Flexibility-based generation maintenance scheduling in presence of uncertain wind power plants forecasted by deep learning considering demand response programs portfolio

Sharifi, Vahid; Abdollahi, Amir; Rashidinejad, Masoud

doi:10.1016/j.ijepes.2022.108225

Cited by 23 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Operational efficiency is a key consideration for energy sector entities, including operators of renewable energy facilities. Accurate production estimations enable these organizations to plan operational activities effectively, including scheduled maintenance [19], production optimization, and human resource management [20]. The reduction in electricity costs is a significant benefit stemming from accurate production estimation.…”

Section: State Of the Artmentioning

confidence: 99%

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso,

Oliveri,

Brusca

et al. 2024

Energies

View full text Add to dashboard Cite

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

show abstract

Section: State Of the Artmentioning

confidence: 99%

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso,

Oliveri,

Brusca

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…By incentivizing consumers to adjust their electricity usage in response to frequency deviations, the demand can be dynamically managed to match the available generation capacity [25]. This active participation of consumers helps to stabilize the PIAT grid frequency [26], [27].…”

Section: Electrical System Operation (Eso)mentioning

confidence: 99%

VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

Tadjeddine,

Bendelhoum,

Bendjillali

et al. 2023

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

The Fluctuations in demand and weather conditions have a significant impact on the frequency and the voltage of Algeria's isolated PIAT power grid. To maintain stability and reliable power supply, it is crucial to keep these quantities close to their expected levels. An automatic (FRR) is employed to regulate real-time frequency deviations caused by integrating variable renewable energy (VRE), specifically wind and solar power in the Kabertene region. In order to mitigate wind power fluctuations, a power system stabilizer is implemented, which helps dampen oscillations. The use of Maximum Power Point Tracking (MPPT) techniques optimizes the extraction of power from solar panels under varying conditions. For efficient scheduling and dispatch of VRE generation, particle swarm optimization (PSO)-based algorithms are used. These algorithms ensure optimal utilization of renewable energy sources by considering their intermittent nature. This study proves the effectiveness of these techniques in enhancing grid stability, reducing frequency deviations, and improving VRE integration. Valuable insights are provided on their practical implementation, playing a crucial role in transitioning to a cleaner and more sustainable energy system.

show abstract

“…(26). The st of constraints (27) ensures that the scheduled reserve is higher than a specified threshold for all periods.…”

Section: Formulation Of Proposed Ufgms Idrrsmentioning

confidence: 99%

“…3 and the peak load of system is 2850 MW [26]. In this study, the eps and reserve criterion are considered 10 -6 and 20%, respectively [27]. The maintenance scheduling and flexibility evaluation horizons are assumed 52-week and 8736 hours, respectively.…”

Section: Numerical Analysismentioning

confidence: 99%

Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling

et al. 2022

Self Cite

View full text Add to dashboard Cite

Increasing emission concerns about greenhouse gases have led to an increasing tendency to use renewable energy sources (RERs) in the power system. Nevertheless, the probabilistic nature of RERs has led to an enhanced require to flexibility provision. Hence, it is necessary to implement a flexibility-based generation maintenance scheduling. For this purpose, it has used the flexibility index of the system in order to evaluate the flexibility of the power system. In flexibility studies, modeling and predicting the variability of renewable resources is important. In this paper, the uncertainties of wind are considered through forecasting by deep learning method in Python. Gas-fired power plants are one of the most important suppliers of flexibility in the supply-side. Therefore, the reliable operation of power system depends on the of natural gas availability. Furthermore, gas demand is subject to various uncertainties, especially in cold seasons, which will have significant effects on power system. in this paper, power-to-gas (P2G) technology as energy storages is modeled to mitigate the impact of wind output and gas demand uncertainty. Meanwhile, integrated natural gas and electricity demand response such as event-based and time-based model has been applied as a flexibility provision from demand-side point of view. In this paper, the objectives of reducing emission and costs, leveling the reserve margin and increasing flexibility are considered as the objectives of optimizing generation maintenance scheduling. In order to solve the multi-objective problem, the augmented Epsilon constraint method has been used. The proposed model has been implemented on a modified IEEE RTS 24 bus.

show abstract

Flexibility-based generation maintenance scheduling in presence of uncertain wind power plants forecasted by deep learning considering demand response programs portfolio

Cited by 23 publications

References 36 publications

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling

Contact Info

Product

Resources

About