Promotion of wind generated electricity using price responsive Demand Side Management: Price prediction analysis for imperfect energy storage

Finn, P.; Fitzpatrick, Colin; Leahy, Martin J.; Relihan, L.

doi:10.1109/issst.2010.5507747

Cited by 10 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To facilitate this, price based Demand Side Management (DSM) is considered. The authors of [9] observed a strong correlation between financial savings, wind energy demand, and conventional energy demand, with price based DSM consistently promoting installed RE sources.…”

Section: Contributions and Related Workmentioning

confidence: 99%

Predicting Renewable Energy Investment Using Machine Learning

Hosein

Bahadoorsingh

et al. 2020

Energies

View full text Add to dashboard Cite

In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from reduced fossil fuel dependence and the fluctuations associated with imported fuel prices. However, numerous countries have not yet made preparations to increase RE production and integration. In many instances, this reluctance seems to be predominant in energy-rich countries, which typically provide heavy subsidies on electricity prices. With such subsidies, there is no incentive to invest in RE since the time taken to recoup such investments would be significant. We develop a model using a Neural Network (NN) regression algorithm to quantitatively illustrate this conjecture and also use it to predict the reduction in electricity price subsidies required to achieve a specified RE production target. The model was trained using 10 leading metrics from 53 countries. It is envisaged that policymakers and researchers can use this model to plan future RE targets to satisfy the Nationally Determined Contributions (NDC) and determine the required electricity subsidy reductions. The model can easily be modified to predict what changes in other country factors can be made to stimulate growth in RE production. We illustrate this approach with a sample use case.

show abstract

Section: Contributions and Related Workmentioning

confidence: 99%

Predicting Renewable Energy Investment Using Machine Learning

Hosein

Bahadoorsingh

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…The BESS cannot charge or discharge within the same time interval and this is achieved through the constraints ( 9) and (10). Also, the charging and discharging power in MW must be less than the maximum charging ( ℎ ) and discharging (…”

Section: ) Battery Storage Restrictionmentioning

confidence: 99%

“…The provision of flexibility through demand response management is examined in [9], where downward flexibility is provided by converting wind power to gas. In [10], the flexibility is provided by demand response when economic incentives are applied. Furthermore, the flexibility provided through the ESSs receives increasing attention lately.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Power System Flexibility and Operating Cost Reduction Using a BESS

Papayiannis

Asprou

Tziovani

et al. 2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

View full text Add to dashboard Cite

Power system operators are continuously striving to achieve more economical, environmental friendly, and stable operation of the system. The integration of Renewable Energy Sources (RES) can benefit the system in terms of cost and environmental friendliness but at the same time it poses challenges related to system stability. One way for achieving an increased penetration of RES while maintaining the system integrity is the installation of Battery Energy Storage Systems (BESS). In this paper, the effect of a large scale BESS on the power system operating condition is investigated. A MILP-based unit commitment algorithm is used for obtaining the optimal generation scheduling in the system when a BESS is employed and the results are used for evaluating the improvement in several indices such as system operating cost, CO2 emissions, and system flexibility. Through a case study, the positive impact of the deployment of a BESS on a power system is verified, indicating that the aforementioned indices are improved in the presence of a BESS.

show abstract

“…It has been already adopted by some countries, in particular Denmark and Ireland [25,26] while other countries will introduce it soon. In this paper, a hourly spot market price, available one day in advance, has been assumed as the real-time electricity price applied to consumers.…”

Section: B Proposed Ems Policymentioning

confidence: 99%

Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

Siano

Cecati

et al. 2012

IEEE Trans. Ind. Inf.

198

View full text Add to dashboard Cite

This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.

show abstract

Promotion of wind generated electricity using price responsive Demand Side Management: Price prediction analysis for imperfect energy storage

Cited by 10 publications

References 4 publications

Predicting Renewable Energy Investment Using Machine Learning

Predicting Renewable Energy Investment Using Machine Learning

Enhancement of Power System Flexibility and Operating Cost Reduction Using a BESS

Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

Contact Info

Product

Resources

About