Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks

2006 ◽  
pp. 56-66 ◽  
Author(s):  
Pavlos S. Georgilakis
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Price Forecasting ◽  
Market Clearing ◽  
Market Clearing Price

scholarly journals A Two-Sided Price-Decoupled Pay-As-Bid Auction Approach for the Clearing of Day-Ahead Electricity Markets

2020 ◽  
Vol 162 ◽  
pp. 01006
Author(s):  
Dávid Csercsik
Keyword(s):  
Objective Function ◽  
Electricity Markets ◽  
Supply And Demand ◽  
Conventional Approach ◽  
Small Scale ◽  
Power Balance ◽  
Market Clearing ◽  
Multiunit Auctions ◽  
Market Clearing Price ◽  
Characteristic Features

In this paper we propose a possible alternative for conventional pay-as-clear type multiunit auctions commonly used for the clearing of day-ahead power exchanges, and analyse some of its characteristic features in comparison with conventional clearing. In the proposed framework, instead of the concept of the uniform market clearing price, we introduce limit prices separately for supply and demand bids, and in addition to the power balance constraint, we formulate constraints for the income balance of the market. The total traded quantity is used as the objective function of the formulation. The concept is demonstrated on a simple example and is compared to the conventional approach in small-scale market simulations.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

Fuzzy Neural Network Market Clearing Power Price Forecasting Based on K-Means Clustering

2011 ◽  
Vol 121-126 ◽  
pp. 2035-2039 ◽  
Author(s):  
Xian Min Wei
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Complex Structure ◽  
Price Forecasting ◽  
Market Clearing ◽  
Market Clearing Price ◽  
Fuzzy Neural ◽  
Long Time

Normalized fuzzy neural network has complex structure, long-time study and other shortcomings. For these shortcomings, this paper applies an improved fuzzy neural network to predict market clearing price. The model is simple, just by k-means clustering to determine the number of fuzzy inference layer nodes, and with strong applicability, higher prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document