Wind Capacity Factor Research Articles

Optimal heterogeneity in a simplified highly renewable European electricity system

The resource quality and the temporal generation pattern of variable renewable energy sources vary significantly across Europe. In this paper spatial distributions of renewable assets are explored which exploit this heterogeneity to lower the total system costs for a high level of renewable electricity in Europe. Several intuitive heuristic algorithms, optimal portfolio theory and a local search algorithm are used to find optimal distributions of renewable generation capacities that minimise the total costs of backup, transmission and renewable capacity simultaneously. Using current cost projections, an optimal heterogeneous distribution favours onshore wind, particularly in countries bordering the North Sea, which results in average electricity costs that are up to 11% lower than for a homogeneous reference distribution of renewables proportional to each country's mean load. The reduction becomes even larger, namely 18%, once the transmission capacities are put to zero in the homogeneous reference distribution. Heuristic algorithms to distribute renewable capacity based on each country's wind and solar capacity factors are shown to provide a satisfactory approximation to fully optimised renewable distributions, while maintaining the benefits of transparency and comprehensibility. The sensitivities of the results to changing costs of solar generation and gas supply as well as to the possible cross-sectoral usage of unavoidable curtailment energy are also examined.

Planning of Renewable Generation in Distribution Systems Considering Daily Operating Periods

This paper presents an optimization approach for planning of renewable generation in energy distribution systems. The considered generations are based on wind turbines and photovoltaic panels. The objective is to determine the optimal placement of the renewable distributed generation aiming at minimizing the investment and operational costs in a long term planning. The proposed approach represents the stochastic behavior of the renewable resources by handling historical data to determine the wind and photovoltaic capacity factors. The method for obtaining such factors considers the daily load periods, which allows representing the availability of the energetic resources in different periods. The optimization method is based on the bioinspired metaheuristic known as artificial immune system. Two systems of the literature and practical wind and solar data from Brazil are used to assess the proposed approach.

Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource?

Optimal siting of wind farms based on a pre-assessment of the spatiotemporal variability of wind resources is considered a suitable method for reducing fluctuations in the delivered output. In this study, we explore the potential for balancing wind energy generation in the Iberian Peninsula using Principal Component Analysis (PCA). This technique permits the discovery of possibly new promising locations for wind power harvesting and an evaluation of the existing wind farm network in terms of reliability in energy generation. Data input to the PCA consists of hourly wind capacity factor in a 5-km spatial resolution grid covering the entire peninsula. These data are derived from an equivalent wind farm power curve fed by modeled wind speed data from 80 m above ground level. PCA reveals three significant balancing patterns prevailing over the IP, where half of the currently operating wind farms in Spain are placed. Hence, among the many constituents of the existing wind farm network, these spots offer the best opportunity for stable power supply. The paper concludes by making proposals on an optimum wind capacity allocation based on the idea of equally distributing installed power between positive/negative dipoles emerging from balancing principal components.

Factors influencing calculation of capacity value of wind power: A case study of the Australian National Electricity Market (NEM)

Calculation of wind power capacity values for risk assessment of power system adequacy has attracted great attention in the literature. And the most popular approach has been the Effective Load Carrying Capability (ELCC) method which allows for the consideration of key factors such as wind capacity factor, forced outage rates (F.O.R) of conventional power stations, system reliability targets, and the correlation between wind availability and system load. However, comparatively little attention has been paid to analysing the effects of other factors such as the number of wind farms and wind installed capacity, the length of historic time series data on demand and wind resources. This paper provides an in-depth analysis of how these factors influence the calculation of capacity value for the Australian National Electricity Market (NEM) power system using metered half-hourly wind and load data for 2006 to 2013. The analysis incorporates the periods with extreme risk events. Our results show that capacity values depend greatly on the design of the simulation model used, and highlight the importance of capturing wind and load data points relating to extremely high demand periods. We compare our NEM-wide estimates to recent estimates for the State of South Australia.

Assessing wind curtailment under different wind capacity considering the possibilistic uncertainty of wind resources

For power system decision makers, they should decide a reasonable wind capacity or target penetration level considering the balance of wind curtailment, investment, system reliability and social benefit. Assessing wind curtailment under different wind capacity, considering the wind resource uncertainty, is therefore necessary for utilizing wind power effectively. In this paper, fuzzy wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources based on the wind speed distribution. By investigating the maximal wind power output that can be accepted in candidate system nodes, a fuzzy linear programming problem is proposed to decide the required wind capacity considering the fuzzy wind capacity factor. Based on the cumulative distribution function of wind capacity resulting from large number of simulations, the relationship between wind capacity and wind curtailment is determined. The effectiveness of the proposed model is validated by its application in two power grids. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty of wind resources and assessing wind curtailment under different wind capacity. Compared with previous research, this method can aid power system planners to allocate wind capacity considering the possibilistic uncertainty of wind resources.

Economic Analysis of Wind Turbine Installation in Taiwan

The wind speed characteristics are analyzed statistically based on a long-term hourly data record to evaluate the proper wind energy potential. The annual average wind speed and wind power density are investigated and compared by some significant indices, wind energy output and capacity factor, to show the variations of proper wind turbine specifications of installation in different locations of Taiwan. The minimum cost of wind energy is used to assess the economical feasibility for turbine installation in Taiwan. Great variations occur in the simulation results in both of the cost of energy and capacity factor. The detailed statistical analysis should be conducted to ensure the successful operation after wind turbine installations.

Combining wind farms with concentrating solar plants to provide stable renewable power

We evaluate the extent to which a combination of wind power and concentrating solar power (CSP) may lead to stable and even baseload power by taking advantage of: 1) spatiotemporal balancing of solar and wind energy resources and 2) storage capabilities of CSP plants. A case study is conducted for the region of Andalusia in Spain. To this end, spatiotemporal variability of modeled CSP and wind capacity factors in a 3-km spatial resolution grid were analyzed based on principal component analysis (PCA) and canonical correlation analysis (CCA). Results reveal that renewable baseload power can be obtained in the study region by locating wind farms and CSP plants using balancing patterns derived from CCA and PCA. In addition, the power fluctuation reduction attained from these patterns was substantially higher than those obtained by interconnecting randomly-located wind farms and CSP plants across the study region. Results were particularly meaningful for the winter season. Upon considering storage capability of the CSP plants, results proved better. The main difference was a higher firm capacity value associated with spring and summer seasons. For the other seasons, the contribution of thermal storage capabilities of the CSP plants to stable power proved less relevant.

MCP방법을 이용한 장기간 풍속 및 풍력에너지 변동 특성 분석

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and -9.57%~9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within .

측정-상관-예측법을 이용한 장기간 풍속 및 설비이용률의 예측

Long-term variations in wind speed and capacity factor(CF)on Seongsan wind farm of Jeju Island, South Korea were derived statistically. These lected areas for this study were Subji, having a year wind data at 30 m above ground level, Sinsan, having 30-year wind data at 10 m above ground level and Seongsan wind farm, where long-term CF was predicted.The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Each year’s CF was derived from the estimated 30-year time series wind data by running WAsP module. As are sult, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficient of variation, CV, and-16.5% ∼ 13.2% for the range of variati on, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ±4%.

Wind distribution and capacity factor estimation for wind turbines in the coastal region of South Africa

The operating curve parameters of a wind turbine should match the local wind regime optimally to ensure maximum exploitation of available energy in a mass of moving air. This paper provides estimates of the capacity factor of 20 commercially available wind turbines, based on the local wind characteristics of ten different sites located in the Western Cape region of South Africa. Ten-min average time series wind-speed data for a period of 1 year are used for the study. First, the wind distribution that best models the local wind regime of the sites is determined. This is based on root mean square error (RMSE) and coefficient of determination (R2) which are used to test goodness of fit. First, annual, seasonal, diurnal and peak period-capacity factor are estimated analytically. Then, the influence of turbine power curve parameters on the capacity factor is investigated. Some of the key results show that the wind distribution of the entire site can best be modelled statistically using the Weibull distribution. Site WM05 (Napier) presents the highest capacity factor for all the turbines. This indicates that this site has the highest wind power potential of all the available sites. Site WM02 (Calvinia) has the lowest capacity factor i.e. lowest wind power potential. This paper can assist in the planning and development of large-scale wind power-generating sites in South Africa.

Energy and exergy assessments for potential wind power in Turkey

The main objective of the study is to estimate wind power potential of Turkey and provide suitable data for evaluating potential wind power by using the wind data collected at 23 different wind-monitoring stations. In this study, energy and exergy analyses of wind power are used to estimate wind power potential in these areas. Exergy analysis of wind power is investigated according to air temperature and pressure at inlet and outlet of wind turbine; energy generated and heat loss from wind turbine. Also, energy and exergy analyses of wind power and capacity factor, energy and exergy efficiencies at 10, 25 and 50 m are given for these wind-monitoring stations.

SUSTAINABLE SUPPLY OF GLOBAL ENERGY NEEDS AND GREENHOUSE GAS REDUCTIONS

Nuclear plants emit virtually no greenhouse gases over their full life-cycle. Consequently, continued operation of existing nuclear plants is recognized as essential to meeting even the modest greenhouse gas reduction targets of the Kyoto Accord. However, much expanded nuclear deployment will be needed as developing economies aggressively grow GDP with its associated growth in electrical power. Projecting to 2040 and based on the scenarios of the United Nations Intergovernmental Panel on Climate Change’s (IPCC), we have examined deploying increased non-carbon energy sources for electricity production, including further conversion of electricity to hydrogen using conventional low-temperaturc water electrolysis. Our NuWind© model has been used to calculate the production costs for hydrogen in typical potential markets, using the actual prices of electricity paid by the Alberta Power Pool and by the Ontario Grid. The analysis shows clearly that by optimizing the co-production of hydrogen and electricity (referred to as the H2/e process) the cost for hydrogen produced can comfortably meet the US Department of Energy’s target for realistic nuclear investment costs, hydrogen generation systems, and wind capacity factors. The synergy of nuclear plus wind power for hydrogen generation plus co-production of electricity improves the economics of harnessing wind energy to produce hydrogen.

UK onshore wind capacity factors 1998–2005

SYNOPSIS In recent years there has been considerable interest in the performance of wind schemes; this article presents a definitive account of an analysis of the capacity factors for UK wind farms between 1998 and 2005 in relation to annual average wind speed.