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Publications (10 of 490) Show all publications
Zhang, T., Kong, L., Zhu, Z., Wu, X., Li, H., Zhang, Z. & Yan, J. (2024). An electromagnetic vibration energy harvesting system based on series coupling input mechanism for freight railroads. Applied Energy, 353, Article ID 122047.
Open this publication in new window or tab >>An electromagnetic vibration energy harvesting system based on series coupling input mechanism for freight railroads
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2024 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 353, article id 122047Article in journal (Refereed) Published
Abstract [en]

Vibration energy harvesting technology is characterized by wide distribution, is pollution-free and independent of weather and climate, and is suitable for powering low-power sensors to ensure efficient and safe operation in freight railroads. This paper proposed an electromagnetic vibration energy harvester based on a series coupling input mechanism for the self-powered sensors in freight railroads. The design utilizes only one rack for vibration energy input to minimize the moment acting on the vibration source during the working process. Two pinions meshed with the rack convert the up and down vibrations into a two-way rotation. The one-way bearings and another pair of gears convert the opposite rotations of two parallel shafts into one-way rotation of the generator shaft, generating electricity. Supercapacitors and rectifier voltage regulator modules are utilized to store electrical energy efficiently. A peak power of 10.219 W and maximum mechanical efficiency of 64.31% is obtained in the experiment equipped with a flywheel under the 8 mm-4 Hz sinusoidal vibration excitation. The experimental results showed that the flywheel can enable the proposed harvester to achieve better power generation performance when the amplitude and frequency are relatively high. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Freight railroads, Rack-and-pinion mechanism, Self-powered sensors, Series design, Vibration energy harvesting
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64513 (URN)10.1016/j.apenergy.2023.122047 (DOI)001087821500001 ()2-s2.0-85173045853 (Scopus ID)
Available from: 2023-10-11 Created: 2023-10-11 Last updated: 2023-11-15Bibliographically approved
Lu, L., Huang, X., Zhou, X., Guo, J., Yang, X. & Yan, J. (2024). High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning. Building Simulation
Open this publication in new window or tab >>High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning
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2024 (English)In: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744Article in journal (Refereed) Epub ahead of print
Abstract [en]

Indoor air pollution resulting from volatile organic compounds (VOCs), especially formaldehyde, is a significant health concern needed to predict indoor formaldehyde concentration (Cf) in green intelligent building design. This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton, silk, and polyester fabric with heat flux in Harbin, Beijing, Xi'an, Shanghai, Guangzhou, and Kunming, China. The time-by-time indoor dry-bulb temperature (T), relative humidity (RH), and Cf, obtained from verified simulations, were collated and used as input data for the long short-term memory (LSTM) of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics (adsorption and release of formaldehyde). The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations. The LSTM-based model also predicted Cf with mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) that fell within 10%, 10%, 0.5, 0.5, and 0.8, respectively. In addition, the characteristics of the input dataset, model parameters, the prediction accuracy of different indoor fabrics, and the uncertainty of the data set are analyzed. The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input, and the prediction accuracy of LSTM is better than recurrent neural network (RNN). The method's feasibility was established, and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.

Place, publisher, year, edition, pages
TSINGHUA UNIV PRESS, 2024
Keywords
multivariate time series, formaldehyde concentration, deep learning, heat-humidity coupling, mass transfer, secondary source effect
National Category
Other Natural Sciences
Identifiers
urn:nbn:se:mdh:diva-65675 (URN)10.1007/s12273-023-1091-4 (DOI)001131873400002 ()2-s2.0-85180645768 (Scopus ID)
Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-01-24Bibliographically approved
Zhang, K., Chen, M., Zhu, R., Zhang, F., Zhong, T., Lin, J., . . . Yan, J. (2024). Integrating photovoltaic noise barriers and electric vehicle charging stations for sustainable city transportation. Sustainable cities and society, 100, Article ID 104996.
Open this publication in new window or tab >>Integrating photovoltaic noise barriers and electric vehicle charging stations for sustainable city transportation
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2024 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 100, article id 104996Article in journal (Refereed) Published
Abstract [en]

Photovoltaic noise barriers (PVNBs) offer a dual advantage of reducing traffic noise pollution and providing renewable electricity to cities. However, how the effective integration of PVNB-generated power into urban energy networks remains a critical area lacking research. To bridge this gap, this study proposes PVNBs-energy storage (ES)-charging station (CS; PVNBs-ES-CS) strategy. It can facilitate the actual consumption of PVNBs power and the mitigation the burden on the grid posed by electric vehicles (EVs) charging demands. The case study conducted in Guangzhou, China, reveals that PVNBs can support up to 5% of the total power demand for EVCSs. Under the PVNBs power maximization consumption scenario, PVNBs can meet up to 30% of the power demands from 60 EVCSs, with 58% of PVNBs generated power being consumed. In the PVNBs-ES-CS future utilization scenario, up to 30% of the power demand of 125 EVCSs can be met, and 36% of the power of PVNBs can be consumed. The combination of PVNBs and EVCSs offers a practical solution for incorporating renewable energy sources into urban energy networks. This application mode can be applied in various cities with EV demands and PVNB power generation data.

Keywords
Solar energy, Photovoltaic application, Energy network, Sustainable cities
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-64948 (URN)10.1016/j.scs.2023.104996 (DOI)001102562800001 ()2-s2.0-85183912621 (Scopus ID)
Available from: 2023-12-07 Created: 2023-12-07 Last updated: 2024-02-14Bibliographically approved
Feng, D., Gao, X., Yang, Y., Feng, S., Yang, X. & Yan, J. (2024). Pathways for carbon emission prediction and mitigation of sustainable industrial parks: a LEAP model application. International Journal of Green Energy
Open this publication in new window or tab >>Pathways for carbon emission prediction and mitigation of sustainable industrial parks: a LEAP model application
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2024 (English)In: International Journal of Green Energy, ISSN 1543-5075, E-ISSN 1543-5083Article in journal (Refereed) Epub ahead of print
Abstract [en]

Industrial parks play a crucial role as a carrier of industrial clusters and energy consumption. Accurately predicting the energy demand and carbon emissions trend is key to scientifically determining the pathways for low-carbon industrial parks. However, exploration in carbon emission prediction on industrial park scale is still in its infancy stage. This paper investigates fuel demand and carbon emissions from 2021 to 2035 in an industrial park in Jiangsu Province, utilizing the Long-range Energy Alternative Planning (LEAP) model to explore the pathways for low carbon development. Energy-saving and emission-reduction effects of different macro-economic policies and micro-energy planning are analyzed based on the energy balance and emission factor methods. Four scenarios are compared: the baseline scenario (BAS), green development scenario (GDS), low carbon scenario (LCS), and strength low carbon scenario (SLS). Results indicated that energy demand under BAS reached at 31.37 Mtce in 2035, and energy-saving rates of GDS, LCS, and SLS in 2035 were 12.94%, 14.00% and 19.08%, respectively. Carbon emissions reached 53.96 MtCO2e in BAS of 2035. However, in the same year, emissions decreased by 24.88%, 43.09%, and 52.52% in GDS, LCS, and SLS, respectively, with SLS being the most suitable for the park.

Place, publisher, year, edition, pages
Taylor & Francis, 2024
Keywords
Carbon emission prediction, industrial park, scenario analysis, LEAP, mitigation strategy
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65791 (URN)10.1080/15435075.2024.2307915 (DOI)001147204700001 ()2-s2.0-85183038121 (Scopus ID)
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-01-31Bibliographically approved
Luo, H., Gao, X., Liu, Z., Liu, W., Li, Y., Meng, X., . . . Sun, L. (2024). Real-time Characterization Model of Carbon Emissions Based on Land-use Status: A Case Study of Xi'an City, China. Journal of Cleaner Production, 434, Article ID 140069.
Open this publication in new window or tab >>Real-time Characterization Model of Carbon Emissions Based on Land-use Status: A Case Study of Xi'an City, China
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2024 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 434, article id 140069Article in journal (Refereed) Published
Abstract [en]

The traditional carbon accounting method, with a lag of over 2 years due to the release time of statistical yearbooks, impedes timely policy adjustments in urban planning and management. Hence, there is an urgent need to establish a real-time carbon emissions characterization model. Xi'an which has a complex land-use structure was chosen as the study site and its carbon emissions were calculated using the Emission Factor Method. The GIS-Kernel Density (KD) model was constructed, and land use was subdivided based on Point of Interest (POI) and road network data. Based on the results of carbon emissions accounting and land-use subdivision, a Multilayer perceptron (MLP) model was established. The remote sensing (RS) images of Xi'an underwent supervised classification, and the carbon emissions of Xi'an were characterized based on the subdivision results and MLP model. The results show that: (1) The accuracy of the characterization model is more than 90%, and with the improvement of RS technology, the accuracy will be further improved; (2) Compared with the existing model, this model can real time reflect the spatial distribution of carbon emissions; (3) Atmospheric emission of Xi'an will be 41.92 million tons at the end of 2022, a decrease of 2.80 million tons compared with that of 2020, but an increase of 0.33 million tons from 2021. The north of Xi'an and periphery of the central urban area are the main carbon sink loss areas, while the east of Xi'an and north foot of the Qinling Mountains are carbon sink growth areas.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Carbon emission, Classification of supervision, Land use, Real-time characterization, Subdivision treatment, Carbon, Motor transportation, Remote sensing, Urban growth, Carbon accounting, Carbon emissions, Carbon sink, Case-studies, Multilayers perceptrons, Real- time, Real-time characterizations, Xi'an cities
National Category
Physical Geography
Identifiers
urn:nbn:se:mdh:diva-65179 (URN)10.1016/j.jclepro.2023.140069 (DOI)001137869600001 ()2-s2.0-85179626893 (Scopus ID)
Available from: 2023-12-21 Created: 2023-12-21 Last updated: 2024-01-31Bibliographically approved
Shabani, M., Wallin, F., Dahlquist, E. & Yan, J. (2024). Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application. Energy Conversion and Management: X, 21, Article ID 100519.
Open this publication in new window or tab >>Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application
2024 (English)In: Energy Conversion and Management: X, ISSN 2590-1745, Vol. 21, article id 100519Article in journal (Refereed) Published
Abstract [en]

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization. This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined. The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy's flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Day-ahead optimization-based battery operation scheduling, Degradation cost minimization, Price arbitrage within real-time electricity price tariff, Residential-grid connected battery application, Revenue maximization, Sustained profitability optimization, Battery management systems, Charging (batteries), Costs, Decision making, Housing, Investments, Lithium-ion batteries, Power markets, Battery applications, Battery operation, Cost minimization, Day-ahead, Electricity prices, Grid-connected, Operations scheduling, Optimisations, Real- time, Profitability
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65372 (URN)10.1016/j.ecmx.2023.100519 (DOI)001155504000001 ()2-s2.0-85181971282 (Scopus ID)
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-02-14Bibliographically approved
Zhang, Q., Yan, J., Gao, H. O. & You, F. (2023). A Systematic Review on power systems planning and operations management with grid integration of transportation electrification at scale. Advances in Applied Energy, 11, Article ID 100147.
Open this publication in new window or tab >>A Systematic Review on power systems planning and operations management with grid integration of transportation electrification at scale
2023 (English)In: Advances in Applied Energy, ISSN 2666-7924, Vol. 11, article id 100147Article in journal (Refereed) Published
Abstract [en]

Transportation electrification plays a crucial role in mitigating greenhouse gas (GHG) emissions and enabling the decarbonization of power systems. However, current research on electric vehicles (EVs) only provides a fragmented examination of their impact on power system planning and operation, lacking a comprehensive overview across both transmission and distribution levels. This limits the effectiveness and efficiency of power system solutions for greater EV adoption. Conducting a systematic review of the effects of EVs on power transmission and distribution systems (e.g., grid integration, planning, operation, etc.), this paper aims to bridge the fragmented literature on the topic together by focusing on the interplay between transportation electrification and power systems. The study sheds light on the interplay between transportation electrification and power systems, delving into the importance of classifying EVs and charging infrastructure based on powertrain design, duty cycle, and typical features, as well as methods of capturing charging patterns and determining spatial-temporal charging profiles. Furthermore, we provide an in-depth discussion on the benefits of smart charging and the provision of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services for maintaining power system reliability. With the holistic systems approach, this paper can identify the main objectives and potential barriers of power transmission and distribution systems in accommodating transportation electrification at scale. Concurrently, it paves the way for a comprehensive understanding of technological innovation, transportation-power system decarbonization, policy pathways, environmental advantages, scenario designs, and avenues for future research.

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Electric vehicles, Grid to vehicle, Planning and operation, Power transmission and distribution, Vehicle to grid
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-63665 (URN)10.1016/j.adapen.2023.100147 (DOI)001053487400001 ()2-s2.0-85162145463 (Scopus ID)
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-12-04Bibliographically approved
Du, J., Zheng, J., Liang, Y., Liao, Q., Wang, B., Sun, X., . . . Yan, J. (2023). A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants. Engineering applications of artificial intelligence, 118, Article ID 105647.
Open this publication in new window or tab >>A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants
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2023 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 118, article id 105647Article in journal (Refereed) Published
Abstract [en]

Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Local dependency, Multi-region, Photovoltaic power generation prediction, TG-A-CNN-LSTM, Theory guided, Time series, Domain Knowledge, Electric power generation, Forecasting, Learning systems, Long short-term memory, Solar energy conversion, Solar power generation, Generation predictions, Learning methods, Photovoltaic power generation, Prediction modelling, Times series, Solar energy
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-61153 (URN)10.1016/j.engappai.2022.105647 (DOI)000894964700008 ()2-s2.0-85142808671 (Scopus ID)
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-01-25Bibliographically approved
Zhang, T., Cao, H., Zhang, Z., Kong, W., Kong, L., Liu, J. & Yan, J. (2023). A variable damping vibration energy harvester based on Half-Wave flywheeling effect for freight railways. Mechanical systems and signal processing, 200, Article ID 110611.
Open this publication in new window or tab >>A variable damping vibration energy harvester based on Half-Wave flywheeling effect for freight railways
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2023 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 200, article id 110611Article in journal (Refereed) Published
Abstract [en]

The vibrational energy, often considered a negative factor, is abundant in everyday life. Especially in railway systems, the negatively impacted track vibrations resulting from moving trains can be captured to provide a practical power supply solution for wireless sensor networks. This paper proposed a variable damping vibration energy harvester with a half-wave flywheel for a freight train-based railway. A double-sided rack as the input member converts the track vibration into the opposite rotation of the two pinions, which are then transmitted to the two parallel shafts respectively. According to the work characteristics of the one-way bearing, the upper and lower vibrations can be collected separately and output a one-way rotation to the generator module. The proposed harvester with a half-wave flywheel features a larger damping force for vibration reduction during the downward track vibration and a smaller damping force conducive to returning the track's original state during the upward track vibration. The experimental results achieve a maximum output power of 10.247 W and a maximum mechanical efficiency of 74.49%. Both simulations and experiments have verified that the proposed system with a half-wave flywheel can increase the damping force in the vibration reduction process and reduce the damping force in the reset process, which is characteristic of improving its power generation performance with a good vibration reduction effect. The VEH with the half-wave flywheel achieved an average power of 5.321 W at the train speed of 90 km/h under random vibration testing, which verifies the feasibility of self-powered wireless sensor networks in railway environments. 

Place, publisher, year, edition, pages
Academic Press, 2023
Keywords
Freight railway, Half-wave flywheel, Rack-pinion mechanism, Self-power applications, Vibration energy harvester, Energy harvesting, Flywheels, Railroad transportation, Railroads, Wheels, Wireless sensor networks, Damping forces, Freight railways, Half-wave, Power applications, Self-power application, Track vibration, Variable damping, Vibration energy harvesters, Damping
National Category
Applied Mechanics
Identifiers
urn:nbn:se:mdh:diva-63962 (URN)10.1016/j.ymssp.2023.110611 (DOI)01053943600001 ()2-s2.0-85166739267 (Scopus ID)
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-09-06Bibliographically approved
Gao, S., Li, H., Hou, Y. & Yan, J. (2023). Benefits of integrating power-to-heat assets in CHPs. Applied Energy, 335, Article ID 120763.
Open this publication in new window or tab >>Benefits of integrating power-to-heat assets in CHPs
2023 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 335, article id 120763Article in journal (Refereed) Published
Abstract [en]

Integrating power-to-heat (P2H) assets in combined heat and power plants (CHPs) is an attractive option, which can improve the flexibility in CHPs. This paper compares the potential benefits of integrating an electrical boiler (EB) and a heat pump (HP) in a CHP from providing flexibility services in both the day-ahead market and the frequency regulation market. An optimization model is developed for the operation of P2H assets and the CHP to maximize the profit. A case study is carried out using the data of a real CHP and electricity prices of Nord Pool. It is found that when an EB or a HP is integrated, the annual profit of the studied CHP from providing frequency regulation can be increased by 3.1 % (EB) or 27.7 % (HP) respectively compared to the CHP without P2H. Despite the high capital cost, a HP can increase the net present value up to 21.8 %, and achieve a payback period of 3 year, which are better than an EB (0.8 % and 5 year). Sensitivity analysis shows that prices of fuel and electricity have significant impacts on the net present value and payback period for the integration of P2H assets. Even though the increase of the fuel price decreases the NPV, it can lead to a decline in the payback period. Meanwhile, the increase of the electricity price results in a large growth in the profit and NPV, but a big reduction in payback period. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Combine heat and power, DA, Electricity market, Flexibility, mFRR, Power-to-heat integration
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-61919 (URN)10.1016/j.apenergy.2023.120763 (DOI)000996087600001 ()2-s2.0-85147592593 (Scopus ID)
Available from: 2023-02-15 Created: 2023-02-15 Last updated: 2023-06-14Bibliographically approved
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