Generation expansion planning considering unit commitment constraints and
            <scp>data‐driven</scp>
            robust optimization under uncertainties

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

doi:10.1002/2050-7038.12878

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…To model the uncertainties of renewable resources and load demand to some extent, representative days approach is deployed which different scenarios of normal monthly generation, one extreme scenario of monthly renewable actual generation, and one extreme scenario of monthly load demand are extracted using clustering approach, similar to other studies such as refs. [46], [47], [36], [43], [19], and [41]. To this end, to preserve the daily pattern of wind generation and load demand, 36 representative days for the target planning year are assumed.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Low‐carbon generation expansion planning considering flexibility requirements for hosting wind energy

Pourmoosavi

Amraee

2022

IET Generation Trans & Dist

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The operational flexibility of electric energy systems is one of the essential requirements for integrating a high share of renewable resources. The operational flexibility significantly impacts the mix of new power generation technologies. In this paper, a low‐carbon generation expansion planning (GEP) model is presented to investigate the impacts of flexibility requirements of power systems with a high share of wind energy. An improved clustered unit commitment (CUC) formulation is proposed to capture the flexibility limitation of thermal generating units fully. In this regard, clustered 10‐min ramp up/down limits for operational reserves, flexible‐ramp reserves, and contingency reserves, are introduced. The yearly variations of load and renewable generations preserving the chronological time correlations are included, considering 36 representative days obtained by the clustering approach. Besides, two types of BES devices are considered to investigate the role of BES in the provision of flexibility. The proposed flexible low‐carbon GEP model is formulated as a mixed‐integer programming model, and an optimal expansion plan is obtained using the CPLEX algorithm. By incorporating improved CUC formulation into the low‐carbon GEP model, more profound insight into power systems' flexibility requirement with high wind generation penetration is obtained.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In ref. [46] a three-stage robust flexible GEP model is proposed for the Egyptian power system to investigate the UC formulation under the short-term and long-term uncertainties of renewable and load. Also, as a flexibility tool, a battery energy storage system is utilised.…”

Section: Flexible Low-carbon Gepmentioning

confidence: 99%

Low‐carbon generation expansion planning considering flexibility requirements for hosting wind energy

Pourmoosavi

Amraee

2022

IET Generation Trans & Dist

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“…nother approach to managing uncertainty in renewable distributed generators is a combination of multiple uncertainties [8]. Also, The correlation of uncertainties is modeled by bounded intervals through a polyhedral uncertainty set in generation expansion planning [9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

El‐Ela

El‐Sehiemy

Shaheen

et al. 2023

J. Electr. Eng. Technol.

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As the wind speed is intermittent and unpredictable, statistical distribution approaches have been used to describe wind dates. The Weibull distribution with two parameters is thought to be the most accurate way for modeling wind data. This study seeks wind energy assessment via searching for optimal parameter estimation of the Weibull distribution. For this target, several analytical and heuristic methods are investigated. The analytical methods such as maximum likelihood method, moment method, energy pattern factor method (EPFM), and empirical method (EM) are used to find these optimal parameters. Also, these parameters are obtained by four heuristic optimization algorithms called particle swarm, crow search, aquila optimizer, and bald eagle search optimizers. The simulation results of analytical and heuristics are assessed together to identify the best probability density function (PDF) of wind data. In addition, these competitive models are submitted to find the most appropriate model to represent wind energy production. In all methods, the error between actual and estimated wind energy density is computed as the target fitness function. The simulation tests are carried out based on per year real data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, different indicators of fitness properties are assessed such as the root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and wind production deviation (WPD). The simulation results declare that the proposed bald eagle search optimization algorithm offers greater accuracy than other analytical and heuristic algorithms in estimating the Weibull parameters. Besides, statistical analysis of the compared methods demonstrates the high stability of the BES algorithm. Moreover, the BES algorithm presents the fastest convergence compared to the others. Furthermore, different models are analyzed to deduce the nonlinear relationship between the wind output power and the regarding speed where the error of wind energy density between actual and estimated is greatly minimized using the cubic model at least values of statistical indicators.

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“…In 19 , MILP with fuzzy objectives has been applied to create the expansion model of the primary heat source in the district heating systems, combined heat and power units. In 20 , the GEP problem was handled by MILP considering the impact of unit commitment and renewable sources but the forced outage rate of generation units didn’t taken into account at expected energy produced calculation. However, because of GEP is a complex problem with nonlinear constraints, the convergence of those classical methods to the optimal decision makes difficult to solve.…”

Section: Introductionmentioning

confidence: 99%

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Abou El Ela,

El-Sehiemy,

Shaheen

et al. 2023

Sci Rep

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Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

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

Cited by 10 publications

References 36 publications

Low‐carbon generation expansion planning considering flexibility requirements for hosting wind energy

Low‐carbon generation expansion planning considering flexibility requirements for hosting wind energy

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

Reliability constrained dynamic generation expansion planning using honey badger algorithm

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