scholarly journals Dead Battery? Wind Power, the Spot Market, and Hydropower Interaction in the Nordic Electricity Market

2013 ◽  
Vol 34 (1) ◽  
Author(s):  
Johannes Mauritzen
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Spot Market

scholarly journals Influence of Wind Power on Intraday Electricity Spot Market: A Comparative Study Based on Real Data

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2974 ◽  
Author(s):  
Pedro Frade ◽  
João Vieira-Costa ◽  
Gerardo Osório ◽  
João Santana ◽  
João Catalão
Keyword(s):  
Wind Power ◽  
Electricity Markets ◽  
Electricity Market ◽  
Consumer Satisfaction ◽  
Renewable Energy Sources ◽  
Spot Market ◽  
Electricity Sector ◽  
Market Condition ◽  
New Paradigm ◽  
Electricity Spot Market

Overtime, in the electricity sector, there has been a technological transfer to renewable electricity generation. With this change, processes, in the economic and availability terms, are expected to improve. In this new paradigm, society demands electricity without an impact on the environment and with the lowest possible cost. The wind power (WP) integration appears in this evolution process by achieving important technological advances, supporting in 2017 a growth of 44% of new projects in Europe, higher than any other renewable technology. However, the renewable energy sources (RES) integration in the electricity networks still presents technical difficulties and challenges, leading to challenges in the electricity markets (EMs). Therefore, this work evaluates the importance of WP and its influence on the Iberian Electricity Market (MIBEL), at the level of the intraday electricity spot market (IESM). This is an innovative study because literature usually focuses on day-ahead WP impact and this study focuses on intraday markets, which are closer to the consumption periods. The goal was to make an analysis on the impacts when betting on WP sources, in order to improve the market interaction with WP integration, considering as criteria the consumer satisfaction, in terms of lower electricity prices and WP availability. For this study, the market bids registered by the Iberian Electricity Market Operator (OMIE), from 2015 to 2017, ran over a new market simulator, specially developed for this proposal, considering a virtual market condition, but not considering the bids made by WP producers. The comparison of the results allowed the evaluation of the WP influence on the EM quantitative, which is noteworthy.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

Optimal operation of a hydrogen refuelling station combined with wind power in the electricity market

2016 ◽  
Vol 41 (46) ◽  
pp. 21057-21066 ◽  
Author(s):  
Stephen Carr ◽  
Fan Zhang ◽  
Feng Liu ◽  
Zhaolong Du ◽  
Jon Maddy
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Optimal Operation

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

scholarly journals Spot Market Mechanism Design for the Electricity Market in China Considering the Impact of a Contract Market

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1064 ◽  
Author(s):  
Chen Zhang ◽  
Wei Yan
Keyword(s):  
Electricity Market ◽  
Cost Minimization ◽  
Incentive Compatibility ◽  
Spot Market ◽  
Market Mechanism ◽  
Decomposition Algorithm ◽  
No Arbitrage ◽  
Arbitrage Opportunities ◽  
Arbitrage Opportunity ◽  
The Impact

To promote the reformation of the electricity market in China, a market mechanism that can support collaboration between the contract market and the upcoming spot market was designed in this paper. The focus of this paper was to develop a mechanism to institutionally stabilize the market by way of disciplining market power abuse through limiting arbitrage opportunities generated from multi-markets. To quantitatively describe the arbitrage opportunity, the arbitrage opportunity function (AOF) was defined. Based on inferences of the no-arbitrage principle and the AOF, a cost-based decomposition algorithm for contracts that could improve contract coverage was proposed. The incentive compatible settlement rule for the uncovered generation on the spot market was designed to properly manipulate the arbitrage opportunity. The decomposition algorithm and the settlement rule constituted the designed market mechanism. To verify the applicability and effectiveness of the proposed mechanism, the principles of incentive compatibility, individual rationality, and payment cost minimization were employed to test the designed market mechanism based on the concept of dominant policy equilibrium. This test was conducted on a fictitious case based on the IEEE-14 system. The analysis and results may provide valuable insights on market design in China based on the functional correlation between the contract market and the spot market.

Sign in / Sign up

Export Citation Format

Share Document