Optimal future energy mix assessment considering the risk of supply for seven European countries in 2030 and 2050

[1]  Matteo Giacomo Prina,et al.  Evaluating near-optimal scenarios with EnergyPLAN to support policy makers , 2023, Smart Energy.

[2]  Matteo Giacomo Prina,et al.  Municipal energy system modelling – A practical comparison of optimisation and simulation approaches , 2023, Energy.

[3]  Poul Alberg Østergaard,et al.  A multi-objective optimization approach in defining the decarbonization strategy of a refinery , 2022, Smart Energy.

[4]  N. Duić,et al.  Optimization of the possible pathways for gradual energy system decarbonization , 2022, Renewable Energy.

[5]  Matteo Giacomo Prina,et al.  The EPLANoptMAC model to plan the decarbonisation of the maritime transport sector of a small island , 2022, Energy.

[6]  Matteo Giacomo Prina,et al.  The EPLANopt model for Favignana island's energy transition , 2021 .

[7]  M. Rocco,et al.  Quantifying the impact of low carbon transition scenarios at regional level through soft-linked energy and economy models: The case of South-Tyrol Province in Italy , 2021 .

[8]  Wolfram Sparber,et al.  Optimisation method to obtain marginal abatement cost-curve through EnergyPLAN software , 2021 .

[9]  Hugo Morais,et al.  Polish Energy Transition 2040: Energy Mix Optimization Using Grey Wolf Optimizer , 2021, Energies.

[10]  Andrea Menapace,et al.  The design of 100 % renewable smart urb an energy systems: The case of Bozen-Bolzano , 2020 .

[11]  Wolfram Sparber,et al.  Classification and challenges of bottom-up energy system models - A review , 2020, Renewable and Sustainable Energy Reviews.

[12]  Alessandro Prada,et al.  Integrated and dynamic energy modelling of a regional system: A cost-optimized approach in the deep decarbonisation of the Province of Trento (Italy) , 2020, Energy.

[13]  Michel Noussan,et al.  Analysis of smart energy system approach in local alpine regions - A case study in Northern Italy , 2020 .

[14]  Matteo Giacomo Prina,et al.  Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios , 2020 .

[15]  A. Toffolo,et al.  Towards Optimal Sustainable Energy Systems in Nordic Municipalities , 2020, Energies.

[16]  J. Lilliestam,et al.  Home-made or imported: On the possibility for renewable electricity autarky on all scales in Europe , 2019, Energy Strategy Reviews.

[17]  Lisa Göransson,et al.  Interconnection of the electricity and heating sectors to support the energy transition in cities , 2019 .

[18]  Olexandr Balyk,et al.  Modelling the future low-carbon energy systems - case study of Greater Copenhagen, Denmark , 2019 .

[19]  Mário Costa,et al.  Increasing the penetration of renewable energy sources in isolated islands through the interconnection of their power systems. The case of Pico and Faial islands, Azores , 2019, Energy.

[20]  Poul Alberg Østergaard,et al.  Evaluation of electricity storage versus thermal storage as part of two different energy planning approaches for the islands Samsø and Orkney , 2019, Energy.

[21]  D. Astiaso Garcia,et al.  Synergy between smart energy systems simulation tools for greening small Mediterranean islands , 2019, Renewable Energy.

[22]  David Moser,et al.  Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning , 2019, Applied Energy.

[23]  A. Bhuvanesh,et al.  Aiming towards pollution free future by high penetration of renewable energy sources in electricity generation expansion planning , 2018, Futures.

[24]  David Moser,et al.  Incorporating combined cycle gas turbine flexibility constraints and additional costs into the EPLANopt model: The Italian case study , 2018, Energy.

[25]  Behnam Zakeri,et al.  Energy security impacts of a severe drought on the future Finnish energy system. , 2018, Journal of environmental management.

[26]  Roberto Vaccaro,et al.  Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model , 2018, Energy.

[27]  Luigi Crema,et al.  An innovative multi-objective optimization approach for long-term energy planning , 2017 .

[28]  Max Roser,et al.  CO₂ and Greenhouse Gas Emissions , 2017 .

[29]  Neven Duić,et al.  Impact of high penetration of wind and solar PV generation on the country power system load: The case study of Croatia , 2016 .

[30]  Luigi Crema,et al.  Designing optimized energy scenarios for an Italian Alpine valley: the case of Giudicarie Esteriori , 2016 .

[31]  Neven Duić,et al.  Two methods for decreasing the flexibility gap in national energy systems , 2016 .

[32]  Brian Vad Mathiesen,et al.  Energy Storage and Smart Energy Systems , 2016 .

[33]  Markus Wagner,et al.  Incorporating domain knowledge into the optimization of energy systems , 2016, Appl. Soft Comput..

[34]  Gianluigi Lo Basso,et al.  Hydrogen to link heat and electricity in the transition towards future Smart Energy Systems , 2016 .

[35]  Goran Krajačić,et al.  Role of District Heating in Systems with a High Share of Renewables: Case Study for the City of Osijek , 2016 .

[36]  Brian Vad Mathiesen,et al.  Smart Energy Europe: The technical and economic impact of one potential 100% renewable energy scenario for the European Union , 2016 .

[37]  Matteo Giacomo Prina,et al.  Smart energy systems applied at urban level: the case of the municipality of Bressanone-Brixen , 2016 .

[38]  William D'haeseleer,et al.  Impact of the level of temporal and operational detail in energy-system planning models , 2016 .

[39]  H. Ossenbrink,et al.  DEPLOYMENT PATHWAYS FOR PHOTOVOLTAICS IN THE EU TOWARDS 2020: COMPARING ECONOMIC FACTORS WITH POLICIES AT MUNICIPAL LEVEL , 2015 .

[40]  Ilija Batas Bjelić,et al.  Simulation-based optimization of sustainable national energy systems , 2015 .

[41]  Brian Vad Mathiesen,et al.  Smart Energy Systems for coherent 100% renewable energy and transport solutions , 2015 .

[42]  Paula Varandas Ferreira,et al.  Renewable energy scenarios in the Portuguese electricity system , 2014 .

[43]  Brian Vad Mathiesen,et al.  Smart Energy Systems: Holistic and Integrated Energy Systems for the era of 100% Renewable Energy , 2013 .

[44]  Neven Duić,et al.  A 100% renewable energy system in the year 2050: The case of Macedonia , 2012 .

[45]  Brian Vad Mathiesen,et al.  The technical and economic implications of integrating fluctuating renewable energy using energy storage , 2012 .

[46]  E. Jochem,et al.  Introduction to Energy Systems Modelling , 2012 .

[47]  David Connolly,et al.  The first step towards a 100% renewable energy-system for Ireland , 2011 .

[48]  B. Mathiesen,et al.  Modelling the existing Irish energy-system to identify future energy costs and the maximum wind penetration feasible , 2010 .

[49]  Brian Vad Mathiesen,et al.  Energy system analysis of 100% renewable energy systems-The case of Denmark in years 2030 and 2050 , 2009 .

[50]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[51]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[52]  P. Laha,et al.  Low carbon electricity system for India in 2030 based on multi-objective multi-criteria assessment , 2021 .

[53]  Wolfram Sparber,et al.  EPLANopt optimization model based on EnergyPLAN applied at regional level: the future competition on excess electricity production from renewables , 2020 .

[54]  K. Skytte,et al.  Decarbonizing Sweden’s energy and transportation system by 2050 , 2017 .

[55]  Henrik Lund,et al.  Tool: The EnergyPLAN Energy System Analysis Model , 2010 .