The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.

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The authors wish to point out a typesetting error in Equation (1) of the above paper, published as [1] in this journal. An addition sign “+” was mistakenly replaced with a multiplication sign “∙” in this equation, as shown in Equation (1a) below. [Formula presented] There correct version of this equation is given in Equation (1b): [Formula presented]where n is the lifetime of the technology, I0 the investment [$], Mt the annual costs in year t [$/year], Et energy produced in year t [MWh/year] and i the interest rate. The authors apologise for any inconveince caused. This minor error has no implications for the validity of the conclusions and recommendations presented in section 6 of the original paper.@en

This research is aimed at designing and optimising a fully renewable energy system for the GCC countries of the Arabian Peninsula for the year 2050 . This includes deriving the energy potential of open-field PV and wind technologies in the region. Apart from the electricity demand of the power sector, the hydrogen demand of the steel, ammonia and road&rail transport sectors is also considered, as well as the export of hydrogen in the liquid form. Different scenarios are considered for a better understanding of the operation of the system. Li-ion batteries and gaseous hydrogen vessels are employed to balance production and demand, while reconversion technologies are also part of the system together with powerlines and hydrogen pipelines. The study shows that the potential of onshore wind and solar energy is way greater than total energy demand, while offshore wind potential is of a comparable size to total energy demand. On average, solar energy is the cheapest source of electricity at 2.9 €c/kWh, followed by onshore wind at 4.9 €c/kWh and offshore wind at 9.9 €c/kWh. However, the minimum cost registered by solar and onshore wind technologies is almost the same at around 2.2 €c/kWh, while offshore turbines do not fall below 5.0 €c/kWh. In a cooperation scenario considering local electricity and hydrogen demand, total annual costs are equal to 130 €bln/year, 21% less than a similar scenario where countries are isolated from each other. Twice as much electricity is produced through wind turbines than PV farms under the cooperation scheme, whereas solar energy slightly dominates the electricity mix in the isolation scenario. Besides, the system relies more on curtailment and battery storage than reconversion. The coupling of the power and hydrogen sectors leads to a total costs reduction of 7%. The LCOE found for the system is of 6.1 €c/kWh, which is around 20% more than the current LCOE of modern gas turbines in the region. The LCOH of gaseous and liquid hydrogen at the port is of 2.5 and 3.0 €/kg respectively, which is 2-3 times more expensive than crude oil and several times more expensive than LNG based on current breakeven costs in the region.

A recent article in this journal claimed to assess the socio-technical potential for onshore wind energy in Europe. We find the article to be severely flawed and raise concerns in five general areas. Firstly, the term socio-technical is not precisely defined, and is used by the authors to refer to a potential that others term as merely technical. Secondly, the study fails to account for over a decade of research in wind energy resource assessments. Thirdly, there are multiple issues with the use of input data and, because the study is opaque about many details, the effect of these errors cannot be reproduced. Fourthly, the method assumes a very high wind turbine capacity density of 10.73 MW/km2 across 40% of the land area in Europe with a generic 30% capacity factor. Fifthly, the authors find an implausibly high onshore wind potential, with 120% more capacity and 70% more generation than the highest results given elsewhere in the literature. Overall, we conclude that new research at higher spatial resolutions can make a valuable contribution to wind resource potential assessments. However, due to the missing literature review, the lack of transparency and the overly simplistic methodology, Enevoldsen et al. (2019) potentially mislead fellow scientists, policy makers and the general public.@en

The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers’ choices are important, institutional changes are still also necessary for more openness and transparency in energy research.@en