Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

Elseify, Mohamed A.; SeyyedAbbasi, Amir; Domínguez-García, José Luis; Kamel, Salah

doi:10.1016/j.est.2023.108986

Cited by 12 publications

(3 citation statements)

References 60 publications

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“…To assess the effectiveness of the algorithms, an aggregate of the outcomes derived from each iteration was computed, thereby providing a holistic measure of performance. The mean and standard deviation values were derived using Equations ( 37) and (38). Considering the varying initial conditions and iterations, a stability analysis was meticulously executed to scrutinize the consistency of the algorithmic performance.…”

Section: Discussionmentioning

confidence: 99%

“…37 Elseify et al, in their studies on the optimization of renewable energy sources, statistically evaluated the algorithms' performance and tried to determine which algorithm is the most effective. 37,38 Khamies et al statistically tested the evolution of renewable energy sources using energy storage systems and a secondary controller and achieved a 44% performance increase with the proposed strategic approach. 39 Mishra et al proposed a new non-dominated sorting multi-objective teaching learning-based optimization technique for the economic emission load distribution problem, and the results obtained were validated and supported by statistical tests.…”

Section: Literature Reviewmentioning

confidence: 99%

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Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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“…Subsequently, the expected power output per hour can be calculated. Recent studies, such as 40 , 41 , have utilized the beta distribution to model the intermittent characteristics of solar radiation, as demonstrated in Ref. 42 .…”

Section: Probabilistic Models Of Generation and Demandmentioning

confidence: 99%

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Elseify,

Hashim,

Hussien

et al. 2024

Sci Rep

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Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

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A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

Khalil,

Elkhodragy,

Salem

2024

Electr Eng

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Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

Cited by 12 publications

References 60 publications

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

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