Variability Of Wind Power Production Research Articles

Which wind turbine types are needed in a cost‐optimal renewable energy system?

AbstractPrevious research has indicated that wind power plants can be designed to have less‐variable power generation, thereby mitigating the drop in economic value that typically occurs at high wind power penetration rates. This study investigates the competitiveness of adapted turbine design and the interplay with other flexibility measures, such as batteries and hydrogen storage, for managing variations. The analysis covers seven turbine designs for onshore and offshore wind generation, with different specific power ratings and hub heights. Various flexibility measures (batteries, hydrogen storage and transmission expansion) are included in the optimization of investment and dispatch of the electricity system of northern Europe. Three driving forces for turbine design selection are identified: (1) lowest cost of electricity generation; (2) annual wind production per land area and (3) improved generation profile of wind power. The results show that in regions with good wind resources and limited availability of variation management, it is cost‐efficient to reduce the variability of wind power production by adapting the turbine design. This remains the case when variation management is available in the form of batteries, hydrogen storage and transmission system expansion. Moreover, it is more cost‐effective to improve variability by changing the specific power rating rather than the turbine hub height.

Optimal offering strategy for wind-storage systems under correlated wind production

This paper formulates the offering problem for a cluster of wind-storage systems in the day-ahead energy market using a risk-constrained stochastic programming approach that anticipates different operating conditions in the real-time energy market. Wind-storage systems can be jointly operated as a cluster so as to achieve higher profitability. However, a meaningful positive correlation among the production provided by wind farms located in the cluster results in a higher level of uncertainty that imposes additional risk. A key issue is how this correlation influences the operation of the cluster in the energy markets. In order to study this subject, this paper presents the uncertainties involved by means of a number of correlated scenarios including: (i) the correlated prices in the day-ahead and the real-time markets, and (ii) the correlated wind power production of multiple wind farms jointly generated using an innovative scenario generation methodology. The comparative statistical analysis validates the good accuracy of the method proposed in order to capture the spatio-temporal correlation among the wind farms. The results of a realistic case study are, moreover, compared with those obtained by considering that the scenarios are generated individually for each wind farm. Upon considering the latter, the variability of wind power production is underestimated, which has a negligible impact on the expected profit; however, the profit risk modeled using the conditional value-at-risk is significantly overestimated. The overestimation error particularly concerns a less risk-averse operator of the cluster in the case of low wind power production.

Wind power variability during the passage of cold fronts across South Africa

Wind is a naturally variable resource that fluctuates across timescales and, by the same token, the electricity generated by wind also fluctuates across timescales. At longer timescales, i.e., hours to days, synoptic-scale weather systems, notably cold fronts during South African winter months, are important instigators of strong wind conditions and variability in the wind resource. The variability of wind power production from aggregates of geographically disperse turbines for the passage of individual cold fronts over South Africa was simulated in this study. When considering wind power variability caused by synoptic-scale weather patterns, specifically cold fronts, the timescale at which analysis is conducted was found to be of great importance, as relatively small mean absolute power ramps at a ten-minute temporal resolution, order of 2-4% of simulated capacity, can result in large variations of total wind power production (at the order of 32–93% of simulated capacity) over a period of three to four days as a cold front passes. It was found that when the aggregate consists of a larger and more geographically dispersed set of turbines, as opposed to a smaller set of turbines specifically located within cold-front dominated high wind areas, variability and the mean absolute ramp rates decrease (or gets ‘smoothed’) across the timescales considered. It was finally shown that the majority of large simulated wind power ramp events observed during the winter months, especially at longer timescales, are caused by the passage of cold fronts.

Simulation of aggregate wind farm short-term production variations

The variability of wind power production poses the greatest challenge in the integration of large-scale wind power in power systems. Furthermore, larger-scale penetration implies a wider geographical spreading of installed wind power, resulting in reduced variability and the smoothing effect of total power generation. Therefore, analysis of the impact of wind power variations on power system operation requires adequate modeling of aggregate power output from geographically dispersed wind farms. This paper analyzes different aspects of Markov chain Monte Carlo simulation methods for the synthetic generation of dependent wind power time series. However, testing indicates that these approaches do not adequately model the stochastic dependence between wind power time series in conjunction with individual persistence which is necessary to obtain realistic distributions of aggregate power output and total power variations. Consequently, a novel approach based on a modified second-order Markov chain Monte Carlo simulation is proposed. Simulation results show that this method obtains synthetic time series of aggregate wind power which very closely fit the original data, with respect to both the cumulative density function of total output power and the probability density function of power variations.

Role of hydrogen in future North European power system in 2060

The Balmorel model has been used to calculate the economic optimal energy system configuration for the Scandinavian countries and Germany in 2060 assuming a nearly 100% coverage of the energy demands in the power, heat and transport sector with renewable energy sources. Different assumptions about the future success of fuel cell technologies have been investigated as well as different electricity and heat demand assumptions. The variability of wind power production was handled by varying the hydropower production and the production on CHP plants using biomass, by power transmission, by varying the heat production in heat pumps and electric heat boilers, and by varying the production of hydrogen in electrolysis plants in combination with hydrogen storage. Investment in hydrogen storage capacity corresponded to 1.2% of annual wind power production in the scenarios without a hydrogen demand from the transport sector, and approximately 4% in the scenarios with a hydrogen demand from the transport sector. Even the scenarios without a demand for hydrogen from the transport sector saw investments in hydrogen storage due to the need for flexibility provided by the ability to store hydrogen. The storage capacities of the electricity storages provided by plug-in hybrid electric vehicles were too small to make hydrogen storage superfluous.

Operational costs induced by fluctuating wind power production in Germany and Scandinavia

Adding wind power generation in a power system changes the operational patterns of the existing units due to the variability and partial predictability of wind power production. For large amounts of wind power production, the expectation is that the specific operational costs (fuel costs, start-up costs, variable operation and maintenance costs, costs of consuming CO2 emission permits) of the other power plants will increase due to more operation time in part-load and more start-ups. The change in operational costs induced by the wind power production can only be calculated by comparing the operational costs in two power system configurations: with wind power production and with alternative wind production having properties such as conventional production, that is, being predictable and less variable. The choice of the characteristics of the alternative production is not straightforward and will therefore influence the operational costs induced by wind power production. A method is applied for calculating the change in operational costs due to wind power production using a stochastic optimisation model covering the power systems in Germany and the Nordic countries. Two cases of alternative production are used to calculate the change in operational costs, namely perfectly predictable wind power production enabling the calculation of the costs connected to partial predictability and constant wind power production enabling the calculation of the operational costs connected to variability of wind power production. A 2010 case with three different wind power production penetration levels is analysed.

Wind Power in Ontario

The purpose of this article is to investigate wind turbine production, the variability of that production, and the relationship between output and system-wide demand. A review of the literature reveals that a variety of measures (and methods) to explore the variability of wind power production exist. Attention then turns to the province of Ontario (Canada), and the performances of four wind farms are examined for 2006 and 2007. Key conclusions include that the wind farms' capacity factors vary from 27.6% to 35.6%, with higher values in winter as compared to summer; wind power performs better than the seasonal average during peak periods; wind is a better “partner” for the Ontario electricity system in the winter as opposed to the summer; and the increased geographic distribution of wind farms decreases their collective variability.