Bridging the scales: A conceptual model for coordinated expansion of renewable power generation, transmission and storage

Relaxation of quantitative energy objectives on generation expansion planning: A computational and policy study

“…Evolutionary algorithms have been extensively used to solve the GEP optimization problem, ie, GA, [29][30][31][37][38][39][40] particle swarm optimization, 24,32,40-43 differential evolution, 39,40,44,45 ant colony optimization, 39,46 and evolution strategies (ES). 39,40 Moreover, some hybrid approaches combining GA with simulated annealing, 47 GA with generalized Benders decomposition, 48 and GA with dynamic programming 49 have been proposed.…”

The European renewable energy target for 2030 – An impact assessment of the electricity sector

“…A more dedicated and transparent analysis of the distributional question in Table A2 Techno-economic characteristics of thermal technologies and storage solutions. Source: Schröder et al (2013), European Commission (2014b), Haller et al (2012aHaller et al ( , 2012b, Schmid et al (2012) and own assumptions. the context of pan-European network planning is highly recommendable.…”

“…The modeling framework LIMES (Long-term Investment Model for the Electricity System) was also applied in Ludig et al (2011, In press) and Haller et al (2012b). Nahmmacher et al (2014a) provide an in-depth documentation of the modeling setup used in this analysis and Nahmmacher et al (2014b) describe the refined approach for modeling the integration of variable renewable sources across Europe.…”

Advanced Methods for Incorporating Solar Energy Technologies into Electric Sector Capacity-Expansion Models: Literature Review and Analysis

“…Modelers use a variety of techniques to select subsets that minimize such concerns. LIMES-EU uses an algorithm to select 12 characteristic days that are representative of temporal and spatial fluctuations (Haller et al 2012). RPM selects a week for each season that is most representative of "typical" seasonal electricity demand, i.e., the week with the lowest root mean square error deviation from the demand in each hour of the average week of the season (Mai et al 2013a).…”