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Tarek Alskaif is an Assistant Professor with Tenure-track at the Information Technology group (INF), Wageningen University & Research (WUR). He received the Ph.D. degree (Cum-laude) from Universitat Politècnica de Catalunya (UPC), Statistical Analysis of Networks and Systems (SANS) research group, Barcelona, Spain in 2016. Tarek is passionate about sustainability and energy transition and believes in the key role of ICT to enable that. Between 2016 - 2020, he was a postdoc at the Energy & Resources group at the Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands, focusing on different research problems in smart grids and smart cities, including smart energy systems design, local energy communities, solar energy forecasting, smart integration of distributed energy resources, wireless sensor networks, among others, all with an emphasis on using ICT platforms, modeling and data analytics. He is the (co)author of more than 40 peer-reviewed scientific articles and a regular reviewer for several international journals and conferences in the area of smart grids and cyber-physical systems. Tarek serves as a technical program committee member in the IEEE International Conference on Smart Energy Systems and Technologies (SEST) since 2019 and a steering committee member and publication chair in the same since 2020.
Moving to a user-centric approach is seen as a key change of paradigm in order to increase the efficiency and sustainability of energy systems. Massive integration of new economic agents such as prosumers and mobile or stationary storage will play a key role in the energy transition. In this paper, a design of a community-based local energy market (CB-LEM) is proposed where the members are allowed to trade energy among each other through a local pool. The price is set on a day-ahead basis under the coordination of a Community Manager (CM). The novel aspect of this work is that every agent takes part in the determination of the local market price while deciding its own scheduling problem under uncertainty concerning renewable-energy generation and storage. After day-ahead clearing, real time operation and ex-post settlement of the local market by the CM are also explained in order to complete the proposed design. A real case study in a neighbourhood in Amsterdam, The Netherlands, is used for testing the proposed framework. In addition, the performance of the ADMM-based clearing process is analyzed in terms of scalability and convergence.
Jose Luis Crespo-Vazquez; Tarek Al Skaif; Angel Manuel Gonzalez-Rueda; Madeleine Gibescu. A Community-Based Energy Market Design Using Decentralized Decision-Making Under Uncertainty. IEEE Transactions on Smart Grid 2020, 12, 1782 -1793.
AMA StyleJose Luis Crespo-Vazquez, Tarek Al Skaif, Angel Manuel Gonzalez-Rueda, Madeleine Gibescu. A Community-Based Energy Market Design Using Decentralized Decision-Making Under Uncertainty. IEEE Transactions on Smart Grid. 2020; 12 (2):1782-1793.
Chicago/Turabian StyleJose Luis Crespo-Vazquez; Tarek Al Skaif; Angel Manuel Gonzalez-Rueda; Madeleine Gibescu. 2020. "A Community-Based Energy Market Design Using Decentralized Decision-Making Under Uncertainty." IEEE Transactions on Smart Grid 12, no. 2: 1782-1793.
This paper explores a future perspective to foster the provision of balancing services to the electricity grid by distributed assets. One recent test case, initiated by the Dutch Transmission System Operator (TSO), was to operate an Electric Vehicle (EV) fleet on the automatic Frequency Restoration Reserve (aFRR) market, which entails fast and automated reserves. To achieve that in a decentralised, automated and transparent manner, the role of blockchain technology for this specific application is explored. We propose a novel configuration that can serve as a basis for deploying distributed assets for aFRR markets using blockchain or any alternative Distributed Ledger Technology (DLT). Automation can be achieved via the deployment of smart contracts, which also results in transparency in the system. The blockchain configurations are designed for three phases in the aFRR market, namely: (i) Operational planning and scheduling by a balancing service provider (i.e., formulation and submission of aFRR bid), (ii) Real-time operations (i.e., activation and measurements), and iii) Verification and settlement (i.e., imbalance correction and financial settlement). The paper concludes that the scalability of distributed assets that can participate in the system, combined with the large transaction times and energy consumption of some consensus mechanisms, could put limitations on the proposed architecture. Future research should address benchmarking studies of other alternatives (e.g., DLTs, such as the ones based on directed acyclic graphs, and non-DLT solutions) with the proposed blockchain solution.
Tarek AlSkaif; Bart Holthuizen; Wouter Schram; Ioannis Lampropoulos; Wilfried Van Sark. A Blockchain-Based Configuration for Balancing the Electricity Grid with Distributed Assets. World Electric Vehicle Journal 2020, 11, 62 .
AMA StyleTarek AlSkaif, Bart Holthuizen, Wouter Schram, Ioannis Lampropoulos, Wilfried Van Sark. A Blockchain-Based Configuration for Balancing the Electricity Grid with Distributed Assets. World Electric Vehicle Journal. 2020; 11 (4):62.
Chicago/Turabian StyleTarek AlSkaif; Bart Holthuizen; Wouter Schram; Ioannis Lampropoulos; Wilfried Van Sark. 2020. "A Blockchain-Based Configuration for Balancing the Electricity Grid with Distributed Assets." World Electric Vehicle Journal 11, no. 4: 62.
Electricity spot market prices are increasingly affected by an expanding amount of renewables and a growing number of market participants. In an attempt to improve forecasting accuracy, this paper evaluates the importance of 62 predictor variables to forecast the the day-ahead electricity price. These variables describe the electricity price, load, generation and weather at different times in the Netherlands, Belgium and Germany. In this study we assess the performance of four machine learning models that forecast the electricity price. Next, we rank the variables according to their importance and identify to what extent different estimators and feature selection methods affect the performance of the forecasting models. We found that Random Forest regression is the best performing model regardless of the number of features selected and the feature selection method applied. Secondly, the performance of all models was not found to improve significantly after the selection of the top 15 ranked variables. Interestingly the top ranked variables differ significantly per selection method. Moreover, the feature selection methods based on Multi-variate Linear Regression and linear kernel Support Vector Machine were found to give the best performance for all models.
Lennard Visser; Tarek AlSkaif; Wilfried Van Sark. The Importance of Predictor Variables and Feature Selection in Day-ahead Electricity Price Forecasting. 2020 International Conference on Smart Energy Systems and Technologies (SEST) 2020, 1 -6.
AMA StyleLennard Visser, Tarek AlSkaif, Wilfried Van Sark. The Importance of Predictor Variables and Feature Selection in Day-ahead Electricity Price Forecasting. 2020 International Conference on Smart Energy Systems and Technologies (SEST). 2020; ():1-6.
Chicago/Turabian StyleLennard Visser; Tarek AlSkaif; Wilfried Van Sark. 2020. "The Importance of Predictor Variables and Feature Selection in Day-ahead Electricity Price Forecasting." 2020 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.
Mayank Jain; Tarek AlSkaif; Soumyabrata Dev. A Clustering Framework for Residential Electric Demand Profiles. 2020 International Conference on Smart Energy Systems and Technologies (SEST) 2020, 1 -6.
AMA StyleMayank Jain, Tarek AlSkaif, Soumyabrata Dev. A Clustering Framework for Residential Electric Demand Profiles. 2020 International Conference on Smart Energy Systems and Technologies (SEST). 2020; ():1-6.
Chicago/Turabian StyleMayank Jain; Tarek AlSkaif; Soumyabrata Dev. 2020. "A Clustering Framework for Residential Electric Demand Profiles." 2020 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
The transition towards a sharing economy and the increasing electrification of the transport sector are occurring simultaneously. Consequently, we can expect more car sharing schemes using electric vehicles (EVs) to emerge in coming years. Numerous studies looked into the grid impact of EV charging and its potential to provide ancillary services, but these studies only considered regular EVs. This study compares the charging patterns of regular and shared EVs and creates insight in the grid impact and potential to provide ancillary services with future adoption of shared EVs. Four scenarios for the adoption of shared EVs are proposed, and a method to generate a set of future charging transactions based on historical charging data is presented. The analysis is performed using charging data from an EV sharing company in the Netherlands. Results indicate that charging demand peaks and grid congestion levels decrease substantially with higher adoption of shared EVs. Adoption of shared EVs increases the potential of EVs to provide ancillary services, due to a higher charging flexibility of shared EVs.
Nico Brinkel; Tarek AlSkaif; Wilfried Van Sark. The Impact of Transitioning to Shared Electric Vehicles on Grid Congestion and Management. 2020 International Conference on Smart Energy Systems and Technologies (SEST) 2020, 1 -6.
AMA StyleNico Brinkel, Tarek AlSkaif, Wilfried Van Sark. The Impact of Transitioning to Shared Electric Vehicles on Grid Congestion and Management. 2020 International Conference on Smart Energy Systems and Technologies (SEST). 2020; ():1-6.
Chicago/Turabian StyleNico Brinkel; Tarek AlSkaif; Wilfried Van Sark. 2020. "The Impact of Transitioning to Shared Electric Vehicles on Grid Congestion and Management." 2020 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
In recent years, the adoption of electric vehicle has increased steadily, posing substantial problems in the low voltage grid. Among these problems are grid congestion and voltage instability. This study proposes a dynamic smart charging strategy based on a constant impedance load flow model, in which the optimal electric vehicle charging power is based on the available grid capacity. The dynamic charging profile is compared to a static charging profile and business as usual charging in a case study on a representative low voltage grid. Simulations are made with projections for electric vehicle charging, residential electricity demand and photovoltaic generation for 2018, 2030 and 2050. The results of the case study show that no grid congestion occurs in the 2018 scenario, and little in the 2030 scenario, meaning that the current grid infrastructure is well- suited for the electric vehicle charging demand and that the business as usual and static smart charging profiles result in an underutilization of the grid capacity. Grid congestion occurs substantially more frequently in the 2050 scenario, but the dynamic smart charging profile results in a greater utilization of the grid capacity compared to the alternatives.
Martijn Verhoog; Nico Brinkel; Tarek Alskaif. Congestion Management in LV Grids Using Static and Dynamic EV Smart Charging. 2020 International Conference on Smart Energy Systems and Technologies (SEST) 2020, 1 -6.
AMA StyleMartijn Verhoog, Nico Brinkel, Tarek Alskaif. Congestion Management in LV Grids Using Static and Dynamic EV Smart Charging. 2020 International Conference on Smart Energy Systems and Technologies (SEST). 2020; ():1-6.
Chicago/Turabian StyleMartijn Verhoog; Nico Brinkel; Tarek Alskaif. 2020. "Congestion Management in LV Grids Using Static and Dynamic EV Smart Charging." 2020 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
Long term Global Horizontal Irradiance (GHI) data sets are essential to assess the local solar resource and estimate the potential power production of photovoltaic systems. Statistical models are found to be very effective in estimating the GHI. In this study we examine to what extent the performance of such models is affected by the distance, direction and temporal difference between the training and testing period. To quantify these factors three machine learning models are considered: Random Forest, Extreme Gradient Boosting, and Artificial Neural Network. These models estimate the GHI at 15 weather stations in the Netherlands by considering 11 meteorological variables. The paper demonstrates that GHI estimation is more accurate when the model is trained on a station that is located closer to the target station, where an increased error of 3% and 7% is found up to a distance of respectively 40 and 120 km. In addition, in the case study it is found that the accuracy of GHI estimation improves when the test station is located in a northeast, east, southeast or south direction from the training station. This partly correlates with the prevailing wind direction. Finally, the testing period selected is found to significantly affect the obtained model performance, whereas the influence of the training period is found to be minimal.
Lennard Visser; Stef Knibbeler; Tarek AlSkaif; Wilfried Van Sark. The Impact of Distance, Cardinal-direction and Time on Solar Irradiance Estimation: A Case-study. 2020 International Conference on Smart Energy Systems and Technologies (SEST) 2020, 1 -6.
AMA StyleLennard Visser, Stef Knibbeler, Tarek AlSkaif, Wilfried Van Sark. The Impact of Distance, Cardinal-direction and Time on Solar Irradiance Estimation: A Case-study. 2020 International Conference on Smart Energy Systems and Technologies (SEST). 2020; ():1-6.
Chicago/Turabian StyleLennard Visser; Stef Knibbeler; Tarek AlSkaif; Wilfried Van Sark. 2020. "The Impact of Distance, Cardinal-direction and Time on Solar Irradiance Estimation: A Case-study." 2020 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.
N.B.G. Brinkel; W.L. Schram; T.A. AlSkaif; I. Lampropoulos; W.G.J.H.M. van Sark. Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits. Applied Energy 2020, 276, 115285 .
AMA StyleN.B.G. Brinkel, W.L. Schram, T.A. AlSkaif, I. Lampropoulos, W.G.J.H.M. van Sark. Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits. Applied Energy. 2020; 276 ():115285.
Chicago/Turabian StyleN.B.G. Brinkel; W.L. Schram; T.A. AlSkaif; I. Lampropoulos; W.G.J.H.M. van Sark. 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits." Applied Energy 276, no. : 115285.
Urban environments can be key to sustainable energy in terms of driving innovation and action. Urban areas are responsible for a significant part of energy use and associated greenhouse gas emissions. The share of greenhouse gas emissions is likely to increase as global urban populations increase. As over half of the human population will live in cities in the near future, the management of energy supply and demand in urban environments will become essential. Developments such as the transformation of the electricity grid from a centralised to a decentralised system as well as the electrification of the transportation and heating systems in buildings will transform the urban energy landscape. Efficient heating systems, sustainable energy technologies, and electric vehicles will be critical to decarbonise cities. An overview of emerging technologies and concepts in the built environment is provided in this literature review on the basis of four main areas, namely, energy demand, supply, storage, and integration aspects. The Netherlands is used as a case study for demonstrating evidence-based results and feasibility of innovative urban energy solutions, as well as supportive policies.
Ioannis Lampropoulos; Tarek AlSkaif; Wouter Schram; Eelke Bontekoe; Simone Coccato; Wilfried Van Sark. Review of Energy in the Built Environment. Smart Cities 2020, 3, 248 -288.
AMA StyleIoannis Lampropoulos, Tarek AlSkaif, Wouter Schram, Eelke Bontekoe, Simone Coccato, Wilfried Van Sark. Review of Energy in the Built Environment. Smart Cities. 2020; 3 (2):248-288.
Chicago/Turabian StyleIoannis Lampropoulos; Tarek AlSkaif; Wouter Schram; Eelke Bontekoe; Simone Coccato; Wilfried Van Sark. 2020. "Review of Energy in the Built Environment." Smart Cities 3, no. 2: 248-288.
In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.
Gijs van Leeuwen; Tarek AlSkaif; Madeleine Gibescu; Wilfried van Sark. An integrated blockchain-based energy management platform with bilateral trading for microgrid communities. Applied Energy 2020, 263, 114613 .
AMA StyleGijs van Leeuwen, Tarek AlSkaif, Madeleine Gibescu, Wilfried van Sark. An integrated blockchain-based energy management platform with bilateral trading for microgrid communities. Applied Energy. 2020; 263 ():114613.
Chicago/Turabian StyleGijs van Leeuwen; Tarek AlSkaif; Madeleine Gibescu; Wilfried van Sark. 2020. "An integrated blockchain-based energy management platform with bilateral trading for microgrid communities." Applied Energy 263, no. : 114613.
While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies.
Tarek AlSkaif; Soumyabrata Dev; Lennard Visser; Murhaf Hossari; Wilfried van Sark. A systematic analysis of meteorological variables for PV output power estimation. Renewable Energy 2020, 153, 12 -22.
AMA StyleTarek AlSkaif, Soumyabrata Dev, Lennard Visser, Murhaf Hossari, Wilfried van Sark. A systematic analysis of meteorological variables for PV output power estimation. Renewable Energy. 2020; 153 ():12-22.
Chicago/Turabian StyleTarek AlSkaif; Soumyabrata Dev; Lennard Visser; Murhaf Hossari; Wilfried van Sark. 2020. "A systematic analysis of meteorological variables for PV output power estimation." Renewable Energy 153, no. : 12-22.
The need to limit climate change has led to policies that aim for the reduction of greenhouse gas emissions. Often, a trade-off exists between reducing emissions and associated costs. In this paper, a multi-objective optimization framework is proposed to determine this trade-off when operating a Community Energy Storage (CES) system in a neighbourhood with high shares of photovoltaic (PV) electricity generation capacity. The Pareto frontier of costs and emissions objectives is established when the CES system would operate on the day-ahead spot market. The emission profile is constructed based on the marginal emissions. Results show that costs and emissions can simultaneously be decreased for a range of solutions compared to reference scenarios with no battery or a battery only focused on increasing self-consumption, for very attractive CO2 abatement costs and without hampering self-consumption of PV-generated electricity. Results are robust for battery degradation, whereas battery efficiency is found to be an important determining factor for simultaneously decreasing costs and emissions. The operational schedules are tested against violating transformer, line and voltage limits through a load flow analysis. The proposed framework can be extended to employ a wide range of objectives and / or location-specific circumstances.
Wouter L. Schram; Tarek AlSkaif; Ioannis Lampropoulos; Sawsan Henein; Wilfried G.J.H.M. Van Sark. On the Trade-Off Between Environmental and Economic Objectives in Community Energy Storage Operational Optimization. IEEE Transactions on Sustainable Energy 2020, 11, 2653 -2661.
AMA StyleWouter L. Schram, Tarek AlSkaif, Ioannis Lampropoulos, Sawsan Henein, Wilfried G.J.H.M. Van Sark. On the Trade-Off Between Environmental and Economic Objectives in Community Energy Storage Operational Optimization. IEEE Transactions on Sustainable Energy. 2020; 11 (4):2653-2661.
Chicago/Turabian StyleWouter L. Schram; Tarek AlSkaif; Ioannis Lampropoulos; Sawsan Henein; Wilfried G.J.H.M. Van Sark. 2020. "On the Trade-Off Between Environmental and Economic Objectives in Community Energy Storage Operational Optimization." IEEE Transactions on Sustainable Energy 11, no. 4: 2653-2661.
Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners.
N.B.G. Brinkel; M.K. Gerritsma; Tarek AlSkaif; I. Lampropoulos; A.M. van Voorden; H.A. Fidder; Wilfried van Sark. Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles. International Journal of Electrical Power & Energy Systems 2019, 118, 105741 .
AMA StyleN.B.G. Brinkel, M.K. Gerritsma, Tarek AlSkaif, I. Lampropoulos, A.M. van Voorden, H.A. Fidder, Wilfried van Sark. Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles. International Journal of Electrical Power & Energy Systems. 2019; 118 ():105741.
Chicago/Turabian StyleN.B.G. Brinkel; M.K. Gerritsma; Tarek AlSkaif; I. Lampropoulos; A.M. van Voorden; H.A. Fidder; Wilfried van Sark. 2019. "Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles." International Journal of Electrical Power & Energy Systems 118, no. : 105741.
Tom Terlouw; Tarek AlSkaif; Christian Bauer; Wilfried van Sark. Optimal energy management in all-electric residential energy systems with heat and electricity storage. Applied Energy 2019, 254, 1 .
AMA StyleTom Terlouw, Tarek AlSkaif, Christian Bauer, Wilfried van Sark. Optimal energy management in all-electric residential energy systems with heat and electricity storage. Applied Energy. 2019; 254 ():1.
Chicago/Turabian StyleTom Terlouw; Tarek AlSkaif; Christian Bauer; Wilfried van Sark. 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage." Applied Energy 254, no. : 1.
Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis - and are designed on - specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.
Fabrizio Orlandi; Alan Meehan; Murhaf Hossari; Soumyabrata Dev; Declan O'Sullivan; Tarek AlSkaif. Interlinking Heterogeneous Data for Smart Energy Systems. 2019 International Conference on Smart Energy Systems and Technologies (SEST) 2019, 1 -6.
AMA StyleFabrizio Orlandi, Alan Meehan, Murhaf Hossari, Soumyabrata Dev, Declan O'Sullivan, Tarek AlSkaif. Interlinking Heterogeneous Data for Smart Energy Systems. 2019 International Conference on Smart Energy Systems and Technologies (SEST). 2019; ():1-6.
Chicago/Turabian StyleFabrizio Orlandi; Alan Meehan; Murhaf Hossari; Soumyabrata Dev; Declan O'Sullivan; Tarek AlSkaif. 2019. "Interlinking Heterogeneous Data for Smart Energy Systems." 2019 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
Smart energy systems in general, and solar energy analysis in particular, have recently gained increasing interest. This is mainly due to stronger focus on smart energy saving solutions and recent developments in photovoltaic (PV) cells. Various data-driven and machine-learning frameworks are being proposed by the research community. However, these frameworks perform their analysis - and are designed on - specific, heterogeneous and isolated datasets, distributed across different sites and sources, making it hard to compare results and reproduce the analysis on similar data. We propose an approach based on Web (W3C) standards and Linked Data technologies for representing and converting PV and weather records into an Resource Description Framework (RDF) graph-based data format. This format, and the presented approach, is ideal in a data integration scenario where data needs to be converted into homogeneous form and different datasets could be interlinked for distributed analysis.
Fabrizio Orlandi; Alan Meehan; Murhaf Hossari; Soumyabrata Dev; Declan O'sullivan; Tarek AlSkaif. Interlinking Heterogeneous Data for Smart Energy Systems. 2019, 1 .
AMA StyleFabrizio Orlandi, Alan Meehan, Murhaf Hossari, Soumyabrata Dev, Declan O'sullivan, Tarek AlSkaif. Interlinking Heterogeneous Data for Smart Energy Systems. . 2019; ():1.
Chicago/Turabian StyleFabrizio Orlandi; Alan Meehan; Murhaf Hossari; Soumyabrata Dev; Declan O'sullivan; Tarek AlSkaif. 2019. "Interlinking Heterogeneous Data for Smart Energy Systems." , no. : 1.
All-electric energy systems are a promising alternative for natural gas based energy systems. Therefore, this paper aims to present a simple method to determine all-electric demand profiles. Next, these demand profiles are used in an optimization problem by including electricity storage in the form of lithium-ion batteries and Battery Electric Vehicles (BEVs). Furthermore, different methodologies are compared to size the lithium-ion battery systems. The results demonstrate that optimal sizing could reduce the installed storage capacity per household hence could reduce investment costs, while the Photovoltaic Self-Consumption (PVSC) ratio remains high. Hence it is recommended to use optimally sized battery systems to improve the economic feasibility of all-electric system lay-outs. Furthermore, the shift to all-electric energy systems results in large grid absorption peaks when a high penetration of BEVs are included.
Tom Terlouw; Tarek AlSkaif; Wilfried Van Sark. Optimal Energy Management of All-electric Residential Energy Systems in the Netherlands. 2019 IEEE Milan PowerTech 2019, 1 -6.
AMA StyleTom Terlouw, Tarek AlSkaif, Wilfried Van Sark. Optimal Energy Management of All-electric Residential Energy Systems in the Netherlands. 2019 IEEE Milan PowerTech. 2019; ():1-6.
Chicago/Turabian StyleTom Terlouw; Tarek AlSkaif; Wilfried Van Sark. 2019. "Optimal Energy Management of All-electric Residential Energy Systems in the Netherlands." 2019 IEEE Milan PowerTech , no. : 1-6.
While the large-scale deployment of Photovoltaic (PV) systems plays an important role in limiting global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological parameters at the PV site. In this paper, we analyse the interdependence of different meteorological variables and show their importance for PV output power estimation. Using Principal Component Analysis (PCA), we identify the primary meteorological variables for PV output power estimation. The numerical evaluation is performed using 3 years long of 9 meteorological variables data and PV output power data of 10 distinct rooftop PV systems, located in the city of Utrecht, the Netherlands. Simulation results show the interdependence between the meteorological variables and demonstrate that relative humidity, visibility, temperature and cloud cover are the most important variables for estimating PV output power in Utrecht.
Tarek AlSkaif; Soumyabrata Dev; Lennard Visser; Murhaf Hossari; Wilfried Van Sark. On the Interdependence and Importance of Meteorological Variables for Photovoltaic Output Power Estimation. 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) 2019, 2117 -2120.
AMA StyleTarek AlSkaif, Soumyabrata Dev, Lennard Visser, Murhaf Hossari, Wilfried Van Sark. On the Interdependence and Importance of Meteorological Variables for Photovoltaic Output Power Estimation. 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC). 2019; ():2117-2120.
Chicago/Turabian StyleTarek AlSkaif; Soumyabrata Dev; Lennard Visser; Murhaf Hossari; Wilfried Van Sark. 2019. "On the Interdependence and Importance of Meteorological Variables for Photovoltaic Output Power Estimation." 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) , no. : 2117-2120.
This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the ‘Present-day’ and the ‘High’ scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms.
Marte K. Gerritsma; Tarek A. AlSkaif; Henk A. Fidder; Wilfried G. J. H. M. Van Sark. Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future. World Electric Vehicle Journal 2019, 10, 14 .
AMA StyleMarte K. Gerritsma, Tarek A. AlSkaif, Henk A. Fidder, Wilfried G. J. H. M. Van Sark. Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future. World Electric Vehicle Journal. 2019; 10 (1):14.
Chicago/Turabian StyleMarte K. Gerritsma; Tarek A. AlSkaif; Henk A. Fidder; Wilfried G. J. H. M. Van Sark. 2019. "Flexibility of Electric Vehicle Demand: Analysis of Measured Charging Data and Simulation for the Future." World Electric Vehicle Journal 10, no. 1: 14.
The operation and production of batteries is associated with environmental impacts that can be quantified with Life Cycle Assessment methodologies. Current life cycle impact assessment methodologies do not assess metal criticality: they are based on geological availability or resource depletion only and do not consider socio-economic factors. Such factors are included by the concept of metal criticality. This paper determines the metal criticality of six home-based battery systems (Li-Ion: LFP-C, NMC-C, NCA-C, NCA-LTO; VRLA battery and the VRFB) for a photovoltaics self-consumption application based on a Life Cycle Assessment approach. Cumulative life cycle inventory results on extraction of metal resources are coupled with characterization factors of 13 metals derived from three state-of-the-art criticality methodologies. The results are presented for two functional units: (1) the installed battery system per kWh of energy delivered (per cycle); (2) additionally including necessary replacements of battery packs during the system lifetime. Due to substantial differences in terms of battery lifetimes between battery technologies, the latter functional unit turns out to be more meaningful. In general, there is a correlation between lower metal criticality scores (i.e. better performance) and batteries with a higher specific energy, longer battery lifetime and lower mass of metal consumption. LFP-C battery shows both low metal criticality scores and comparatively robust results, while VRFB exhibits low metal criticality but associated with relatively high uncertainties. In contrast, the VRLA battery performs the worst due to low discharge efficiency and relatively short battery lifetime. We argue that metal criticality could be reduced by improving the specific energy of the battery, by selecting low metal-intensive and low-critical metal containing components, by increasing the use of secondary metals and by selecting batteries with longer battery lifetimes.
Tom Terlouw; Xiaojin Zhang; Christian Bauer; Tarek Alskaif. Towards the determination of metal criticality in home-based battery systems using a Life Cycle Assessment approach. Journal of Cleaner Production 2019, 221, 667 -677.
AMA StyleTom Terlouw, Xiaojin Zhang, Christian Bauer, Tarek Alskaif. Towards the determination of metal criticality in home-based battery systems using a Life Cycle Assessment approach. Journal of Cleaner Production. 2019; 221 ():667-677.
Chicago/Turabian StyleTom Terlouw; Xiaojin Zhang; Christian Bauer; Tarek Alskaif. 2019. "Towards the determination of metal criticality in home-based battery systems using a Life Cycle Assessment approach." Journal of Cleaner Production 221, no. : 667-677.