Assessment and Optimization of Wind Energy Integration Into the Power Systems: Application to the Portuguese System

Faias, Sérgio; Sousa, Jorge; Reis, Francisco; Castro, Rui

doi:10.1109/tste.2012.2201220

Cited by 26 publications

(14 citation statements)

References 20 publications

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“…Unfortunately, the massive penetration of wind generators in existing electric grids caused several side-effects, which determines the need for improving the robustness of system control and protection functions, mitigating the impacts of the large uncertainties induced by the intermittent and not-programmable nature of the wind power profiles [1]. To address this issue, the combination of effective wind forecasting tools and reliable generator models, implemented by knowledge discovering from experimental data streaming, represents one of the most promising enabling methodology, especially in assessing the impacts of wind power profiles on power system security and spinning reserve optimization [2,3], and in enhancing asset maintenance [3] and optimal bidding in electrical markets [4,5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

De¹,

Vaccaro

Villacci

2018

Electronics

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Abstract:The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling

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Section: Introductionmentioning

confidence: 99%

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

De¹,

Vaccaro

Villacci

2018

Electronics

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“…To optimally utilise variable renewable generation, such as wind power, power systems, and the way they are operated, are changing: transmission networks must connect distant renewable generation to load centres, distribution networks must accommodate small scale generation, and operators must consider the stochastic nature of this new variable generation when performing scheduling tasks [1]- [4]. Decisions relating to scheduling tasks are informed by forecasts on a variety of temporal and spatial scales, and the upper limit on the level of variable generation that can be accommodated by power systems will ultimately be set by the skill of these forecasts (and their users).…”

Section: Introductionmentioning

confidence: 99%

Kernel methods for short-term spatio-temporal wind prediction

Dowell

Weiss

Infield

2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Two nonlinear methods for producing short-term spatio-temporal wind speed forecast are presented. From the relatively new class of kernel methods, a kernel least mean squares algorithm and kernel recursive least squares algorithm are introduced and used to produce 1 to 6 hour-ahead predictions of wind speed at six locations in the Netherlands. The performance of the proposed methods are compared to their linear equivalents, as well as the autoregressive, vector autoregressive and persistence time series models. The kernel recursive least squares algorithm is shown to offer significant improvement over all benchmarks, particularly for longer forecast horizons. Both proposed algorithms exhibit desirable numerical properties and are ripe for further development.

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“…Thus, it is essential to evaluate the grid integration of wind power [2]. A volume of researches related to wind power integration has been reported in the literature and can be categorized as the following three streams: 1) Assessment of maximal capability of incorporating wind power under various constraints [3][4][5][6][7][8][9]; 2) Economic dispatch of wind power with other power generation sources [10][11][12][13][14]; 3) Maximization of wind power penetration level in an existing grid [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Unit commitment model was applied to discover suitable operational strategies of units in a hybrid power system [21]. These studies [10][11][12][13][14] cared more about ratings, cost, and locations of complementary resources. The third research stream focused on maximizing the penetration level of wind power while considering the power system technical constraints [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

Sun

Xie

Bie

et al. 2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Due to the energy crisis, environmental contamination and other problems related with conventional generations, it is essential to consider the integration of renewable energy, especially wind power. This paper, therefore, outlines the design and study of an interval optimization model to determine optimal wind capacity in an existing power grid. Interval wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources. This model aims at minimizing the overall cost including the variable cost of coal-fired generation, start-up/shutdown cost of generation units, capital investment of wind farm, reserve cost, penalty cost of wind curtailment and fixed operational cost of wind power. The effectiveness of the proposed model is validated by its application in a real-life power grid. Compared with previous research, this method can aid power system planners to make decisions about wind capacity considering the possibilistic uncertainty of wind resource.

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Assessment and Optimization of Wind Energy Integration Into the Power Systems: Application to the Portuguese System

Cited by 26 publications

References 20 publications

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

Kernel methods for short-term spatio-temporal wind prediction

Optimal wind capacity integration considering the possibilistic uncertainty of wind resources

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