A unified unit commitment — Economic dispatch model for short-term power system scheduling under high wind energy penetration

2014 ◽  
Author(s):  
Emmanouil A. Bakirtzis ◽  
Andreas V. Ntomaris ◽  
Evaggelos G. Kardakos ◽  
Christos K. Simoglou ◽  
Pandelis N. Biskas ◽  
...  
Keyword(s):  
Power System ◽  
Wind Energy ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
High Wind ◽  
Short Term ◽  
Power System Scheduling

Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method

2009 ◽  
Author(s):  
Gong Li ◽  
Jing Shi ◽  
Junyi Zhou
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Short Term ◽  
Goodness Of Fit Test ◽  
Wind Speed Forecasting

Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.

Comparative Study Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Amit Tiwari ◽  
Adarsh Dhar Dubey ◽  
And Devesh Patel
Keyword(s):  
Neural Network ◽  
Power System ◽  
Structural Changes ◽  
Unit Commitment ◽  
Network Reliability ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Short Term Load Forecasting

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling & for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning & operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling & unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flows & to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability & to the reduced occurrences of equipment failures & blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.

scholarly journals Security Constrained Stochastic Power System Scheduling Algorithms - With Wind Electric Generators

2021 ◽  
Author(s):  
Yu Peng
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Output ◽  
Unit Commitment ◽  
System Security ◽  
Transmission System ◽  
Short Term ◽  
Transmission Systems ◽  
Probabilistic Distribution ◽  
Ac Transmission

Power systems worldwide are embracing diverse supply mixes that incorporate a significant portion from renewables such as wind and solar energy. Wind energy is characterised by reliable equipment, but with an output that is uncertain and intermittent. In addition to equipment unreliability (system N-1 criterion), output uncertainties of wind electric generators (WEGs) introduce risk into daily power system schedules. This risk from the uncertainty of output from WEGs can be quantified as expected energy not served (EENS). Furthermore, the introduction of new forms of generation changes the methods of operating transmission systems, further necessitating the use of transmission security constraints in power systems optimization algorithms. This dissertation explores new approaches to stochastically model the real power output of WEGs and to efficiently tackle AC transmission system security constraints for power system optimization algorithms such as optimal power flow (OPF) and day-ahead unit commitment (UC). Usually, normal probabilistic distribution is used to model uncertainty in short-term wind power output forecast and compute EENS. In this dissertation, a new triangular approximate distribution (TAD) model is proposed which is a linear approximation of normal probabilistic distribution to model short-term wind power output forecast and compute EENS. This TAD model is used to formulate a practical risk-constrained fast OPF for transmission systems to simultaneously minimize: 1) risk due to uncertainties in power output from WEGs, and 2) the total operating cost. The integration of new energy resources causes transmission systems to operate in new, challenging, and often unforeseen operating states. Thus, it is imperative that UC algorithms incorporate AC transmission system security constraints and stochastically model output of WEGs to ensure reliable operation of transmission systems. As a first step, a successive mixed integer linear programming (MILP) method is proposed for AC transmission system security constrained unit commitment (SCUC) challenge. Fuzzy sets theory is used to model infeasible constraints in this MILP formulation. As a next step, the TAD model of WEGs is integrated into the MILP formulation of SCUC to create a fast security and risk constrained probabilistic UC algorithm. The two UC algorithms are tested on large systems.

scholarly journals Short-term power system scheduling under high renewable energy penetration

2016 ◽  
Author(s):  
Εμμανουήλ Μπακιρτζής
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Short Term ◽  
Power System Scheduling

Η παρούσα διδακτορική διατριβή παρουσιάζει λύσεις για την αντιμετώπιση της αυξημένης αβεβαιότητας και μεταβλητότητας στο βραχυπρόθεσμο προγραμματισμό του συστήματος ηλεκτρικής ενέργειας (ΣΗΕ), λόγω της αυξημένης διείσδυσης ανανεώσιμων πηγών ενέργειας στο μείγμα παραγωγής ηλεκτρικής ενέργειας. Παρουσιάζεται ένα καινοτόμο μοντέλο βραχυπρόθεσμου προγραμματισμού ως ένα μονοεπίπεδο εργαλείο πραγματικού χρόνου που πραγματοποιεί ενοποιημένη ένταξη μονάδων και οικονομική κατανομή (ΕΕΜΟΚ) σε κυλιόμενη βάση, με χρονικό ορίζοντα προγραμματισμού μέχρι και 36 ώρες. Παρουσιάζονται και εφαρμόζονται οι έννοιες της πολλαπλής χρονικής ανάλυσης και της μεταβλητής πολυπλοκότητας μοντελοποίησης για τον περιορισμό των υπολογιστικών απαιτήσεων. Το μοντέλο ΕΕΜΟΚ παρουσιάζεται σε αιτιοκρατικό και στοχαστικό πλαίσιο. Στο αιτιοκρατικό πλαίσιο, η αβεβαιότητα του καθαρού φορτίου αντιμετωπίζεται με τον προγραμματισμό εφεδρειών και παράλληλα παρουσιάζεται μια μέθοδος δυναμικής ποσοτικοποίησης των εφεδρειών. Στο στοχαστικό πλαίσιο, η αβεβαιότητα του καθαρού φορτίου αντιμετωπίζεται με τη χρήση σεναρίων φορτίου και αιολικής παραγωγής και παράλληλα παρουσιάζεται μία κατάλληλη μέθοδος δημιουργίας σεναρίων. Τα προτεινόμενα μοντέλα εξετάζονται μέσω ετήσιων προσομοιώσεων κυλιόμενου προγραμματισμού, χρησιμοποιώντας πραγματικά στοιχεία και μία τροποποιημένη μορφή του Ελληνικού ΣΗΕ. Υπολογίζονται ετήσιοι δείκτες απόδοσης όπως το κόστος λειτουργίας, η κυκλικότητα των θερμικών μονάδων, η απόρριψη φορτίου, η απόρριψη αιολικής ενέργειας και η οριακή τιμή συστήματος. Τα αποτελέσματα δείχνουν ότι η υλοποίηση του ΕΕΜΟΚ στο βραχυπρόθεσμο προγραμματισμό του ΣΗΕ αποφέρει σημαντικές βελτιώσεις στη λειτουργία του ΣΗΕ συγκριτικά με τα υφιστάμενα πολυεπίπεδα μοντέλα προγραμματισμού. Η εφαρμογή στοχαστικού προγραμματισμού ενισχύει την ιδέα του ΕΕΜΟΚ παρέχοντας ένα οικονομικά αποδοτικότερο πρόγραμμα κατανομής. Τέλος, παρουσιάζεται μία υπολογιστικά εφαρμόσιμη υλοποίηση του ΕΕΜΟΚ, που ενσωματώνει όλους τους περιορισμούς που χρησιμοποιούνται στις σύγχρονες αγορές ηλεκτρικής ενέργειας.

scholarly journals Frequency control analysis based on unit commitment schemes with high wind power integration: A Spanish isolated power system case study

2020 ◽  
Vol 121 ◽  
pp. 106044 ◽  
Author(s):  
Ana Fernández-Guillamón ◽  
José Ignacio Sarasúa ◽  
Manuel Chazarra ◽  
Antonio Vigueras-Rodríguez ◽  
Daniel Fernández-Muñoz ◽  
...  
Keyword(s):  
Power System ◽  
Wind Power ◽  
Unit Commitment ◽  
Frequency Control ◽  
Control Analysis ◽  
High Wind ◽  
Wind Power Integration ◽  
Commitment Schemes
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