scholarly journals Two methods for estimating limits to large-scale wind power generation

2015 ◽  
Vol 112 (36) ◽  
pp. 11169-11174 ◽  
Author(s):  
Lee M. Miller ◽  
Nathaniel A. Brunsell ◽  
David B. Mechem ◽  
Fabian Gans ◽  
Andrew J. Monaghan ◽  
...  
Keyword(s):  
Power Generation ◽  
Kinetic Energy ◽  
Wind Power ◽  
Energy Flux ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Flux Method ◽  
Free Atmosphere

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way.

Effects of Large Scale Wind Power on Total System Variability and Operation: Case Study of Northern Ireland

2003 ◽  
Vol 27 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Shashi Persaud ◽  
Brendan Fox ◽  
Damian Flynn
Keyword(s):  
Power Generation ◽  
Northern Ireland ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wind Generation ◽  
Total System ◽  
Power Capacity ◽  
Spinning Reserve

The paper simulates the potential impact of significant wind power capacity on key operational aspects of a medium-sized grid-power system, viz. generator loading levels, system reserve availability and generator ramping requirements. The measured data, from Northern Ireland, consist of three years of 1/2 hourly metered records of (i) total energy generation and (ii) five wind farms, each of 5 MW capacity. These wind power data were scaled-up to represent a 10% annual energy contribution, taking account of diversity on the specific variability of total wind power output. The wind power generation reduced the system non-wind peak-generation. This reduction equalled 20% of the installed wind power capacity. There was also a reduction in the minimum non-wind generation, which equalled 43% of the wind power capacity. The analysis also showed that the spinning-reserve requirement depended on the accuracy of forecasting wind power ahead of scheduling, i.e. on the operational mode. When wind power was predicted accurately, (i) it was possible to reduce non-wind generation without over-commitment, but, (ii) the spinning-reserve non-wind conventional generation would usually have to be increased by 25% of the wind power capacity, unless quick-start gas generation was available. However, with unpredicted wind power generation, (i) despite reductions in non-wind generation, there was frequent over-commitment of conventional generation, but (ii) usually the spinning-reserve margin could be reduced by 10% of the wind power capacity with the same degree of risk. Finally, it was shown that wind power generation did not significantly increase the ramping duty on the system. For accurately predicted and unpredicted wind power the increases were only 4% and 5% respectively.

Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

2014 ◽  
Vol 543-547 ◽  
pp. 647-652
Author(s):  
Ye Zhou Hu ◽  
Lin Zhang ◽  
Pai Liu ◽  
Xin Yuan Liu ◽  
Ming Zhou
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wake Effect ◽  
Effect Model ◽  
The Individual

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

scholarly journals Weather dependent estimation of continent-wide wind power generation based on spatio-temporal clustering

2017 ◽  
Vol 14 ◽  
pp. 131-138 ◽  
Author(s):  
Bruno U. Schyska ◽  
António Couto ◽  
Lueder von Bremen ◽  
Ana Estanqueiro ◽  
Detlev Heinemann
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Stable Operation ◽  
Temporal Clustering ◽  
Renewable Power ◽  
Spatio Temporal

Abstract. Europe is facing the challenge of increasing shares of energy from variable renewable sources. Furthermore, it is heading towards a fully integrated electricity market, i.e. a Europe-wide electricity system. The stable operation of this large-scale renewable power system requires detailed information on the amount of electricity being transmitted now and in the future. To estimate the actual amount of electricity, upscaling algorithms are applied. Those algorithms – until now – however, only exist for smaller regions (e.g. transmission zones and single wind farms). The aim of this study is to introduce a new approach to estimate Europe-wide wind power generation based on spatio-temporal clustering. We furthermore show that training the upscaling model for different prevailing weather situations allows to further reduce the number of reference sites without losing accuracy.

scholarly journals A secondary option market design for reserve procurement by renewable energy sources.

2021 ◽  
Author(s):  
Reza Ghaffari
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Real Time ◽  
Wind Power ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Wind Power Generation ◽  
Reliable Operation ◽  
Actual Load ◽  
Balancing Services

Wind power generation is uncertain and intermittent accentuating variability. Currently in many power systems worldwide, the total generation-load unbalance caused by mismatch between forecast and actual wind power output is handled by automatic governor control and real-time 5-minute balancing markets, which are operated by the independent system operators for maintaining reliable operation of power systems. Mechanisms such as automatic governor control and real-time 5-minute balancing markets are in place to correct the mismatch between the load forecast and the actual load. They are not designed to address increased uncertainty and variability introduced by large-scale wind power or solar power generation expected in the future. Thus, large-scale wind power generation with increased uncertainty and intermittency causing variability poses a techno-economic challenge of sourcing least cost load balancing services (reserve).

scholarly journals Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6532
Author(s):  
Vahab Rostampour ◽  
Thom S. Badings ◽  
Jacquelien M. A. Scherpen
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Distribution System ◽  
Large Scale ◽  
Wind Power Generation ◽  
Forecast Errors ◽  
Monte Carlo Simulation Study ◽  
System Operator ◽  
The Impact

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

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