Why we must move beyond LCOE for renewable energy design

Loth, Eric; Qin, Chris; Simpson, Juliet G.; Dykes, Katherine

doi:10.1016/j.adapen.2022.100112

Cited by 31 publications

(15 citation statements)

References 24 publications

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“…7 labels each supply chain number with its respective technologies. Despite criticism for relying on narrow conclusions derived from levelised cost analyses, 69 this report evaluated the levelised cost of hydrogen (LCOH) for all supply chains used in Section 3.1. An SMR supply chain, 41 , has the lowest LCOH of all supply chains at 2.28 USD kg −1 , while a nitrogen reduction supply chain, 27 , had the lowest electrolyser LCOH at 3.26 USD kg −1 .…”

Section: Discussionmentioning

confidence: 99%

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain

Alfonso Vargas,

Kim,

Alfonso Vargas

et al. 2024

Energy Environ. Sci.

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Section: Discussionmentioning

confidence: 99%

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain

Alfonso Vargas,

Kim,

Alfonso Vargas

et al. 2024

Energy Environ. Sci.

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“…Since spot prices change each hour and there are distinct yearly weather patterns, we use these time scales to calculate the Annual Energy Revenue (AER). We can calculate AER using time-series [3] or with a statistical approach [7]:…”

Section: A Stationary Revenue Modelmentioning

confidence: 99%

“…AEV has the same units (Wh) as AEP but includes the energy devaluation when production and market demand misalign. Like Loth et al [3], we use the average annual price (v) to define the hourly normalised price (ṽ = v/v). If we combine this with Equation (2), we can write the AER as a function of the non-dimensional price:…”

Section: The Annual Energy Valuementioning

confidence: 99%

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Incorporating Electricity Prices in Wind Turbine Design: Introducing the AEV Metric

Bechmann,

Barlas,

Madsen

2024

J. Phys.: Conf. Ser.

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This paper addresses the challenge of incorporating electricity prices into wind turbine design methods and shows how price volatility drives wind turbines towards larger rotors and lower specific power. Since wind speed and electricity prices fluctuate, current efforts to estimate a wind turbine’s revenue are based on time-series approaches. However, this paper presents a new way of accounting for price volatility based on wind distributions, which is computationally cheap and easily integrates with current wind turbine and farm design methods and tools. The new method demonstrates that a traditional wind turbine can lose more than 15% of its revenue in open energy markets like Denmark due to price volatility. Designing turbines with lower specific power can substantially increase revenue by producing more energy at low wind speeds with higher energy demand and electricity prices.

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“…The impact of technological choices (for example, offshore support structures or powertrain type) on LCOE is well understood. Looking beyond LCOE, an increasing number of studies is now focusing on wind energy system optimization for economic value [1,2]. On the environmental side, various life cycle assessment (LCA) analyses have been conducted to quantify the environmental impact of different wind turbine and farm configurations [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

How do technological choices affect the economic and environmental performance of offshore wind farms?

Kainz,

Guilloré,

Bottasso

2024

J. Phys.: Conf. Ser.

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The ongoing energy transition towards fully sustainable energy systems requires designing wind farms looking beyond the sole levelized cost of energy, in order to concurrently ensure not only the economic profitability but also the environmental friendliness of future plants. Within this new approach to design, it becomes necessary to understand the effects that various possible technological choices have on both the economic and the environmental performance of wind farms. This study presents a framework designed to support these coupled economic-environmental assessments. The capabilities of the code are showcased by analysing the impact of different choices in terms of support structure type, specific power, tower height, powertrain type, and array and export voltage level for an exemplary offshore farm, chosen here as the IEA Wind 740-10-MW Reference Offshore Wind Plant with irregular layout. While the effects of many technological choices on the cost of energy are already well understood by industry, the present analysis shows that — at least in this specific case — climate change impacts are mainly driven by steel production, due to the massive amount of required material, but also, interestingly, by vessel activities. A low specific power, tall towers, and a high export cable voltage appear to offer the greatest potential for the concurrent improvement of the environmental and economic performance of the plant.

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Why we must move beyond LCOE for renewable energy design

Cited by 31 publications

References 24 publications

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain

Incorporating Electricity Prices in Wind Turbine Design: Introducing the AEV Metric

How do technological choices affect the economic and environmental performance of offshore wind farms?

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Why we must move beyond LCOE for renewable energy design

Cited by 31 publications

References 24 publications

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H2 supply chain

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H2 supply chain

Incorporating Electricity Prices in Wind Turbine Design: Introducing the AEV Metric

How do technological choices affect the economic and environmental performance of offshore wind farms?

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Product

Resources

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Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain

Nurturing the blossoming hydrogen economy using HBAT: modelling every link in the H₂ supply chain