Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing
Date
2021-01-01
Author
Çabuk, Sezer
Mert, Özenç Murat
Kestel, Sevtap Ayşe
Kalaycı, Erkan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
267
views
0
downloads
Cite This
Hydro inflow forecasting is crucial for effective hydro optimization, virtual power plant pricing, volume risk management, and weather derivatives pricing in the electricity markets. Predicting hydro inflow allows the decision-makers to economically use water for optimal periods, quantify volume risk and determine effective portfolio management strategies. This study aims pricing a hydroelectricity power plant as a Virtual Power Plant based on Turkish energy markets. For pricing of such a non-standard option, inflow and price scenarios and optimization model with constraints are performed. For the hydro inflow forecasting utilized for the optimization model, SARIMAX with precipitation as an exogenous variable is applied. In addition to the point forecasts, we generate various inflow scenarios based on the residuals as a stochastic process for defined VPP. Moreover, a hydro optimization problem where the objective function maximizes the expected value of generation by tracing generated inflow and price scenarios is made. Price scenarios are simulated using the hourly behavior of historical Day-Ahead Market. The optimization outputs are evaluated according to different prices and inflow levels. For a defined VPP, Volume at Risk measure is defined to measure the risky volume for the valuation of VPP.
Subject Keywords
Hydro inflow forecast
,
Virtual power plant
,
Optimization
,
SARIMAX
,
Volume at risk
URI
https://link.springer.com/chapter/10.1007/978-3-030-84981-8_7#citeas
https://hdl.handle.net/11511/94764
Relation
Applied Operations Research and Financial Modelling in Energy
Collections
Graduate School of Applied Mathematics, Book / Book chapter
Suggestions
OpenMETU
Core
Hydro Inflow Forecasting and Virtual Power Plant Pricing in the Turkish Electricity market
Çabuk, Sezer; Kestel, Sevtap Ayşe; Kalaycı, Erkan (2019-05-23)
Hydro inflow forecasting with most accurate quantitative models is a very crucial subject for effective hydro optimization, virtual power plant pricing, volume risk management and weather derivatives pricing in the Turkish electricity market. Predicting increase or decrease in hydro inflow, seasonal characteristics of hydrological years such as wet, dry or normal, allow the decision-makers to economically use water for optimal periods, quantify of volume risk and determine effective portfolio management str...
Hydro inflow forecasting and virtual power plant pricing in the Turkish electricity market
Çabuk, Sezer; Kestel, Sevtap Ayşe; Danışoğlu, Seza; Department of Financial Mathematics (2016)
Hydro inflow forecasting with most accurate quantitative models is a very crucial subject for effective hydro optimization, virtual power plant pricing, volume risk management and weather derivatives pricing in the Turkish electricity market. Predicting increase or decrease in hydro inflow, seasonal characteristics of hydrological years such as wet, dry or normal, allow the decision makers to economically use water for optimal periods, quantify of volume risk and determine effective portfolio management strat...
On the parametric and nonparametric prediction methods for electricity load forecasting
Erişen, Esra; İyigün, Cem; Department of Industrial Engineering (2013)
Accurate electricity load forecasting is of great importance in deregulated electricity markets. Market participants can reap significant financial benefits by improving their electricity load forecasts. Electricity load exhibits a complex time series structure with nonlinear relationships between the variables. Hence, new models with higher capabilities to capture such nonlinear relationships need to be developed and tested. In this thesis, we present a parametric and a nonparametric method for short-term ...
Wavelet Multivariate Adaptive Regression Splinesand Their Application to the UK Electricity Market
Yıldırım, Miray Hanım; Bayrak, Özlem Türker; Kestel, Sevtap Ayşe; G Wilhelm, Weber (null; 2015-05-16)
The growing effect of electricity prices on energy markets appeals for more accurate forecasting techniques since the market suffers from high volatility, high frequency, nonstationarity and multiple seasonality. Aiming at maximum utilities under highly-volatile conditions, both the supplier and the consumer sides seek to monitor and response to the ongoing changes of the electricity prices. In this study, we use a new hybrid approach, called Wavelet - Multivariate Adaptive Regression Splines (W MARS), to f...
Data-driven modeling using deep neural networks for power systems demand and locational marginal price forecasting
Jimu, Honest; Fahrioğlu, Murat; Electrical and Electronics Engineering (2022-9)
Forecasting electricity demand and locational marginal prices (LMPs) have become critical components for power system security and management. Electricity Demand Forecasting (EDF) aids the utility in maximizing the use of power-generation plants and scheduling them for both reliability and cost-effectiveness. In this thesis, a novel Deep Neural Network Long Short-Term Memory (DNN-LSTM) forecasting model is suggested to improve accuracy and robustness for predicting hourly day ahead power system load and LMP...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Çabuk, Ö. M. Mert, S. A. Kestel, and E. Kalaycı,
Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing
. 2021.