Applied Energy最新文献

{"title":"Enhancing cyber-resilience in integrated energy system scheduling with demand response using deep reinforcement learning","authors":"Yang Li ,&nbsp;Wenjie Ma ,&nbsp;Yuanzheng Li ,&nbsp;Sen Li ,&nbsp;Zhe Chen ,&nbsp;Mohammad Shahidehpour","doi":"10.1016/j.apenergy.2024.124831","DOIUrl":"10.1016/j.apenergy.2024.124831","url":null,"abstract":"<div><div>Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor–critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor–critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124831"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage business model for voltage sag sensitive industrial users employing energy storage systems","authors":"Hong Liao,&nbsp;Yunzhu Chen,&nbsp;Zixuan Zheng,&nbsp;Xianyong Xiao,&nbsp;Shu Zhang","doi":"10.1016/j.apenergy.2024.124945","DOIUrl":"10.1016/j.apenergy.2024.124945","url":null,"abstract":"<div><div>Integration of a behind-the-meter (BTM) energy storage system (ESS) is a dependable method of reducing electricity costs and improving power quality for industrial users susceptible to voltage sags. However, at present, barriers such as substantial initial investment costs, extended investment return periods, and limited service strategy flexibility are impeding the widespread implementation of BTM ESSs in industrial contexts. Integrated energy service providers (IESPs) supply innovative BTM ESS solutions and have emerged as viable options for navigating these challenges. This study proposes a new two-stage business model designed to advance ESS deployment while considering the needs of both the IESP and users. Herein, the framework of the proposed business model is outlined to delineate the participant roles and responsibilities at different stages. A detailed cost–benefit analysis is conducted to support a scenario analysis based on prospect theory. Then, a Stackelberg game model is constructed to describe the interactive relationship between the participants; using this model, an optimal ESS service strategy can be determined by resolving a simplified Stackelberg game problem. Our findings reveal that this model effectively mitigates long-term income risks for IESPs and substantially lowers initial investment costs for users by 21.93 % to 89.23 %, along with a reduction in lifecycle costs by 2.35 % to 17.12 % for users. A comprehensive analysis of six critical parameters indicates that the ESS lifetime, the disparity between peak and off-peak electricity prices, and power quality management performance exert the most significant influence on the total net benefits for all participants. The proposed model is expected to further the adoption of ESSs among industrial users.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124945"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liquid air energy storage system with oxy-fuel combustion for clean energy supply: Comprehensive energy solutions for power, heating, cooling, and carbon capture","authors":"Yungeon Kim ,&nbsp;Taehyun Kim ,&nbsp;Inkyu Lee ,&nbsp;Jinwoo Park","doi":"10.1016/j.apenergy.2024.124937","DOIUrl":"10.1016/j.apenergy.2024.124937","url":null,"abstract":"<div><div>Liquid air energy storage systems have garnered significant attention in the energy storage sector because of their high energy density and geographical independence. However, despite their substantial potential for improving renewable energy-based systems, their commercialization is hindered by their low round-trip efficiency. Furthermore, dependency on other thermal systems and environmental challenges remain unresolved, despite efforts to address these limitations. This study proposes an independent liquid air energy storage system that offers effective energy solutions, including the ability to provide power, heating, and cooling with improved efficiency and sustainability. Moreover, in-depth assessments of the energy, exergy, economic, and environmental performance were conducted. Under rated conditions, the system delivers 118.19 MW of power, 38.64 MW of heating, and 81.07 MW of cooling, achieving a round-trip efficiency of 80.56 %. Additionally, the system generates nitrogen as a by-product, providing further economic benefits. An economic analysis revealed that it yields a net present value of $636.51 million and an internal rate of return of 25.67 %. Environmentally, the system uses an oxy-fuel combustion method to capture 99.997 % of carbon dioxide emissions from natural gas combustion without consuming additional energy. These findings will contribute to the future development of sustainable and eco-friendly energy supply systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124937"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of mobility decarbonization with carbon tax policies and electric vehicle incentives in the U.S.","authors":"Weijie Pan,&nbsp;Ekundayo Shittu","doi":"10.1016/j.apenergy.2024.124838","DOIUrl":"10.1016/j.apenergy.2024.124838","url":null,"abstract":"<div><div>On-road transportation electrification shows a significant stride toward achieving the low-carbon emission targets in the United States. Similarly, decarbonizing electricity generation technologies is crucial to prevent the transfer of <span><math><mrow><mi>C</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> emissions from the transportation sector to the power generation sector. The realization of nationwide emission mitigation goals undoubtedly depends on collaborative efforts from all U.S. states. However, in the presence of heterogeneous low-carbon policies, the state-level decarbonization landscape, which includes the electric vehicle (EV) adoption process and the choice of local renewable portfolios, blurs the national-level path toward the decarbonization target from a long-term planning perspective. To address this knowledge gap, this study employs an Integrated Assessment Model (IAM), specifically GCAM-USA, which is a state-level representation of the U.S. energy system embedded within the Global Change Analysis Model (GCAM). The aim is to present a blueprint illustrating how the U.S. decarbonization process is influenced by differentiated state-level EV purchase incentives and/or carbon tax policies. The modeling results reveal a heightened sensitivity of the effectiveness of decarbonization in midwestern states to the implementation of various low-carbon strategies, leading to a proposal to establish an interstate collaborative energy strategy for electricity generation in the face of the emerging era of electrified transportation. The insights derived from this study can contribute to raising awareness among federal and state policymakers about the importance of tailored state strategies for decarbonization while simultaneously striving to facilitate the harmonization of national-level progress in achieving climate goals.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124838"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling seasonal thermal storage dynamics in the year-round scheduling of renewable energy systems","authors":"Haiyang Jiang ,&nbsp;Jiajun Luo ,&nbsp;Yan Guo ,&nbsp;Ershun Du ,&nbsp;Ning Zhang ,&nbsp;Yuchen Fang ,&nbsp;Yating Wang ,&nbsp;Goran Strbac","doi":"10.1016/j.apenergy.2024.124828","DOIUrl":"10.1016/j.apenergy.2024.124828","url":null,"abstract":"<div><div>Seasonal thermal storage (STS) enables the long-term storage of renewable energy in heat, which could effectively address the seasonal mismatch between renewable energy supply and heat demand. This paper models the temperature distribution of the water-based STS, considering the insulating effects of the soil surrounding the tank. Considering that the commonly used state-of-charge (SOC) model could not describe the time-variant heat loss in detail in a renewable energy system scheduling problem, a temperature field correction method is proposed to correct the scheduling results. Three case studies are performed on Garver’s 6-node system and HRP-38 system to validate the proposed method’s enhanced accuracy in managing STS compared to the SOC model. The proposed method could also reduce renewable curtailment in the scheduling problem due to a more detailed description of the heat loss process and a more effective renewable energy system scheduling scheme.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124828"},"PeriodicalIF":10.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-layer energy dispatching and collaborative optimization of regional integrated energy system considering stakeholders game and flexible load management","authors":"Zhi-Feng Liu ,&nbsp;Shi-Xiang Zhao ,&nbsp;Xing-Fu Luo ,&nbsp;Ya-He Huang ,&nbsp;Rui-Zheng Gu ,&nbsp;Ji-Xiang Li ,&nbsp;Ling-Ling Li","doi":"10.1016/j.apenergy.2024.124918","DOIUrl":"10.1016/j.apenergy.2024.124918","url":null,"abstract":"<div><div>The Integrated Energy System (IES) facilitates the synergistic operation of diverse energy forms through flexible energy conversion and management strategies, offering robust support for energy transition and sustainable development. However, the IES model belongs to a highly complex and nonlinear multi-objective optimization problem. Achieving reliable solutions and designing efficient flexible load management strategies to effectively manage energy supply-demand balance still face challenges. Therefore, this study constructed a novel two-layer energy dispatching and collaborative optimization model for regional integrated energy system (TDCOMRIES) considering stakeholders game and flexible load management. Specifically, in TDCOMRIES, a master-slave game model led by the energy manager and a cooperative game model among the IES group are constructed to explore the relationships among stakeholders during the system operation process; a novel multi-objective snake optimization algorithm was proposed, and combined with the GUROBI solver to optimize TDCOMRIES; and two price responsiveness-based flexible load management strategies are presented to enhance the management efficiency and reduce the operational costs of the IES. Finally, the effectiveness and feasibility of the proposed model, algorithm, and strategies were verified through three carefully designed cases. The results demonstrated that the flexible load management strategy leads to a significant reduction of approximately 18 % in the economic costs of IES, thereby effectively enhancing the target benefits for various stakeholders. Meanwhile, the IES achieved an average of 40 % new energy generation, an average electricity self-sufficiency rate of 60 %, and a 5.1 % increase in the level of energy satisfaction.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124918"},"PeriodicalIF":10.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel robust optimization method for mobile energy storage pre-positioning","authors":"Hening Yuan,&nbsp;Yueqing Shen,&nbsp;Xuehua Xie","doi":"10.1016/j.apenergy.2024.124810","DOIUrl":"10.1016/j.apenergy.2024.124810","url":null,"abstract":"<div><div>The traditional power distribution network is transitioning to an active electrical distribution network due to the integration of distributed energy resources. Simultaneously, the increasing occurrence of extreme weather requires power networks to be more resilient. Distributed energy resources, especially mobile energy storage systems (MESS), play a crucial role in enhancing the resilience of electrical distribution networks. However, research is lacking on pre-positioning of MESS to enhance resilience, efficiency and electrical resource utilization in post-disaster operations. To address these issues, this paper introduces a proactive MESS pre-positioning method in active electrical distribution networks considering the uncertainties of distributed generation output. Firstly, the flexible resources in active distribution networks are modeled, including distributed generation, MESS and Electric Vehicles. Then, a robust optimization model is established for the pre-positioning of MESS considering the PV output uncertainty, where the big-M method and the column constraint generation algorithm are used to calculate the optimal capacity and location of the MESS. Finally, the effectiveness of the MESS pre-positioning model is verified using the IEEE 33-node system and the IEEE 141-node system, respectively. The simulation results show that the load loss at most of the nodes is significantly reduced and the total system cost is reduced by 17.65% compared with the case of fixed MESS access location. The results also show that when the number of MESS is low, each additional MESS unit reduces the total load shedding cost by about 20%. Moreover, the proposed robust optimization model for MESS pre-positioning is also effective in large-scale systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124810"},"PeriodicalIF":10.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization","authors":"Bo Zhao,&nbsp;Weige Zhang,&nbsp;Yanru Zhang,&nbsp;Caiping Zhang,&nbsp;Chi Zhang,&nbsp;Junwei Zhang","doi":"10.1016/j.apenergy.2024.124713","DOIUrl":"10.1016/j.apenergy.2024.124713","url":null,"abstract":"<div><div>As intelligent computation power in embedded systems has rapidly developed in recent years, the health state monitoring and remaining useful life prediction of batteries based on deep learning can gradually be deployed and applied in the onboard management system. However, there are still problems with large amounts of data calculation, high model complexity, and poor interpretability. Therefore, this paper proposes a remaining life prediction method for batteries combined with interpretable deep learning and network optimization. First, based on the fused deep learning model, the interpretable algorithm is used to explain the degree of attention of the model to different features and quantify the contribution of each part in input data, thereby identifying important aging features and removing useless data. Then, structured pruning is adopted to remove redundant network parameters under the constraints of ensuring prediction accuracy. The structure generally realizes model interpretation and full process optimization from battery aging data to network parameters. According to the validation of the selected dataset, compared with the original model, the model optimized by the method proposed in this paper has an average prediction accuracy increase of 0.19 % and an average speed increase of 46.88 %. It greatly saves computational resource consumption and improves model operation efficiency while ensuring prediction accuracy. In addition, the explanation and analysis of crucial feature areas in battery aging data provide a reference for effective health management.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124713"},"PeriodicalIF":10.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep neural network-assisted fast and precise simulations of electrolyte flows in redox flow batteries","authors":"Zixiao Guo ,&nbsp;Jing Sun ,&nbsp;Shuaibin Wan ,&nbsp;Zhenyu Wang ,&nbsp;Jiayou Ren ,&nbsp;Lyuming Pan ,&nbsp;Lei Wei ,&nbsp;Xinzhuang Fan ,&nbsp;Tianshou Zhao","doi":"10.1016/j.apenergy.2024.124910","DOIUrl":"10.1016/j.apenergy.2024.124910","url":null,"abstract":"<div><div>Flow fields are a key component in redox flow batteries, which is to distribute electrolytes onto electrodes at the maximum uniformity with the minimum pump work. Achieving this design goal requires accurate simulations of electrolyte flows and identification of the dead zones where the flows become weak or stagnant. However, conventional case-by-case numerical simulation requires significant computational resources. In this work, we use deep learning to predict the electrolyte flow in flow batteries with a neural network knows as U-Net. The U-Net is well trained by learning the mapping between the input (flow field geometry) and output (velocity magnitude distribution). Results show that the pixel-wise comparison of the velocity magnitudes between the U-Net-predicted results and finite element simulated results exhibits an average Euclidean distance of 442.6 and an average R² of 0.979, indicating that the electrolyte distribution can be accurately simulated based on the geometric characteristics of flow fields. In addition, dead zones are precisely identified by labeling the regions with low velocity magnitudes. Modifying the channel depth in these regions substantially enhances the under-rib convection, thereby improving the system efficiency by 5.5 % at 200 mA cm⁻². Furthermore, compared to the numerical simulation, the U-Net-assisted prediction significantly reduces the computational time by 99.9 %. It is anticipated that the U-Net-assisted simulation provides an accurate and efficient tool for obtaining the velocity distribution of flow fields that can assist the flow field design especially in large quantities and large scale.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124910"},"PeriodicalIF":10.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative experimental study of alkaline and proton exchange membrane water electrolysis for green hydrogen production","authors":"Jingyi Wang ,&nbsp;Jinbin Yang ,&nbsp;Yu Feng ,&nbsp;Jing Hua ,&nbsp;Zhengjian Chen ,&nbsp;Mei Liao ,&nbsp;Jingran Zhang ,&nbsp;Jiang Qin","doi":"10.1016/j.apenergy.2024.124936","DOIUrl":"10.1016/j.apenergy.2024.124936","url":null,"abstract":"<div><div>Alkaline electrolysis (ALK) and polymer electrolyte membrane electrolysis (PEM) are two pivotal technologies supporting the advancement of green hydrogen production. Understanding their distinct characteristics is essential for optimizing production systems, with potential implications for future hybrid electrolysis strategies. However, experimental studies on green hydrogen electrolysis are limited, particularly comparative investigations between these two systems. This study conducts a comprehensive comparative experimental analysis of ALK and PEM systems with an identical hydrogen production rate of 1400 ml/min. It focuses on electro-heat-mass coupled dynamics across steady-state, cold-start, controlled dynamic processes, and solar power integration. Results reveal that PEM consumes less energy for hydrogen production, ranging from 4.1 to 4.3 kWh/Nm³, compared to 4.6–4.8 kWh/Nm³ for ALK. This study proposes that cold start time be characterized by two specific time points: reaching rated electrical parameters and achieving operational conditions. The second time point is typically longer and represents the primary limiting factor in the cold start process. Dynamic responses during ramp-up and ramp-down processes are notably asymmetric, with longer durations observed during ramp-down. Heat and gas purity responses to electrical changes also follow distinct patterns, with hydrogen to oxygen (HTO) stabilizing slower than temperature and oxygen to hydrogen (OTH). This facilitates the potential that the lower load limit can be reduced under dynamic conditions compared to steady states, as demonstrated by ALK and PEM adjusting from 50 % to 30 % and from 40 % to 10 % respectively in solar integration. Both systems exhibit agile ramp rate, with ALK adjusting its current by 70 %/s and PEM by 90 %/s. Both systems show viability for solar power integration, with PEM being more immediately suitable, while ALK requires further investigation to effectively manage rising HTO levels. This study provides an experimental data foundation and insights for advancing green hydrogen production.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"379 ","pages":"Article 124936"},"PeriodicalIF":10.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}