• Energy Research

A topographic multiplier is widely used to evaluate the effect of topography on the design wind speed. The maximum value of directional topographic multipliers has been used as the topographic multiplier in many design codes. However, this simplified method is conservative and overestimates the speedup effect in some cases. In this paper a topographic multiplier considering wind directionality is proposed by using Monte Carlo simulation, and investigated at two typical meteorological stations. Then a reduction factor is proposed as the ratio of the topographic multiplier estimated by Monte Carlo simulation to that obtained by the simplified method. A topographic variation coefficient is proposed as the deviation of directional topographic multipliers and investigated systematically by using terrain models. As a result, the reduction factor decreased as the topographic variation increased. An empirical formula is proposed to improve the simplified method.

In this study, the availability and the levelized cost of energy (LCOE) are investigated considering failure rate and downtime for onshore wind turbines in Japan. The failure mode effect analysis is conducted using the wind turbine failure database collected by the New Energy and Industrial Technology Department Organization (NEDO). The normalized failure rate and downtime between Europe and Japan are comparable. The occurrence rate is similar between Europe and Japan, but the downtime in Japan is much longer than that of Europe. Three cost-reduction scenarios are then proposed to improve availability and to reduce LCOE using assumed failure rate and downtime in each mode based on the industry interview and best practices in Japan. The availability is improved from 87.4% for the baseline scenario to 92.7%, 95.5% and 96.4% for the three scenarios, and LCOE is also reduced from 13.7 Yen/kWh to 11.9, 11.0 and 10.7 Yen/kWh. Finally, the probability distributions of downtime and repair cost are obtained for each failure mode. It is found that the probability distributions of the failure modes with the shortest downtime show similar probability distributions regardless of the size of the assembly. The effects of downtime and repair-cost uncertainties on LCOE are also evaluated.

AbstractThe costs of wind power have declined to levels on par with or below those of conventional sources in many parts of the world. Wind power has become one of the fastest‐growing sources of new electricity generation. We take stock of wind power cost evolution over the past 20 years, review methodologies commonly used for cost assessment, discuss the potential for continued cost reduction, and identify anticipated cost and value drivers. Our scope includes both onshore and offshore wind technologies. We draw from a vast body of literature on these topics to highlight key trends, approaches, and limitations. Furthermore, we discuss strategies for wind power assets to enhance their marginal economic value to the broader power system and consumers. We identify a myriad of factors that are expected to influence the future cost and value of wind power, including siting, project scale, turbine size, operational synergies, commodity prices, advancements in turbine technologies, enhanced management of the wind resource, and novel control technologies that provide value for the electricity grid. Because the common methods for forecasting future costs each have their own strengths and weaknesses, we find the best insights are elicited from a combination of methods. Overall, researchers and analysts anticipate further sizable cost reductions for onshore and offshore wind. Midrange forecasts for levelized cost of energy in 2050 are generally between $20 and $30/MWh for onshore wind and $40 and $60/MWh for offshore wind, a reduction to approximately half of today's levels. Optimistic forecasts anticipate these levels as early as 2030.This article is categorized under: Wind Power > Economics and Policy

AbstractIn this study, an anomaly detection and prediction method for high‐tension bolts is proposed by using the measured strain of tower shell. The finite element models (FEMs) for the high‐tension bolts and tower‐top are built and validated with measurement data. An algorithm of anomaly detection and prediction is then developed by applying Mahalanobis‑Taguchi system. The threshold of abnormal condition for high‐tension bolts is proposed, and the formula for the prediction of remaining axial bolt force is derived by using the strain of tower shell obtained from the FEM model. Finally, onsite measurements are performed at Taikoyama wind farm to investigate the relationship between the strain of tower shell and the remaining axial bolt force. It is found that the proposed threshold based on the FEM model succeeds in detecting abnormal bolt by the Mahalanobis‑Taguchi system, and the proposed formula accurately predicts the remaining axial bolt force.

In this study, offshore wind climate assessments are carried out by using mesoscale model Weather Research and Forecasting (WRF) and validated by measurement at a demonstration site located 3.1 km offshore of Choshi. An optimal nudging method is investigated by using offshore and meteorological observations. The land-use datasets are then created from a higher-resolution land-use data by using a maximum area sampling scheme according to the horizontal resolution of the mesoscale model. Finally, the sea surface temperature datasets are corrected by observation data. It is found that the relative error of annual wind speed is reduced from 7.3% to 2.2% and the correlation coefficient between predicted and measured wind speed is improved from 0.80 to 0.84 by considering the effects of land-use and sea surface temperature.

This presentation was part of the EERA JP Wind SP8 WORKSHOP on Future market arrangements for large-scale offshore wind energy development in the European Seas (June 10).