A soft computing approach to projecting locational marginal price

Date

2013-05-01

Author

Nwulu, Nnamdi I.
Fahrioglu, Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

400
views

0
downloads

The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.

Subject Keywords

Locational marginal price, Artificial neural networks, Support vector machines, Back propagation learning algorithm, Radial basis function

URI

https://hdl.handle.net/11511/64998

Journal

NEURAL COMPUTING & APPLICATIONS

DOI

https://doi.org/10.1007/s00521-012-0875-8

Collections

Engineering, Article

Suggestions

OpenMETU
Core

Application of a Hybrid Machine Learning model on short term electricty demand prediction Assar, Ahmed Khaled Ahmed Farouk; Fahrioğlu, Murat; Sustainable Environment and Energy Systems (2022-2) Electricity demand forecasting is an important procedure in the electricity market and plays a great role in assuring a sustainable and efficient operation chain. By accurately forecasting the demand, one can see a considerable reduction in production costs as well as saving energy resources. Therefore, optimizing the demand forecasting techniques became an inseparable goal of power economics, leading to the introduction of machine learning to this sector that proved to be superior to other pre-defined alte...
Supplementing demand management programs with distributed generation options Fahrioglu, Murat; Alvarado, F. L.; Lasseter, R. H.; Yong, T. (2012-03-01) Ever increasing electrical energy demand is forcing power serving entities around the world to use various demand management programs to help in stressful times of the electric power grid. Demand management programs aim to control electrical energy demand among customers and create load relief for electric utilities. Recently demand management contracts have been designed in which incentives are offered to customers who willingly sign up for load interruption. In recent years much technological advancement ...
A Cost-Based Approach to Elicit Ancillary Service Tariffs from Unified Capacity and Energy Tariffs Sezer, Hikmet; Güven, Ali Nezih; Tör, Osman Bülent; Cebeci, Mahmut Erkut; Teimourzadeh, Saeed (2022-01-01) Establishing separated markets for capacity, energy, and ancillary services (AS) is one of the key steps to liberalize electricity sector and realize electricity markets. This study aims at eliciting AS tariffs from unified capacity and energy tariffs through a cost-based approach. In the proposed framework, maintaining annual revenue requirements of the power plants is considered as the main objective while separating the AS tariff from unified tariffs. The conducted study considers two types of unified ta...
An empirical evidence for generalized shrinkage methods: application of bagging in day-ahead electricity price forecasting and factor augmentation . Özen, Kadir; Yıldırım Kasap, Dilem; Department of Economics (2020) Fundamental dynamics behind electricity prices are multi-dimensional and elaborate. A popular approach to forecasting electricity price is to utilize large number of predictors. In this study, using the day-ahead electricity price data from commonly studied markets of five major series and GEFCom2014 data, a variant of shrinkage method, Bootstrap Aggregation (bagging) is proposed to incorporate information from available predictors. Bagging manifests itself as a computationally simpler alternative to common...
A NEW ALGORITHM FOR SCHEDULING CONDITION-BASED MAINTENANCE OF GAS TURBINES Yilmaz, Yavuz; Kurz, Rainer; Ozmen, Ayse; Weber, Gerhard Wilhelm (2015-06-19) In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Reg...

Citation Formats

N. I. Nwulu and M. Fahrioglu, “A soft computing approach to projecting locational marginal price,” NEURAL COMPUTING & APPLICATIONS, pp. 1115–1124, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64998.