Pub Date : 2026-04-15Epub Date: 2026-01-30DOI: 10.1016/j.apenergy.2026.127433
Tayenne Dias de Lima, Pedro Faria, Zita Vale
Battery energy storage systems (BESS) play a critical role in enhancing the flexibility, reliability, and efficiency of residential energy management. Optimized scheduling of BESS is essential to maximize operational benefits while mitigating carbon emissions. Consequently, investigations addressing the enhanced operation of BESS in energy management systems are highly relevant. From this perspective, it is important to consider operational patterns that preserve the long-term performance and lifespan of batteries. This paper presents a mixed integer linear programming model for the optimal scheduling of home energy systems supported by solar generation and battery systems. After optimization, the model calculates battery degradation, considering both cycle and calendar aging effects. Additionally, a carbon emissions penalty was incorporated into the objective function to address environmental impacts. The model was coded in Python and solved through the CBC solver. The model was tested under different battery SOC limits and seasonal conditions (winter and summer), highlighting the role of BESS in reducing energy costs, emissions, and grid dependency while evidencing the impact of operational strategies on battery aging.
{"title":"Optimal scheduling of home energy systems considering battery aging and CO2 emissions","authors":"Tayenne Dias de Lima, Pedro Faria, Zita Vale","doi":"10.1016/j.apenergy.2026.127433","DOIUrl":"10.1016/j.apenergy.2026.127433","url":null,"abstract":"<div><div>Battery energy storage systems (BESS) play a critical role in enhancing the flexibility, reliability, and efficiency of residential energy management. Optimized scheduling of BESS is essential to maximize operational benefits while mitigating carbon emissions. Consequently, investigations addressing the enhanced operation of BESS in energy management systems are highly relevant. From this perspective, it is important to consider operational patterns that preserve the long-term performance and lifespan of batteries. This paper presents a mixed integer linear programming model for the optimal scheduling of home energy systems supported by solar generation and battery systems. After optimization, the model calculates battery degradation, considering both cycle and calendar aging effects. Additionally, a carbon emissions penalty was incorporated into the objective function to address environmental impacts. The model was coded in Python and solved through the CBC solver. The model was tested under different battery SOC limits and seasonal conditions (winter and summer), highlighting the role of BESS in reducing energy costs, emissions, and grid dependency while evidencing the impact of operational strategies on battery aging.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127433"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076598","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}
Pub Date : 2026-04-15Epub Date: 2026-02-06DOI: 10.1016/j.apenergy.2026.127484
Ángel Paredes , Yihong Zhou , José A. Aguado , Thomas Morstyn
The imperative for increased power system flexibility, driven by the energy transition, positions Independent Aggregators (IAs) as central to integrating Distributed Energy Resources (DERs). However, the inherent uncertainty of DERs limits their participation in reserve markets and complicates the design of economic incentives through bi-level optimization methods. To address this challenge, this paper proposes a bi-level optimization framework that employs a novel reformulation of Wasserstein distributionally robust joint chance constraints. The approach enables IAs to mobilize stochastic DER flexibility through robust incentives while securing reserve provision. The problem is reformulated as a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and a Faster Inner Convex Approximation (FICA) technique. This provides computationally fast and accurate probability guarantees for reserve delivery. Empirical validation using Spanish market data demonstrates that the proposed FICA-enabled framework for DER aggregation substantially enhances economic efficiency and ex-post risk compliance over benchmarks. FICA increases the aggregator profits under stringent robustness while determining optimal incentive combinations that unlock higher flexibility volumes, with less computational burden as single-level approaches. This research offers IAs a practical, robust tool for effective reserve market participation, facilitating DER integration in reserve markets.
{"title":"Independent aggregators securing end user Wasserstein distributionally robust flexibility through bilevel incentives","authors":"Ángel Paredes , Yihong Zhou , José A. Aguado , Thomas Morstyn","doi":"10.1016/j.apenergy.2026.127484","DOIUrl":"10.1016/j.apenergy.2026.127484","url":null,"abstract":"<div><div>The imperative for increased power system flexibility, driven by the energy transition, positions Independent Aggregators (IAs) as central to integrating Distributed Energy Resources (DERs). However, the inherent uncertainty of DERs limits their participation in reserve markets and complicates the design of economic incentives through bi-level optimization methods. To address this challenge, this paper proposes a bi-level optimization framework that employs a novel reformulation of Wasserstein distributionally robust joint chance constraints. The approach enables IAs to mobilize stochastic DER flexibility through robust incentives while securing reserve provision. The problem is reformulated as a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and a Faster Inner Convex Approximation (FICA) technique. This provides computationally fast and accurate probability guarantees for reserve delivery. Empirical validation using Spanish market data demonstrates that the proposed FICA-enabled framework for DER aggregation substantially enhances economic efficiency and ex-post risk compliance over benchmarks. FICA increases the aggregator profits under stringent robustness while determining optimal incentive combinations that unlock higher flexibility volumes, with less computational burden as single-level approaches. This research offers IAs a practical, robust tool for effective reserve market participation, facilitating DER integration in reserve markets.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127484"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171557","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}
Under the dual context of rapid AI-driven digital expansion and green, low-carbon transition, understanding how renewable energy supports computing power and how global environmental change—particularly extreme climate events—shapes the spatial heterogeneity of China's electricity–computing integration has become a pressing scientific challenge. By integrating energy supply, computing demand, and environmental constraints (including mean and extreme climate conditions), this study develops a three-dimensional framework to assess the suitability of computing power centers (CPCs) and establish a new spatial paradigm for electricity–computing synergy in China. The results reveal pronounced spatial mismatches among the three dimensions. From the energy perspective, the energy supply is evolving into distinct renewable clusters, with the Northwest and Tibetan Plateau hosting 87.25% of national photovoltaic and 79.77% of wind potential, while the Southwest anchors a hydropower cluster accounting for 63% of national installed capacity. However, environmental constraints act as a spatial filter; Northern regions maximize suitability by leveraging natural cooling to balance Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE), whereas Southern and Western regions are constrained by intensifying heatwaves and seismic risks, respectively. Conversely, computing demand remains heavily concentrated in Eastern coastal agglomerations. To resolve these structural imbalances, we propose a functional zoning paradigm comprising energy-oriented, demand-driven, and incremental-development regions. We further demonstrate that prioritizing this energy-computing coupling substantially improves operational efficiency and reduces carbon footprints. This spatial paradigm promotes interregional complementarity and facilitates the decoupling of Artificial Intelligence expansion from carbon emissions, providing a scientific basis for the “East-Data, West-Computing” initiative.
{"title":"Toward a new spatial paradigm of electricity–computing synergy under renewable energy endowments and geographical environmental constraints","authors":"Jiaju Guo , Wenjuan Hou , Xiaoyue Wang , Xueliang Zhang , Shaohong Wu , Linsheng Yang , Wenhui Jiang , Lunwei Zhang","doi":"10.1016/j.apenergy.2026.127495","DOIUrl":"10.1016/j.apenergy.2026.127495","url":null,"abstract":"<div><div>Under the dual context of rapid AI-driven digital expansion and green, low-carbon transition, understanding how renewable energy supports computing power and how global environmental change—particularly extreme climate events—shapes the spatial heterogeneity of China's electricity–computing integration has become a pressing scientific challenge. By integrating energy supply, computing demand, and environmental constraints (including mean and extreme climate conditions), this study develops a three-dimensional framework to assess the suitability of computing power centers (CPCs) and establish a new spatial paradigm for electricity–computing synergy in China. The results reveal pronounced spatial mismatches among the three dimensions. From the energy perspective, the energy supply is evolving into distinct renewable clusters, with the Northwest and Tibetan Plateau hosting 87.25% of national photovoltaic and 79.77% of wind potential, while the Southwest anchors a hydropower cluster accounting for 63% of national installed capacity. However, environmental constraints act as a spatial filter; Northern regions maximize suitability by leveraging natural cooling to balance Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE), whereas Southern and Western regions are constrained by intensifying heatwaves and seismic risks, respectively. Conversely, computing demand remains heavily concentrated in Eastern coastal agglomerations. To resolve these structural imbalances, we propose a functional zoning paradigm comprising energy-oriented, demand-driven, and incremental-development regions. We further demonstrate that prioritizing this energy-computing coupling substantially improves operational efficiency and reduces carbon footprints. This spatial paradigm promotes interregional complementarity and facilitates the decoupling of Artificial Intelligence expansion from carbon emissions, providing a scientific basis for the “East-Data, West-Computing” initiative.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127495"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170935","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}
Pub Date : 2026-04-15Epub Date: 2026-02-02DOI: 10.1016/j.apenergy.2026.127461
Mehmet Kurtoğlu , Fatih Eroğlu
Battery energy storage systems (BESSs) play a significant role in increasing the performance of solar photovoltaic (PV) systems by reducing the adverse effects of intermittency of power generated by solar PV systems. Over the past decade, the integration of BESS technology with solar PV systems has gained significant attention in residential applications, electric vehicle charging stations, and large-scale grid-connected systems. Although numerous studies on solar PV-integrated BESS, a review that holistically covers all key aspects such as DC-DC converter topologies, maximum power point tracking (MPPT) methods, optimization approaches, and energy management systems (EMSs) is still missing. While existing studies focus on these topics separately, there remains a research gap that combines standalone and grid-scale applications of solar PV-integrated BESS. The novelty of this study is that it presents a systematic and up-to-date review of solar PV-integrated BESS by highlighting DC-DC converter topologies, MPPT methods, optimization methodologies, and EMS strategies. In this context, a total of 166 articles have been carefully chosen from an initial pool of about 35,000 using criteria such as article type, language, database indexing, recency, and, most importantly, relevance to the main topic, with sources limited to reputable publishers. This paper not only presents an overview of the DC-DC converter topologies, MPPT methods, optimization methods and EMS of solar PV-integrated BESS but also provides a detailed review, basically with regard to the latest publications of it. Moreover, as a future outlook, potential research directions and recommendations are outlined to overcome the current research gaps and topics of solar PV-integrated BESS. By presenting a comprehensive overview and a critical analysis of recent developments, this review aims to serve as an essential reference for researchers and industry professionals working on solar PV-integrated BESS technologies.
{"title":"Current trends and challenges in solar PV-integrated battery energy storage technology: Key components, methods, and future prospects","authors":"Mehmet Kurtoğlu , Fatih Eroğlu","doi":"10.1016/j.apenergy.2026.127461","DOIUrl":"10.1016/j.apenergy.2026.127461","url":null,"abstract":"<div><div>Battery energy storage systems (BESSs) play a significant role in increasing the performance of solar photovoltaic (PV) systems by reducing the adverse effects of intermittency of power generated by solar PV systems. Over the past decade, the integration of BESS technology with solar PV systems has gained significant attention in residential applications, electric vehicle charging stations, and large-scale grid-connected systems. Although numerous studies on solar PV-integrated BESS, a review that holistically covers all key aspects such as DC-DC converter topologies, maximum power point tracking (MPPT) methods, optimization approaches, and energy management systems (EMSs) is still missing. While existing studies focus on these topics separately, there remains a research gap that combines standalone and grid-scale applications of solar PV-integrated BESS. The novelty of this study is that it presents a systematic and up-to-date review of solar PV-integrated BESS by highlighting DC-DC converter topologies, MPPT methods, optimization methodologies, and EMS strategies. In this context, a total of 166 articles have been carefully chosen from an initial pool of about 35,000 using criteria such as article type, language, database indexing, recency, and, most importantly, relevance to the main topic, with sources limited to reputable publishers. This paper not only presents an overview of the DC-DC converter topologies, MPPT methods, optimization methods and EMS of solar PV-integrated BESS but also provides a detailed review, basically with regard to the latest publications of it. Moreover, as a future outlook, potential research directions and recommendations are outlined to overcome the current research gaps and topics of solar PV-integrated BESS. By presenting a comprehensive overview and a critical analysis of recent developments, this review aims to serve as an essential reference for researchers and industry professionals working on solar PV-integrated BESS technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127461"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170936","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}
Pub Date : 2026-04-15Epub Date: 2026-02-11DOI: 10.1016/j.apenergy.2026.127503
Patrick Jürgens , Paul Müller , Fritz Brandhuber , Christoph Kost
To reflect the complexity of the energy transition, a current challenge in modeling national energy transition pathways is to combine high resolution in time, space, techno-economic and sector coupling details in a single model. To address this challenge, the paper discusses improvements in the simulation-based optimization approach, which is used by the energy system model REMod as an alternative to the widely used linear optimization approach. The REMod model uses a simulation of the energy system coupled with a black-box optimization algorithm to optimize the transformation path. To limit the computational complexity, while taking into account the hourly operation of the energy system along the whole transformation path, various aspects have to be considered: performance of the simulation, choice of the optimization algorithm and selection of the termination criterion and population size. The model employs a novel method of endogenous interpolation to incorporate the entire transformation path into the objective function, and it can be evaluated in parallel. This allows for an increase in complexity both in technological details and in geographical resolution, enabling a long-term energy system model to be solved that is unique in terms of its techno-economic and sector coupling details, temporal resolution, and multi-regional representation. The approach cannot guarantee global optimality, which leads to variance in the results that requires careful consideration when interpreting them. With its system-wide focus on transition pathways, the model complements existing models that excel in specific sectors or operational optimization, for example.
{"title":"Improving the simulation-based optimization in the REMod model to deal with complexity in energy system modeling","authors":"Patrick Jürgens , Paul Müller , Fritz Brandhuber , Christoph Kost","doi":"10.1016/j.apenergy.2026.127503","DOIUrl":"10.1016/j.apenergy.2026.127503","url":null,"abstract":"<div><div>To reflect the complexity of the energy transition, a current challenge in modeling national energy transition pathways is to combine high resolution in time, space, techno-economic and sector coupling details in a single model. To address this challenge, the paper discusses improvements in the simulation-based optimization approach, which is used by the energy system model REMod as an alternative to the widely used linear optimization approach. The REMod model uses a simulation of the energy system coupled with a black-box optimization algorithm to optimize the transformation path. To limit the computational complexity, while taking into account the hourly operation of the energy system along the whole transformation path, various aspects have to be considered: performance of the simulation, choice of the optimization algorithm and selection of the termination criterion and population size. The model employs a novel method of endogenous interpolation to incorporate the entire transformation path into the objective function, and it can be evaluated in parallel. This allows for an increase in complexity both in technological details and in geographical resolution, enabling a long-term energy system model to be solved that is unique in terms of its techno-economic and sector coupling details, temporal resolution, and multi-regional representation. The approach cannot guarantee global optimality, which leads to variance in the results that requires careful consideration when interpreting them. With its system-wide focus on transition pathways, the model complements existing models that excel in specific sectors or operational optimization, for example.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127503"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171025","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}
Pub Date : 2026-04-15Epub Date: 2026-02-04DOI: 10.1016/j.apenergy.2026.127469
Sebastián Madrigal, Ramon Gallinad, Jose L. Vicario, Antoni Morell, Ramon Vilanova
Energy communities operate under collective self-consumption schemes, where locally generated renewable energy is shared among participating members. In practice, this sharing is commonly governed by static allocation coefficients fixed in advance, which do not capture the time-varying and heterogeneous demand of participants. This mismatch can reduce community self-consumption, increase surplus injections, and raise reliance on the grid. This paper proposes a data-driven framework to dynamically compute allocation coefficients based on predicted individual demand and demonstrates its application in a municipal energy community in Catalonia, Spain. The approach uses an extreme gradient boosting model to forecast hourly consumption profiles and then derive adaptive allocation coefficients that better align shared photovoltaic generation with expected demand. The proposed strategy is evaluated against a static baseline and alternative dynamic schemes using multiple performance indicators, including community self-consumption, surplus energy, and grid dependency. In the case study, the extreme gradient boosting-based allocation increases community self-consumption by 8.4%, reduces surplus energy by 34%, and lowers grid dependency by up to 30% for key members, resulting in a more balanced and efficient distribution of locally generated energy. These results highlight the potential of machine learning-enabled allocation to improve collective self-consumption performance in the existing regulatory framework.
{"title":"Improving energy distribution in collective self-consumption via XGBoost-based allocation coefficients prediction","authors":"Sebastián Madrigal, Ramon Gallinad, Jose L. Vicario, Antoni Morell, Ramon Vilanova","doi":"10.1016/j.apenergy.2026.127469","DOIUrl":"10.1016/j.apenergy.2026.127469","url":null,"abstract":"<div><div>Energy communities operate under collective self-consumption schemes, where locally generated renewable energy is shared among participating members. In practice, this sharing is commonly governed by static allocation coefficients fixed in advance, which do not capture the time-varying and heterogeneous demand of participants. This mismatch can reduce community self-consumption, increase surplus injections, and raise reliance on the grid. This paper proposes a data-driven framework to dynamically compute allocation coefficients based on predicted individual demand and demonstrates its application in a municipal energy community in Catalonia, Spain. The approach uses an extreme gradient boosting model to forecast hourly consumption profiles and then derive adaptive allocation coefficients that better align shared photovoltaic generation with expected demand. The proposed strategy is evaluated against a static baseline and alternative dynamic schemes using multiple performance indicators, including community self-consumption, surplus energy, and grid dependency. In the case study, the extreme gradient boosting-based allocation increases community self-consumption by 8.4%, reduces surplus energy by 34%, and lowers grid dependency by up to 30% for key members, resulting in a more balanced and efficient distribution of locally generated energy. These results highlight the potential of machine learning-enabled allocation to improve collective self-consumption performance in the existing regulatory framework.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127469"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171152","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}
Pub Date : 2026-04-15Epub Date: 2026-02-09DOI: 10.1016/j.apenergy.2026.127492
Rong Shi , Yue Chen , Shuxia Yang , Xiongfei Wang
The extension of the green hydrogen value chain plays a vital role in optimizing the energy structure and advancing the development of clean energy. However, the storage and transportation of green hydrogen pose a significant challenge to its expansion. As a promising hydrogen carrier, green e-methanol is expected to provide an effective pathway for promoting the application of green hydrogen. This work constructs a complex network evolutionary game model within a multi-market coupling context to investigate the strategic evolution of value co-creation in a renewable‑hydrogen-methanol value chain (RHMV) consisting of renewable energy, green hydrogen, and methanol producers. It further examines the effects of key influencing factors. The study yields the following findings: (1) A portion of participants in the RHMV are inclined to engage in value co-creation. (2) When the initial willingness to cooperate is low, value co-creation cannot be realized, and the effect of increasing this parameter differs heterogeneously among different participating entities. (3) Carbon quota prices, government subsidies, e-methanol prices, and green certificate prices all possess incentive thresholds, and their incentive effects are nonlinear. (4) There are feasible ranges for hydrogen production energy consumption and the electricity price fluctuation coefficient, with the optimal values differing across various entities within the RHMV. Finally, corresponding policy recommendations are provided based on the research findings.
{"title":"Value co-creation in the renewable energy-hydrogen-methanol chain: A complex network evolutionary game under multi-market contexts","authors":"Rong Shi , Yue Chen , Shuxia Yang , Xiongfei Wang","doi":"10.1016/j.apenergy.2026.127492","DOIUrl":"10.1016/j.apenergy.2026.127492","url":null,"abstract":"<div><div>The extension of the green hydrogen value chain plays a vital role in optimizing the energy structure and advancing the development of clean energy. However, the storage and transportation of green hydrogen pose a significant challenge to its expansion. As a promising hydrogen carrier, green e-methanol is expected to provide an effective pathway for promoting the application of green hydrogen. This work constructs a complex network evolutionary game model within a multi-market coupling context to investigate the strategic evolution of value co-creation in a renewable‑hydrogen-methanol value chain (RHMV) consisting of renewable energy, green hydrogen, and methanol producers. It further examines the effects of key influencing factors. The study yields the following findings: (1) A portion of participants in the RHMV are inclined to engage in value co-creation. (2) When the initial willingness to cooperate is low, value co-creation cannot be realized, and the effect of increasing this parameter differs heterogeneously among different participating entities. (3) Carbon quota prices, government subsidies, e-methanol prices, and green certificate prices all possess incentive thresholds, and their incentive effects are nonlinear. (4) There are feasible ranges for hydrogen production energy consumption and the electricity price fluctuation coefficient, with the optimal values differing across various entities within the RHMV. Finally, corresponding policy recommendations are provided based on the research findings.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127492"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171449","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}
Pub Date : 2026-04-15Epub Date: 2026-02-09DOI: 10.1016/j.apenergy.2026.127371
Dafeng Zhu , Sicheng Liu , Bo Yang , Haoran Deng , Yu Wu , Zhao Yang Dong
AI-driven digitalization is intensifying data center (DC) energy use and emissions, while variable demands and renewables exacerbate supply-demand and carbon allocation-emission imbalances. These challenges are further compounded by existing strategies that often overlook carbon-energy coupling and lack real-time, incentive-compatible coordination across DCs and energy resources. To address these challenges and satisfy online demands, a tiered carbon trading and capture coordination model is proposed, along with a multi-energy market framework integrating energy storage, an electrolyzer and a combined cooling and power unit, to maximize overall benefits and fully absorb renewable energy. Then, an improved stochastic optimization constructs virtual queues and introduces an auxiliary variable to ensure charge/discharge benefits and system stability while decarbonizing DCs without requiring priori information of system random processes. To avoid privacy leakage, a distributed energy clearing method is applied to facilitate low-complexity trading among DCs. Through case studies, the proposed method can reduce carbon emissions and approach the optimal costs while mitigating battery degradation.
{"title":"Energy optimization for data centers via carbon-aware multi-energy market coordination","authors":"Dafeng Zhu , Sicheng Liu , Bo Yang , Haoran Deng , Yu Wu , Zhao Yang Dong","doi":"10.1016/j.apenergy.2026.127371","DOIUrl":"10.1016/j.apenergy.2026.127371","url":null,"abstract":"<div><div>AI-driven digitalization is intensifying data center (DC) energy use and emissions, while variable demands and renewables exacerbate supply-demand and carbon allocation-emission imbalances. These challenges are further compounded by existing strategies that often overlook carbon-energy coupling and lack real-time, incentive-compatible coordination across DCs and energy resources. To address these challenges and satisfy online demands, a tiered carbon trading and capture coordination model is proposed, along with a multi-energy market framework integrating energy storage, an electrolyzer and a combined cooling and power unit, to maximize overall benefits and fully absorb renewable energy. Then, an improved stochastic optimization constructs virtual queues and introduces an auxiliary variable to ensure charge/discharge benefits and system stability while decarbonizing DCs without requiring priori information of system random processes. To avoid privacy leakage, a distributed energy clearing method is applied to facilitate low-complexity trading among DCs. Through case studies, the proposed method can reduce carbon emissions and approach the optimal costs while mitigating battery degradation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127371"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171453","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}
Pub Date : 2026-04-15Epub Date: 2026-02-06DOI: 10.1016/j.apenergy.2026.127463
Chi Liu , Zhezhuang Xu , Jiawei Zhou , Yazhou Yuan , Kai Ma , Meng Yuan
The substantial energy demands of buildings are increasingly supplied by renewable sources like photovoltaics. However, their intermittency necessitates the integration of stationary energy storage systems (ESS) within building energy management systems (BEMS) to stabilize power and coordinate multi-energy flows. The proliferation of electric vehicles (EVs) facilitates their integration with ESS, forming a combined battery system (CBS) that expands the arbitrage potential and flexibility of BEMS. To fully exploit the potential of CBS in optimizing BEMS operational costs, this paper proposes a deep reinforcement learning (DRL) real-time joint energy scheduling method based on heterogeneous battery systems. We first analyze the aging characteristics of different battery types within the CBS, and propose an innovative degradation assessment framework tailored to heterogeneous energy storage systems in vehicle-to-grid scenarios. This framework introduces a cycle degradation coefficient to provide real-time feedback on battery aging costs, making it suitable for DRL-driven scheduling. To achieve optimized collaborative scheduling of ESS and EVs, we propose an enhanced DRL algorithm incorporating double dueling and prioritized experience replay mechanisms. This algorithm addresses challenges such as complex state features, action coupling, and decreased learning efficiency in heterogeneous energy storage environments. It also prioritizes the travel demands of EV users to promote their participation. Experimental simulations from a real-world commercial building validate the effectiveness of the proposed approach, achieving a 43.39% reduction in system operating costs compared to the mixed-integer linear programming approach under equivalent conditions.
{"title":"Deep reinforcement learning-based energy scheduling for green buildings with stationary and EV batteries of heterogeneous characteristics","authors":"Chi Liu , Zhezhuang Xu , Jiawei Zhou , Yazhou Yuan , Kai Ma , Meng Yuan","doi":"10.1016/j.apenergy.2026.127463","DOIUrl":"10.1016/j.apenergy.2026.127463","url":null,"abstract":"<div><div>The substantial energy demands of buildings are increasingly supplied by renewable sources like photovoltaics. However, their intermittency necessitates the integration of stationary energy storage systems (ESS) within building energy management systems (BEMS) to stabilize power and coordinate multi-energy flows. The proliferation of electric vehicles (EVs) facilitates their integration with ESS, forming a combined battery system (CBS) that expands the arbitrage potential and flexibility of BEMS. To fully exploit the potential of CBS in optimizing BEMS operational costs, this paper proposes a deep reinforcement learning (DRL) real-time joint energy scheduling method based on heterogeneous battery systems. We first analyze the aging characteristics of different battery types within the CBS, and propose an innovative degradation assessment framework tailored to heterogeneous energy storage systems in vehicle-to-grid scenarios. This framework introduces a cycle degradation coefficient to provide real-time feedback on battery aging costs, making it suitable for DRL-driven scheduling. To achieve optimized collaborative scheduling of ESS and EVs, we propose an enhanced DRL algorithm incorporating double dueling and prioritized experience replay mechanisms. This algorithm addresses challenges such as complex state features, action coupling, and decreased learning efficiency in heterogeneous energy storage environments. It also prioritizes the travel demands of EV users to promote their participation. Experimental simulations from a real-world commercial building validate the effectiveness of the proposed approach, achieving a 43.39% reduction in system operating costs compared to the mixed-integer linear programming approach under equivalent conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127463"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171560","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}
Pub Date : 2026-04-15Epub Date: 2026-02-09DOI: 10.1016/j.apenergy.2026.127483
Yichi Zhang , Xiongzheng Wang , Gongzhe Nie , Chengyao He , Yunfan Yang , Mingzhi He , Xin Meng
To address real-time control failures in renewable energy hydrogen production systems (REHPS) caused by forecasting errors and optimization delays, this paper proposes a rolling horizon framework embedded with a rule-based compensation mechanism to enhance the real-time performance and controllability of optimal scheduling in practical applications. A mixed-integer linear programming model is formulated using multi-state nonlinear electrolyzer modeling and time-of-use electricity prices to optimize the start-stop sequences and power allocation of electrolyzer clusters. By integrating the rule-based compensation within the rolling window, dispatch decisions are effectively corrected to achieve real-time response. Clustering analysis based on a real-world wind power dataset shows that the proposed strategy increases the wind utilization rate by an average of 7.224% and the system hydrogen production efficiency by 5.97%, compared to the best rule-based strategy (S1). Furthermore, the sensitivity analysis of the strategy demonstrates its robustness across varying prediction accuracy levels. The proposed rule-based compensator enables the deployment of a centralized optimizer onto an industrial-grade real-time controller without requiring additional hardware. This solution has been applied in the preliminary design study of a hundred-megawatt-scale REHPS in Northeast China, providing a deployable optimization solution for large-scale renewable hydrogen production.
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