Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters

Colmenar‐Santos, Antonio; Campíez-Romero, Severo; Enríquez-García, Lorenzo Alfredo; Pérez-Molina, Clara

doi:10.3390/en7116856

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…As for the total power installed in the grid (P T ), we initially used 120 GW, which is approximately the level of wind power installed in Europe in 2013 (117.289 GW) …”

Section: Methodssupporting

confidence: 90%

“…The CF is usually in the range 30% to 45% for offshore wind farms and in the range 15% to 45% for onshore ones. The average CF for European wind farms in the period 2003 to 2007 was 0.21. For this study, given that in this period the vast majority of the European wind farms were onshore and that we expect offshore wind farms to grow more than onshore ones, we considered it reasonable to choose 0.23 as the desired mean CF of the grid.…”

Section: Methodsmentioning

confidence: 52%

“…This gross CF has some losses from wake losses; electric losses up to the point where the electricity fed into the grid is measured; and production losses due to the mechanical availability of the wind turbine generators. Although Colmenar‐Santos et al showed that the losses depend on the wind regime, for the present study we assumed losses of 2.5% of the generated energy.…”

Section: Methodsmentioning

confidence: 99%

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Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

et al. 2017

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In this work we use the mean-variance portfolio optimization, using as input the power derived from the wind and density results of meteorological model simulations (ERA-Interim reanalysis), to minimize the variability of the wind power produced in a large region. The methodology involves selecting the placement of the wind farms on a high spatial resolution grid. We used the EU-28 region to check the method and perform sensitivity tests. We studied the influence of the ratio between the total installed power of the whole domain (P t ) and the maximum power that can be installed per cell (P mi ) on the variability of wind power yield. The results show that the reliability of the electrical system improves when P mi grows and worsens when P t grows. A quadratic fit relates the variability of the system and the aforementioned ratio. The optimization procedure tends to select groups of terrain cells where wind farms should be installed. These groups grow when more energy production is demanded of the system, but they roughly maintain their location. There is some evidence that in a larger region greater system reliability could be achieved. Most of the selected cells have either a high or a low capacity factor and those with the latter are crucial in enhancing system reliability. KEYWORDS electricity, Europe, MVP, planning, variability, wind energy 1 | INTRODUCTION In many countries, there has been a rapid development of wind energy. While a small amount of wind energy can be incorporated into the electrical system without difficulty, higher amounts of it generate problems in predicting how much energy has to be produced by other sources, mainly coal, oil, open-cycle gas turbines, combined-cycle gas turbines, combustible renewables and waste, and hydropower. 1 In addition, other undesirable consequences, such as difficulties for the grid or increased costs of secondary services, have already been identified and estimated. 2-4 While wind power prediction, mainly based on persistence and atmospheric models' predictions, helps in ameliorating the problem, a nonnegligible degree of uncertainty about the wind power that will be added to the electrical system still exists. It leads to instabilities in the electrical network and therefore limits the number of wind farms that it is desirable to have installed in a region. To compensate for that instability, a number of flexible resources (eg, dispatchable power plants and demand-side management) whose power output should be equal to the variability are required to cover it. 5 Therefore, to increase the amount of clean energy from wind power that is provided to the electrical network, either the number of flexible resources must also grow or the wind power variability must be reduced. The installation and maintenance of flexible resources increases the overall cost of energy. From the work of Roques et al 6 and Drake and Hubacek, 7 we know that variability of wind energy can be reduced by combining the power output of separate wind farms or countries in a powerful electrical...

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“…As for the total power installed in the grid (P T ), we initially used 120 GW, which is approximately the level of wind power installed in Europe in 2013 (117.289 GW) …”

Section: Methodssupporting

confidence: 90%

Section: Methodsmentioning

confidence: 52%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

et al. 2017

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“…Depending on its location, wind power generation may bring power losses or benefits. To calculate the losses, it is necessary to know the wind power production profile and load profile [27,28]. Wind power production profile should be determined based on the wind speed measurements on site and the power curves of the selected wind turbines [29].…”

Section: Exploitation Costsmentioning

confidence: 99%

Mathematical model for the optimal determination of voltage level and PCC for large wind farms connection to transmission network

Đorđević

Đurišić

2019

IET Renewable Power Generation

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This study presents a practical mathematical model for the determination of the optimal point of connection of large wind farms to the transmission network. A comparative analysis of costs of connecting wind farms to the transmission network is given, about their distance from the point of common coupling (PCC). Calculations have been made for different installed capacity of the wind farm and for different voltage levels of PCC. Based on these calculations, there have been defined competitive distances of a wind farm with a certain power from the network of different voltage levels (e.g. 110 and 220 kV) for which the connection costs are equal. Such calculations are widely applicable and can be used by transmission system operators and wind power plant developers to optimise and plan the connection of a wind farm to the transmission network.

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“…Meanwhile, resistance losses occurring in farm cabling are on the order of 1%. 33 Assuming that this applies to a s D 8 farm, this implies a loss per metre of 25 W m 1 per turbine for a 1.91 MW turbine. Multiplied by the present worth of the onshore turbines cost, this implies an opportunity cost of losses per turbine of $55 m 1 .…”

Section: A1 Onshore Parametersmentioning

confidence: 99%

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

et al. 2016

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Wind turbine spacing is an important design parameter for wind farms. Placing turbines too close together reduces their power extraction because of wake effects and increases maintenance costs because of unsteady loading. Conversely, placing them further apart increases land and cabling costs, as well as electrical resistance losses. The asymptotic limit of very large wind farms in which the flow conditions can be considered 'fully developed' provides a useful framework for studying general trends in optimal layouts as a function of dimensionless cost parameters. Earlier analytical work by Meyers and Meneveau (Wind Energy 15, 305-317 (2012)) revealed that in the limit of very large wind farms, the optimal turbine spacing accounting for the turbine and land costs is significantly larger than the value found in typical existing wind farms. Here, we generalize the analysis to include effects of cable and maintenance costs upon optimal wind turbine spacing in very large wind farms under various economic criteria. For marginally profitable wind farms, minimum cost and maximum profit turbine spacings coincide. Assuming linear-based and area-based costs that are representative of either offshore or onshore sites we obtain for very large wind farms spacings that tend to be appreciably greater than occurring in actual farms confirming earlier results but now including cabling costs. However, we show later that if wind farms are highly profitable then optimization of the profit per unit area leads to tighter optimal spacings than would be implied by cost minimization. In addition, we investigate the influence of the type of wind farm layout.

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Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters

Cited by 13 publications

References 23 publications

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

Mathematical model for the optimal determination of voltage level and PCC for large wind farms connection to transmission network

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

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