Generation Expansion Planning Considering High Share Renewable Energies Uncertainty

Abdalla, Omar H.; Adma, Maged A. Abu; Ahmed, Abdelmonem S.

doi:10.1109/mepcon47431.2019.9008180

Cited by 5 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But it has more steps to create output and consume more time which is not effective for expansion planning. However, the wind power uncertainty has been represented by annual variation of capacity factor based on historical data of wind location where the effect of wind power variation is analyzed at a certain probability of expectation [33]. Furthermore, both long and short-term wind power uncertainties were modeled within the GEP problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Abou El-Ela,

El-Sehiemy,

Shaheen

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Abou El-Ela,

El-Sehiemy,

Shaheen

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Generation expansion planning (GEP) is a complicated task aiming to determine the optimal generation technology mix. 1,2 Integrating high share renewable energy sources (RES) within the existing power systems becomes an essential requirement for mitigating climate changes, depletion of fossil fuels, and improving RES technologies. 3 However, the intermittency and strong stochastic nature of RES have introduced a significant impact of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Generation expansion planning considering unit commitment constraints and data‐driven robust optimization under uncertainties

Abdalla

Adma

Ahmed

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

This article presents a Generation Expansion Planning (GEP) methodology considering the impact of unit commitment constraints under uncertainties of both Renewable Energy Sources (RES) and forecasted load. Spatial and temporal data-driven robust optimization under the correlation of RES uncertainty is analyzed. As the intermittency nature of RES complicates dynamic characteristics of the net load profile and increases the need for operational flexibility, a robust GEP model is proposed considering the unit commitment constraints and data-driven robust optimization in addition to the correlation among different RES uncertainties. Long-and short-term uncertainty is represented and incorporated into the proposed GEP model. The GEP is solved through three stages. In the first stage, the GEP model focuses on the RES generation planning considering the long-term uncertainties. The impact of unit commitment constraints under short-term uncertainty is considered in the second stage. An appropriate Energy Storage System (ESS) is studied in the third stage. The results have demonstrated that: (a) considering the data-driven robust optimization under correlation of RES uncertainty reduces the conservativeness and

show abstract

“…The results of GEP are beneficial in determining the type, capacity, and the availability of new plants to be in service over a planning horizon. Furthermore, GEP being studied to meet the forecasted electricity demand with an appropriate degree of reliability to achieve the generation adequacy 1,2 . The robust GEP model should take into account the impact of the inherent uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…A peaking reserve is used to tackle the uncertainty of wind power 32 . Reference 2 described the RES implementation within the GEP model as thermal power plants with zero fuel cost and a high forced outage rate (FOR). The RES uncertainties are represented through the variation of the renewable output energy and incorporated into the GEP model through a FOR to reduce the RES capacity credit.…”

Section: Introductionmentioning

confidence: 99%

“…The correlated uncertainties are incorporated into the planning model through a devolved correlated polyhedral uncertainty set. References 2,33 proposed a reserve margin as an appropriate solution to cope with the uncertainty problem. The previously mentioned efforts 2,29‐33 proposed only the reserve margin solution to cope with the uncertainty problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust generation expansion planning considering high penetration renewable energies uncertainty

Abdalla

Adma

Ahmed

2020

Engineering Reports

Self Cite

View full text Add to dashboard Cite

The proper optimal generation expansion planning (GEP) should meet the reliability criteria requirements over a planning horizon under the presence of uncertainties. The intermittent nature of renewable energy sources (RES) introduces an enormous uncertainties impact within the planning model. A simulation model for RES uncertainty is developed using the capacity factor (CF) of the RES historical data. The RES simulation model is handled via the probability density function (PDF). The uncertainty parameter of different RES is described as a flexible polyhedral uncertainty set and incorporated within the proposed GEP model. The influence of different uncertainty scenarios for each RES uncertainty on the GEP model can be analyzed separately. The RES uncertainty scenarios are predefined and incorporated within the proposed GEP model through a proposed parameter named as a confidence level. The proposed confidence level parameter is beneficial to the power system planner to control the degree of robustness. Different GEP results are presented for various RES uncertainty scenarios. Three methods are proposed as appropriate solutions to deal with the RES uncertainty impact. The most economical method among the three proposed methods is determined by developing an objective function tailored to achieve the optimality of the economic factor.

show abstract

Generation Expansion Planning Considering High Share Renewable Energies Uncertainty

Cited by 5 publications

References 19 publications

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Generation expansion planning considering unit commitment constraints and data‐driven robust optimization under uncertainties

Robust generation expansion planning considering high penetration renewable energies uncertainty

Contact Info

Product

Resources

About