Applied Energy最新文献

Gas–electricity integrated energy systems (GE-IES) offers a promising solution for enhancing energy efficiency and accommodating renewable energy sources. Accurate dynamic simulation is essential for optimizing and controlling GE-IES. However, the presence of various local controllers introduces prominent discrete characteristics, posing challenges for the dynamic simulation of the GE-IES. This paper investigates the dynamic simulation method in GE-IES with discrete characteristics. Firstly, we propose an adaptive step size simulation method based on quantized state system theory. This method maintains the event-driven characteristics of the quantized state integration algorithms, while enhancing computational speed through adaptive step size adjustments. Secondly, we establish an event-driven simulation framework that facilitates interactions of different subsystems during the dynamic simulation, improving the compatibility with various models and solving algorithms. Finally, we validate the accuracy, efficiency, and scalability of the proposed method and the framework using two typical GE-IES cases with different scales. Simulation results demonstrate the effectiveness on the dynamic simulation of GE-IES and highlight the feasibility of natural gas networks in consuming and storing surplus renewable energy.

Power quality issues in weak grids with large impedance pose significant challenges that limit wind energy (WE) penetration levels and the performance efficiency of existing WE infrastructure. The presence of non-linear (NL) loads at the point of common coupling (PCC) further restricts these levels. This paper addresses these challenges by introducing an additional distributed static compensator (DSTATCOM) at the PCC, controlled by a higher-order Delayed Least Mean Fourth (DLMF) algorithm. The proposed DLMF control algorithm estimates the active and reactive components of the load current by updating their respective weights with appropriate delays, considering variations in loads, DC-link voltage, and wind energy generation. The MATLAB implementation of the proposed control is designed and validated through experimental investigations. These investigations involve varying short circuit ratios, wind speeds, and the presence of NL loads. The results demonstrate that the proposed method can enhance wind penetration levels in weak grids by up to 30%.

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. This paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

The proper power allocation between multiple energy sources is crucial for hybrid electric vehicles to guarantee energy economy. As a data-driven technique, offline deep reinforcement learning (DRL) solely exploits existing data to train energy management strategy (EMS), which becomes a promising solution for intelligent power allocation. However, current offline DRL-based strategies put high demands on the quality of datasets, and it is difficult to obtain numerous high-quality samples in practice. Thus, a bootstrapping error accumulation reduction (BEAR)-based strategy is proposed to enhance the energy-saving performance with different kinds of datasets. After that, based on the advanced V2X technology, a data-driven energy management updating framework is proposed to improve both fuel economy and adaptability of EMS via multi-updating. Specifically, the framework deploys multiple V2X-based buses to collect real-time information, and updates the strategy periodically making full use of offline data. The results show that the proposed BEAR-based EMS performs better than state-of-the-art offline EMSs in terms of fuel economy, especially realizing an improvement of 2.25% when training with mixed datasets. It is also validated that the offline EMS with the updating mechanism can reduce energy costs step by step under two different kinds of initial datasets.

This paper presents a newly developed methodology aimed at assessing at national level the techno-economic potential of district heating (DH) based on renewables and excess heat sources. The novelty of the model lies in the use of an optimization approach to match heat demand and heat sources at large scale level, while keeping a high degree of spatial detail. Areas suitable for DH adoption are identified by minimizing heat delivery costs, and therefore by choosing the most economical technology between district heating and the alternative individual solution. The optimization approach, usually applicable at limited analytical scope because of the computational burden, is here adapted to large scale analysis through the introduction of novel methodological elements with which the network topology is simulated nationwide.

The methodology applies to preliminarily identified maps of available heat sources and eligible heat demand, with the quantification of the latter including retrofitting and low connection rate scenarios. It then consists in two steps: connecting elements in a graph through triangulation and routing algorithms and optimizing connections to minimize the overall heat delivery costs, either by adopting district heating or individual heating systems. The whole methodology is based on open-source data and tools for broad applicability. The paper presents the elaborated methodology together with the application of the entire model to Italy. The outcome is a map of the potential district heating systems identified with significant spatial detail nationwide. A four-fold expansion is envisaged, covering 12% of the national heat demand with renewables- and excess heat- based district heating.

Hybrid photovoltaic (PV) - wind power plants (HyPPs), i.e., where the PV and wind systems are co-located and share the Point of Interconnection (POI) with the grid, have recently attracted more attention. This trend is driven by the expected reduced capital and operational expenditures achieved through, e.g., shared land and POI infrastructure for HyPPs compared to two individual PV and wind installations. However, if the POI is underdimensioned relative to the PV and wind capacities, the generation from either or both of the assets must at times be curtailed, unless mitigated by solutions like energy storage. This curtailment might lead to income losses. Moreover, as HyPPs typically are designed using wind and PV generation data on hourly resolution, the actual curtailment losses can be underestimated. This might in turn lead to HyPP designs which are techno-economically sub-optimal.

In this study, a comparative analysis is conducted to analyze how the curtailment and income loss estimations for HyPPs, as well as the techno-economic optimal HyPP topologies, are impacted by varying the input data time resolution. One year of site measured PV and wind power generation data on 5 s resolution from an existing HyPP located in Eastern Germany are used as basis for the study. For a HyPP topology with an undersized POI, where the installed capacities of the POI, PV, and wind systems are all equal, the curtailment losses are estimated to be 1.45%, 1.43% and 1.09% using the 5 s, 1 min and 1 h resolution datasets, respectively. Moreover, using the 1 h instead of the 1 min dataset leads to a 1.86% overestimation of the total Net Present Value (NPV) for this HyPP topology. As the shares of the PV and wind systems increase relative to the POI capacity, the differences in the estimation of the curtailment losses and NPVs between the high- and low-resolution datasets become more significant.

The existing building stock is crucial for enhancing decarbonization targets and mitigating climate change. This article delves into a methodological approach that combines prospective life cycle assessment, building thermal simulation using projected future climate data, and global sensitivity analysis to pinpoint the most influential parameters under current climate conditions and future scenarios. The methodology covers plausible decarbonization pathways for the electricity mix, considering the growing utilization of renewable sources, which are influenced by the building locations. An adaptive reuse process involves converting a historic residence into an office building to validate the proposed methodology. Several retrofit strategies are assessed, such as exterior wall insulation, roof insulation, and window replacement. The findings reveal a 12% rise in average usage impacts and a 7% increase in cradle-to-use impacts from the base scenario to future climate projections. Embodied impacts surpass use-phase impacts by 23% in future climates and 33% in certain baseline scenarios. Utilizing future climate data in the life cycle analysis to estimate energy requirements can aid in forecasting building performance under climate change, especially in adapting the existing building stock for enhanced thermal comfort with minimal environmental impact.

Recommender systems play a critical role in optimizing building energy consumption by providing personalized advice based on data analytics and user preferences. However, the literature highlights the need for systems that can justify their recommendations, as many of these systems use non-transparent machine-learning techniques. This research introduces two distinct types of indicators with three main goals: to identify patterns of flexible consumption behavior using transparent and straightforward methods suitable for remote decision support systems, thereby eliminating the need for extensive databases; to evaluate the feasibility of installing solar panels on building facades, rooftops, and structures using high-resolution 3D models; and to enhance understanding through a quantitative assessment of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability through the Flexible Consumption Indicators. Topology Indicators perform a quantitative assessment of the feasibility of support surfaces for installing photovoltaic panels, taking into account rooftop pitch angles, orientations, and surrounding and internal structures, identifying those areas exposed to sufficient levels of irradiation. This study, which uses actual consumption profiles and similar households' buildings 3D models, demonstrates how the proposed indicators can aid identifying users with flexible consumption profiles that reside in buildings compatible with renewable energy sources, aiding in decision-making process within the energy transition.

Hydrogen, viz. a green energy carrier, is poised to considerably contribute to the empowerment of a sustainable society. By valorizing plastics, catalytic pyrolysis was envisaged as a promising route to produce green hydrogen and value-added product here. Firstly, the screening of optimal catalyst support (from activated carbon and four zeolites: M-zeolite, B-zeolite, Y-zeolite, ZSM-5) was executed by studying catalytic polypropylene (PP) pyrolysis over supported Ni catalysts. In view of the highest H₂ yield (19.2 mmol/g_PP) of Ni/ZSM-5, ZSM-5 was put forth as the optimal catalyst support. Then, the identification of optimal active metal (from Ni, Fe, Co, FeNi, FeCo, and NiCo) was performed by running the catalytic PP pyrolysis over ZSM-5 supported catalysts. For catalytic PP pyrolysis, NiCo/ZSM-5 was the optimal catalyst with the highest H₂ yield (28.7 mmol/g_PP), while the resulting pyrolysis oil demonstrated potential for use as jet fuel. From catalytic pyrolysis of various plastics over NiCo/ZSM-5, polystyrene gave the highest H₂ composition (83.2 vol%) of pyrolysis gas and high composition (52.8 area%) of benzocyclobutene (useful chemicals for semiconductor and microelectronics fields) in pyrolysis oil. Lastly, the catalytic mechanism was discussed based on the results, revealing NiCo's remarkable enhancement in H₂ yield to 28.7 mmol/g, which surpassed the individual yields of Ni (19.2 mmol/g) and Co (10.2 mmol/g), thereby underscoring the synergistic effect of NiCo. This study supports the recycling of plastics waste into hydrogen energy and valuable products, contributing to environmental pollution mitigation.

Geothermal energy plays a pivotal role in the global energy transition towards carbon-neutrality, providing a sustainable, renewable, and abundant source of clean energy in the fight against climate change. Despite advancements, the optimal engineering of geothermal systems and energy extraction remains challenging, particularly in accurately predicting production temperatures. Here, we present an innovative numerical approach using a hybrid neural network that merges Artificial Neural Network (ANN) and Bidirectional Gated Recurrent Unit (BiGRU). With this hybrid network, we comprehensively assess 22 influential factors, including construction parameters, physical parameters, and well layout, which influence thermal breakthrough time and production temperature across varying fracture density. While the ANN captures the nonlinear interplay between static constraints and thermal breakthrough time, the BiGRU adeptly handles the temporal intricacies of production temperature. We examine the impact of ANN parameters on model performance, in comparison with conventional temporal models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and BiGRU. Our findings reveal that augmenting hidden layers and neurons in ANN enhances its capacity to model intricate nonlinear processes, albeit with a risk of overfitting. Notably, the relu activation function emerges as optimal for managing nonlinear processes, while BiGRU excels over RNN, GRU, and LSTM models in forecasting production temperature of fractured geothermal systems, owing to its ability to extract implicit information from time series across historical and future trajectories. Crucially, the prediction uncertainty, measured by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), remains within 0.15, underscoring the precision and efficacy of our hybrid approach in forecasting geothermal energy extraction. This study presents a significant stride towards a high-precision and efficient predictive framework crucial for advancing geothermal energy extraction in the broader context of renewable energy transition endeavors.