum promotion of H₂O to the discharge capacity.

With a rising number of buildings being equipped with private distributed energy resources (DERs) such as rooftop PV panels and energy storage devices, an effective energy management method for a building cluster becomes increasingly imperative. This paper proposes a novel decentralized transactive energy management (TEM) method for a hybrid cluster of residential and commercial buildings, which enables peer-to-peer (P2P) energy trading among the DER owners and consumers. The strategic interactions among the DER owners and consumers are modeled as a multi‑leader-multi-follower (MLMF) Stackelberg game and formulated as a bi-level model. The DER owners, the commercial and residential consumers are all autonomous entities, optimizing their individual welfare functions and sharing necessary trading-related information, which are expressed as the upper-level leaders' models and the lower-level followers' models, respectively. To preserve the privacy and autonomy of each entity within the building cluster, a distributed algorithm incorporated with an efficient P2P pricing mechanism is designed for the formulated MLMF Stackelberg game model. Simulation results demonstrate the effectiveness of the proposed method on mitigating the reliance of the building cluster on the power grid, motivating the DERs to actively participate in P2P trading, and reducing the consumers' energy consumption costs

This study developed a methodological approach for long-term electricity demand forecasting and applied it to the electricity demand in Cuba, which is crucial for transitioning from a fossil fuel-dependent system to renewable energy sources. The methodology employs enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) applied for obtaining long-term trends from historical electricity usage data decomposition, combined with a long short-term memory (LSTM) deep learning model for prediction. Comprehensive datasets, including historical electricity consumption, economic indicators, and demographic data, are utilized in the analysis. Monte Carlo simulations, then, are integrated to address uncertainties in prediction and explore 50 different scenarios of future electricity demand. The study forecasts varying scenarios for the energy demand of Cuba by 2050, with the extreme low scenario projecting a decrease of up to 7.9% compared to the 2019 level. This research offers a groundbreaking framework specifically designed to aid Cuba's energy sector stakeholders in informed decision-making during this critical energy transition. The adaptability of the methodology makes it applicable for long-term projections in various sectors, offering a reliable tool for global decision makers.

This study presents a comprehensive system-level analysis model for evaluating performance characteristics of a hundred-kilowatt proton exchange membrane fuel cell (PEMFC) test system. Unlike conventional power-focused systems, the test system has a more complex architecture and numerous balance of plants (BOPs). The developed model integrates detailed input-output traits of each system component. The energy efficiency ratio (EER) and energy conversion efficiency (η) are introduced as metrics for assessing net power consumption and conversion capability of the test system. By simulating various operational scenarios (considering temperature, load current, cathode pressure, humidity, and PEMFC power), the model predicts the behaviors of BOPs and energy flow relations. The changing rules of the EER and η are also investigated. An increase in temperature, current, and cathode pressure leads to an improvement in EER. Increasing operating temperature, cathode pressure, and humidity can enhance η. Key findings suggest optimal conditions for system self-sufficiency include an operating temperature below 90 °C, load current over 1200 mA cm⁻², and air humidity under 90%. Furthermore, the PEMFC power is advisable to configure between 50% and 100% of the test system's maximum power. These insights are pivotal for improving the design and functionality of PEMFC testing equipment, further contributing significant advancements to fuel cell technology.