Energy, Engineering, Transportation, Civil and Structural Engineering
94
Scopus Publications
5800
Scholar Citations
41
Scholar h-index
96
Scholar i10-index
Scopus Publications
Techno-economic optimization of solar thermal and pit thermal energy storage under spatial and hydraulic constraints in district heating network Puneet Saini, Chris Bales, Pei Huang Renewable Energy, 2026 District heating (DH) systems in Sweden are facing growing challenges due to rising biomass prices and uncertainty surrounding future supply availability. Integrating solar thermal collectors with pit thermal energy storage (PTES) offers a promising pathway for reducing dependence on biomass and improving system sustainability. However, the optimal integration and techno-economic performance of such systems under real-world limitations (such as spatial and hydraulic constraints) remain largely underexplored. This study develops a two-stage modeling framework to evaluate solar + PTES integration while accounting for practical constraints such as sitting feasibility and discharge power limitations. A Python-based simplified model first generates a sensitivity matrix mapping heat delivery performance across a wide range of collector areas and storage volumes, which guides the selection of PTES sizes for subsequent detailed modelling in TRNSYS. Three system configurations are analyzed: a real-world case with waste heat and pipe limitations, a generalized case without waste heat but with pipe constraints, and an ideal case without any integration barriers. Monte Carlo simulations and sensitivity analyses are performed to quantify economic uncertainties and identify key influencing factors. Results show that discharge power limitations and pipe constraints significantly penalise the system performance, leading to higher levelized costs of heat (LCOH) compared with the ideal benchmark. The findings highlight the necessity of simultaneous consideration of spatial and techno-economic constraints to achieve cost-effective solar fractions and optimal storage design in real-world DH systems. • Quantify the impact of spatial and hydraulic constraints on solar + PTES in DH. • Employ a two-stage framework: Python pre-sizing and detailed TRNSYS simulations. • Conduct Monte Carlo and sensitivity analysis to assess economic uncertainties. • Show that pipe discharge limitations reduce solar fraction and increase LCOH • Reveal that optimal pipe sizing depends on target solar fraction and waste heat availability.
Techno-economic assessment of electric vehicle-based decentralized electricity delivery for enhanced power resilience during outages Rehman Zafar, Asitha Sanjaya Indatissa, Pei Huang, Anna-Lena Lane, BjornssonLars-Henrik Björnsson Energy Reports, 2026 The integration of electric vehicles (EVs) into power systems offers a promising pathway to enhance energy resilience during grid outages. Most of the existing studies treat EV outage support as a short-term, technical problem, with limited attention to economics, business models (e.g., ownership and stakeholders), or long-term outages. To bridge the gap, this study presents a comprehensive framework to evaluate the techno-economic feasibility of using EV mobility as a decentralized electricity delivery solution during both short-term and long-term outages. A rule-based control strategy is developed to operate EVs under realistic operational constraints, with the primary objective of maximizing energy delivery and minimizing unmet demand during outages. The framework incorporates real-world data, including household and critical load profiles, EV battery capacities, ownership types, outage durations, travel distances, and seasonal variations. Scenario-based simulations are performed to compare various technical and economical key performance indicators. Results show that EVs with personal ownership type and large capacity (100 kWh) can effectively meet residential energy needs during short-term outages. In long-term outages, expanding the EV fleet effectively reduce unmet demand, and higher solar generation in summer and spring significantly improves energy availability compared to winter, where unmet load is more than twice as high under identical conditions. The findings offer practical insights for stakeholders and policymakers in developing EV-based resilience strategies for future energy systems. Furthermore, the study highlights the importance of designing appropriate business models to support the integration of mobile energy services into resilience planning. • Techno-economic framework to assess EVs as mobile energy for short/long outage resilience. • Rule-based EV dispatch cuts unmet demand considering battery, mobility, ownership limits. • Scenario evaluation using KPIs for technical reliability and economic viability. • Business/ownership models exploration outlines market paths and operational challenges. • Policy and governance guidance for scalable and cost-effective EV-based resilience.
Combined hosting capacity analysis for electric vehicles and rooftop photovoltaic systems – impacts on a Swedish residential power grid Maria Sandström, Pei Huang, Chris Bales, Erik Dotzauer Energy, 2026 Electric vehicles (EVs) and solar photovoltaic (PV) systems pose challenges to existing power grids, which were often not designed to accommodate such large loads or local generation. With growing adoption of these technologies, hosting capacity (HC) studies are essential for understanding their impact and supporting future planning. However, combined HC assessments of EVs and PVs remain limited compared to studies treating the technologies separately, particularly when accounting for spatial uncertainties, temporal correlations, varying implementation levels, and different charging strategies. This study addresses this gap by applying a broadly applicable method for calculating the HC of EVs and PV systems in a Swedish residential grid. A combined time-series and stochastic approach was used across multiple scenarios, supported by geographic information system-based (GIS-based) PV potential analysis. The analysis is based on real-world data and performed as a case study, providing new insights into joint EV-PV integration. Results show that spot price-based charging resulted in the most frequent violations. Evenly distributed charging was the most grid-friendly strategy in winter, while PV generation caused significant violations in summer regardless of charging strategy. Under the even charging strategy, the average number of unique buses with voltage violations during summer was 11–63 times higher than during winter. This is because the strategy uses low power, limiting the impact of EV charging, while high uncontrolled PV production in summer becomes the main driver of voltage problems. The findings highlight the need for strategic charging strategies and additional measures to manage the grid impact of high PV implementation. • Broadly applicable method assesses joint EV–PV hosting capacity using real-world data. • Combined time-series and stochastic analysis captures temporal and spatial uncertainties. • Grid-friendly charging eases EV impacts while PV-driven issues remain challenging. • Voltage violations under even charging are 11–63 times higher in summer than winter.
Enhancing grid hosting capacity through an Electric Vehicle based virtual Electricity Network (EVEN) Pei Huang, Rehman Zafar Journal of Energy Storage, 2025 Large-scale electric vehicle (EV) adoption is placing increasing pressure on distribution networks with limited hosting capacity (HC). While many smart charging strategies have been proposed to enhance HC, most assume EVs are stationary loads and overlook their inherent mobility. This study proposes a novel EV -based virtual E lectricity N etwork (EVEN) concept, where EVs charge in capacity-rich networks and discharge in capacity-constrained areas, effectively transferring energy without using the physical grid. This mobile electricity delivery mechanism enables HC enhancement in weak networks. We develop an integrated framework that combines inter-network energy delivery optimization, stochastic time-series HC analysis, and battery degradation modeling. The framework is applied to two real-world systems: a 50-bus rural residential network with limited HC and a 76-bus industrial network with surplus capacity. Simulation results show that shifting just 10 % of evening demand from the rural to the industrial network can reduce total undervoltage by 80 % and voltage violations by 65 %, with only minimal side effects upstream. While the EVEN solution increases battery cycling degradation, it reduces calendar degradation, resulting in overall battery wear comparable to non-EVEN scenarios. This is one of the first studies to quantify the system-level HC benefits of EV-mediated energy delivery while accounting for battery health impacts. The results suggest that EVEN solution is a scalable, cost-effective alternative to physical grid reinforcement, offering a practical means of transforming EVs into mobile grid-support assets under normal operating conditions. • Propose an EV -based virtual e lectricity n etwork (EVEN) enabling inter-network energy transfer • Show that shifting load between networks can significantly relieve grid constraints • Effectively reduce total undervoltage by 80 % and violation counts by 65 % in the weak network • Observe comparable total battery wear as increased battery cycling offsets reduced calendar aging • Identify EVEN as a scalable, cost-effective non-wires alternative to traditional grid reinforcement.
Do smart charging and vehicle-to-grid strengthen or strain power grids with rising EV adoption? Insights from a Swedish residential network Pei Huang, Maria Sandström Applied Energy, 2025 The rapid growth of electric vehicles (EVs) is placing new demands on residential distribution networks. Smart charging and Vehicle-to-Grid (V2G) technologies offer potential solutions for mitigating the large peak load and enhancing the grid hosting capacity (HC), yet their effectiveness across varying EV penetration levels remains underexplored. Therefore, this study evaluates how EV penetration levels affect the effectiveness of these two technologies. Furthermore, to help improve the grid performances, this study also pioneeringly proposes two hypothetical pricing settings—diverse electricity buying prices and reduced electricity selling prices—and evaluates their performances by comparing with existing price settings. Using real-world network and EV charging data from Sweden, we assess peak load and HC under different scenarios of power flow direction, charging controls, and electricity prices strategies across eight EV penetration levels. Results reveal that coordinated charging, especially with V2G, more effectively reduces peak loads compared to individual controls. However, V2G, if not regulated well, can increase peak loads at high EV penetration levels. Diversified electricity buying prices help lower aggregated peak loads but are less effective in enhancing local HC due to peak load shifting rather than peak load reduction. Additionally, high electricity selling prices benefit at low EV penetration but become less effective as penetration grows. The findings suggest that electricity pricing strategies and charging controls should adapt dynamically to the level of EV penetration. These insights provide critical guidance to policymakers, distribution system operators, and aggregators in designing adaptive pricing and control strategies to integrate EVs without overburdening the grid.
Enhancing electric vehicle charging load prediction in data-scarce scenarios: A hybrid deep learning-based approach integrating clustering analysis and transfer learning Rehman Zafar, Pei Huang, Yongjun Sun Energy and AI, 2025 Accurate electric vehicle (EV) load forecasting is crucial for efficient grid operations and demand-side management, yet it is challenging in data-scarce scenarios. Transfer learning (TL) offers a solution by transferring knowledge from data-rich to data-limited scenarios. However, when the knowledge domain exhibits highly diverse behaviors, applying TL alone could introduce large biases, reducing accuracy and limiting its effectiveness. To address this problem, this study proposes a hybrid deep learning-based framework that integrates TL and K-means clustering. The proposed approach consists of two phases. In the source domain phase, a deep-learning-based model is trained using the full dataset and then fine-tuned using clustered user behaviors. In the target domain phase with limited data, TL is applied to transfer knowledge from the source-domain fine-tuned cluster models. For validation, the developed prediction method has been tested using real-world datasets and compared with two other cases: one with applying TL from the source-domain base model trained from full dataset, and one without applying TL. Results show the hybrid method improves forecasting accuracy, reducing the normalized root mean squared error by 3.99% and 8.22%, respectively. This study establishes a structured approach for targeted knowledge transfer, enhancing prediction accuracy in data-scarce settings. The framework is scalable and adaptable to other energy forecasting applications, supporting sustainable and resilient energy management.
Transforming electric vehicles into mobile power sources: technical performance evaluation of the electric vehicle based virtual electricity network (EVEN) solution for improving the power supply resilience Pei Huang, Abadi Kidanemariam, Lars-Henrik Bjornsson Sustainable Cities and Society, 2025 The growing frequency of power grid disruptions demands innovative solutions to enhance supply resilience. Electric vehicle (EV) fleets, as mobile energy storage units, offer a sustainable response to prolonged outages by forming an EV-based virtual electricity network (EVEN), which transfers electricity from functioning to affected areas. Unlike existing studies focusing on individual EVs as backup, this paper addresses this gap by developing a generic model for the EVEN solution and a method for coordinating electricity delivery via EVs. The model features a central emergency hub which functions during main grid failures and EV-equipped households. Using a real-world system in Sweden, the EVEN model is evaluated for resilience metrics, including days without energy deficits, electricity delivery, and battery degradation. Additionally, seven key parameters—energy supply, demand, EV configuration, and renewable energy systems—are analyzed to identify optimal conditions. Results indicate that the EVEN solution significantly enhances power supply resilience, particularly for small energy users, with small battery degradation, and is most effective when households are near the central hub. This study advances understanding of EVs in strengthening grid resilience, offering a resource-efficient, scalable and sustainable solution for future energy security.
Techno-economical assessment of battery storage combined with large-scale Photovoltaic power plants operating on energy and Ancillary Service Markets Mohamad Koubar, Oskar Lindberg, David Lingfors, Pei Huang, Magnus Berg, Joakim Munkhammar Applied Energy, 2025 A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.
Uncertainty analysis of peak cooling load calculation for HVAC system design Indoor Air 2014 13th International Conference on Indoor Air Quality and Climate, 2014
RECENT SCHOLAR PUBLICATIONS
Techno-economic assessment of electric vehicle-based decentralized electricity delivery for enhanced power resilience during outages R Zafar, AS Indatissa, P Huang, AL Lane, BLH Björnsson Energy Reports 15, 108947 , 2026 2026 Citations: 1
Techno-economic optimization of solar thermal and pit thermal energy storage under spatial and hydraulic constraints in district heating network P Saini, C Bales, P Huang Renewable energy, 125820 , 2026 2026
Midbrain Energy Homeostasis Biomarker for Differential Diagnosis of Early-Stage Parkinson Disease: A 1 H and 31 P MRI Study Q Yu, Y Zhang, P Huang, Y Li, P Liu, Z Jin, X Wang, B Zhang, J Weng, ... Radiology 319 (1), e251533 , 2026 2026
Gut microbiota and SCFA biomarkers for early diagnosis of PD patients and differentiation of its motor subtypes P Zhang, J Du, C Gao, J Liu, M Huang, H Li, X Shen, Y Tan, S Chen, ... npj Parkinson's Disease , 2026 2026
Combined hosting capacity analysis for electric vehicles and rooftop photovoltaic systems–impacts on a Swedish residential power grid M Sandström, P Huang, C Bales, E Dotzauer Energy, 140805 , 2026 2026
Integration of Volumetric, Iron, and Neuromelanin Magnetic Resonance Imaging Measures Effectively Differentiates Parkinson's Disease from Multiple System Atrophy Y Liu, B Xiao, P Huang, Y Li, X Wang, Y Zhang, Z Jin, F Liu, EM Haacke, ... Movement Disorders 41 (3), 750-760 , 2026 2026
A Python Model for Design Optimisation of Solar District Heating with Pit Thermal Energy Storage P Saini, P Huang, C Bales 4th International Sustainable Energy Conference–ISEC 2026. Conference for … , 2026 2026
Enhancing grid hosting capacity through an Electric Vehicle based virtual Electricity Network (EVEN) P Huang, R Zafar Journal of Energy Storage 140, 119028 , 2025 2025 Citations: 3
Do smart charging and vehicle-to-grid strengthen or strain power grids with rising EV adoption? Insights from a Swedish residential network P Huang, M Sandström Applied Energy 401, 126713 , 2025 2025 Citations: 5
A Four‐Arm Randomized Controlled Trial on the Effects of Multi‐Dimensional Cognitive Intervention in Parkinson's Disease Patients with Mild Cognitive Impairment and Subjective … P Huang, C Gao, Y Tan, S Chen, Y Ling, Z Chen, Z Zhou, H Xia Alzheimer's & Dementia 21, e098518 , 2025 2025
Enhancing electric vehicle charging load prediction in data-scarce scenarios: A hybrid deep learning-based approach integrating clustering analysis and transfer learning R Zafar, P Huang, Y Sun Energy and AI 21, 100545 , 2025 2025 Citations: 17
Electric Vehicle Based Virtual Electricity Network (EVEN) Solution for Performance Enhancement in Distribution Networks P Huang, R Zafar Nordic Energy Informatics Academy Conference, 299-308 , 2025 2025 Citations: 1
GABA outperforms iron and neuromelanin in detecting nigrostriatal alterations in early-stage Parkinson’s disease with RBD Y Zhang, P Huang, P Liu, Y Li, Z Jin, Q Yu, X Shi, Y Liu, Z Cheng, P Wu, ... npj Parkinson's Disease 11 (1), 229 , 2025 2025 Citations: 2
Transforming electric vehicles into mobile power sources: technical performance evaluation of the electric vehicle based virtual electricity network (EVEN) solution for … P Huang, A Kidanemariam, LH Bjornsson Sustainable cities and society 128, 106477 , 2025 2025 Citations: 8
White matter hyperintensity tissue property spatial variations as a function of cognitive status in Parkinson’s disease M Reheman, S Buch, N He, P Huang, Q Yu, X Wang, Y Liu, Y Zhang, Z Jin, ... NeuroImage 312, 121236 , 2025 2025 Citations: 4
Techno-economical assessment of battery storage combined with large-scale Photovoltaic power plants operating on energy and Ancillary Service Markets M Koubar, O Lindberg, D Lingfors, P Huang, M Berg, J Munkhammar Applied Energy 382, 125200 , 2025 2025 Citations: 31
Multiscale hybrid surface structure modifications for enhanced pool boiling heat transfer: State-of-the-art review Q Wang, H Ren, P Huang, D Gao, Y Sun Renewable and Sustainable Energy Reviews 208, 115018 , 2025 2025 Citations: 33
A Techno-Economic Framework for Using Electric Vehicle Mobility to Support Household Energy During Short-Term Power Outages R Zafar, AS Indatissa, P Huang Applied Energy Symposium and Forum: Resilient energy systems, Resilient 2025 … , 2025 2025
Solar district heating system with pit thermal energy storage and heat pump: Techno-economic analysis for a Swedish case study P Saini, I Öhrström, J Öhrström, C Bales, P Huang Applied Energy Symposium and Forum: Resilient energy systems September 23-25 … , 2025 2025
Economic and Energy Matching Analysis of Vehicle-to-Grid with Various Prosumer Profiles and Grid Tariffs M Koubar, R Fachrizal, P Huang, D Lingfors, M Berg, J Munkhammar 2025
MOST CITED SCHOLAR PUBLICATIONS
Mild cognitive impairment has similar alterations as Alzheimer's disease in gut microbiota B Li, Y He, J Ma, P Huang, J Du, L Cao, Y Wang, Q Xiao, H Tang, S Chen Alzheimer's & Dementia 15 (10), 1357-1366 , 2019 2019 Citations: 501
A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating P Huang, B Copertaro, X Zhang, J Shen, I Löfgren, M Rönnelid, J Fahlen, ... Applied energy 258, 114109 , 2020 2020 Citations: 474
A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties S Zhang, P Huang, Y Sun Energy 94, 654-665 , 2016 2016 Citations: 199
Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system P Huang, M Lovati, X Zhang, C Bales, S Hallbeck, A Becker, H Bergqvist, ... Applied Energy 255, 113864 , 2019 2019 Citations: 172
HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique P Huang, G Huang, Y Wang Energy and Buildings 91, 26-36 , 2015 2015 Citations: 161
Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease Y Qian, X Yang, S Xu, P Huang, B Li, J Du, Y He, B Su, LM Xu, L Wang, ... Brain 143 (8), 2474-2489 , 2020 2020 Citations: 160
A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered P Huang, M Lovati, X Zhang, C Bales Applied energy 268, 114983 , 2020 2020 Citations: 136
Imaging the Nigrosome 1 in the substantia nigra using susceptibility weighted imaging and quantitative susceptibility mapping: An application to Parkinson's disease Z Cheng, N He, P Huang, Y Li, R Tang, SK Sethi, K Ghassaban, ... NeuroImage: Clinical 25, 102103 , 2020 2020 Citations: 130
Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements P Huang, Y Sun, M Lovati, X Zhang Energy 222, 119931 , 2021 2021 Citations: 129
A multi-criteria system design optimization for net zero energy buildings under uncertainties Y Sun, P Huang, G Huang Energy and Buildings 97, 196-204 , 2015 2015 Citations: 124
Mechanisms of motor symptom improvement by long-term Tai Chi training in Parkinson’s disease patients G Li, P Huang, SS Cui, YY Tan, YC He, X Shen, QY Jiang, P Huang, ... Translational neurodegeneration 11 (1), 6 , 2022 2022 Citations: 123
Prevalence and risk factors for depression and anxiety in Chinese patients with Parkinson disease SS Cui, JJ Du, R Fu, YQ Lin, P Huang, YC He, C Gao, HL Wang, SD Chen BMC geriatrics 17 (1), 270 , 2017 2017 Citations: 110
Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements P Huang, G Huang, Y Sun Applied Energy 213, 486-498 , 2018 2018 Citations: 105
Digital twin for accelerating sustainability in positive energy district: A review of simulation tools and applications X Zhang, J Shen, PK Saini, M Lovati, M Han, P Huang, Z Huang Frontiers in Sustainable Cities 3, 663269 , 2021 2021 Citations: 102
Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities P Huang, Z Ma, L Xiao, Y Sun Applied Energy 255, 113855 , 2019 2019 Citations: 95
A preliminary simulation study about the impact of COVID-19 crisis on energy demand of a building mix at a district in Sweden X Zhang, F Pellegrino, J Shen, B Copertaro, P Huang, PK Saini, M Lovati Applied Energy 280, 115954 , 2020 2020 Citations: 90
5th generation district heating and cooling (5GDHC) implementation potential in urban areas with existing district heating systems A Volkova, I Pakere, L Murauskaite, P Huang, K Lepiksaar, X Zhang Energy Reports 8, 10037-10047 , 2022 2022 Citations: 84
Regional high iron in the substantia nigra differentiates Parkinson’s disease patients from healthy controls K Ghassaban, N He, SK Sethi, P Huang, S Chen, F Yan, EM Haacke Frontiers in aging neuroscience 11, 106 , 2019 2019 Citations: 84
Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions J Chai, P Huang, Y Sun Energy 185, 176-189 , 2019 2019 Citations: 81
Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty S Zhang, Y Sun, Y Cheng, P Huang, MO Oladokun, Z Lin Applied Energy 228, 1020-1031 , 2018 2018 Citations: 78
