Economic analysis of feed-in tariffs for generating electricity from renewable energy sources

Kooten, G. Cornelis van

doi:10.4337/9780857933690.00017

“…Therefore, RES based energy resources are in tandem with generally and has also been favored by eco-friendly organizations. Moreover, renewable energy resources generate energy with lower production prices and transmission cost [6], [7]. In the contemporary era, the sheer dependence of power grids over non-renewable sources is a recipe for environmental disaster and failure to bridge mushrooming energy demand-supply gap; therefore, power grids require lion's share of energy supply from RES.…”

Section: Introductionmentioning

confidence: 99%

Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network

Qayyum

¹

,

Jamil

²

,

Jamil

³

et al. 2022

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The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed challenging problems. This study overcomes essential issues overlooked by contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The study encompasses two stages: (1) predictive optimization model, which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan to trade energy efficiently by meeting the load requirements most of the time. The plan also decides optimal ESS charging and discharging operation to manage excess power efficiently. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. Outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

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“…The power wastages are reduced through SG and gap between the producer and consumers is also reduced. To improve the efficiency of SG, the Machine Learning (ML) algorithms are integrated into SG (van Kooten, 2013). The quality of power should be maintained using the technologies like dynamic voltage regulator in SG (Danalakshmi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Random forest based power sustainability and cost optimization in smart grid

Durairaj¹,

Wróblewski

²

,

Joseph³

et al. 2022

Production Engineering Archives

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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“…The power wastages are reduced through SG and gap between the producer and consumers is also reduced. To improve the efficiency of SG, the Machine Learning (ML) algorithms are integrated into SG [7]. The quality of power should be maintained using the technologies like dynamic voltage regulator in SG [8].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

Durairaj¹,

Wróblewski²,

Sheela³

et al. 2022

Preprint

1

0

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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Economic analysis of feed-in tariffs for generating electricity from renewable energy sources

Cited by 5 publications

References 11 publications

Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network

Predictive Optimization Based Energy Cost Minimization and Energy Sharing Mechanism for Peer-to-Peer Nanogrid Network

Random forest based power sustainability and cost optimization in smart grid

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

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