Ramzi Kouadri

University Ferhat Abbas of Setif

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Linda Slimani

University Ferhat Abbas of Setif

Tarek Bouktir

University Ferhat Abbas of Setif

Imadeddine Benhabsa

University Ferhat Abbas of Setif

Souhil Mouassa

University of Bouira

Ismail Musirin

Universiti Teknologi MARA

Alessandro Bosisio

University of Pavia

Abdellatif Hamouda

University Ferhat Abbas of Setif

Tarek Bouktir

University Ferhat Abbas of Setif

KHEMMOUDJ Youcef Aymen

University Ferhat Abbas of Setif

Houssam Eddine Alouache

University Ferhat Abbas of Setif

Cooperative Institutions

University Ferhat Abbas of Setif

University of Pavia

Laboratory for Research on Enterprise and Decisions

University of Bouira

Hassiba Benbouali University of Chlef

Universidad de Jaén

Politecnico di Milano

Ricerca sul Sistema Energetico (Italy)

Izhevsk State Technical University

Larbi Ben M'hidi University of Oum El Bouaghi

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Improving power system performance by integrating TCSC and SVC devices in the presence of stochastic renewable energy generators

Wind Engineering (2025)

Imadeddine Benhabsa Ramzi Kouadri Linda Slimani Souhil Mouassa

This paper investigates the application of advanced metaheuristic algorithms Blood-Sucking Leech Optimizer (BSLO), Bonobo Optimizer (BO), and Electric Eel Foraging Optimization (EEFO) to solve the optimal power flow (OPF) problem with stochastic renewable energy generators (REGs), specifically photovoltaic power generators (PVGs) and wind power generators (WGs). Two scenarios are examined: Scenario 1 evaluates the proposed algorithms performance without Flexible AC Transmission Systems (FACTS), focusing on minimizing Total Generation Cost (TGC), Active Power Loss (APL), and a combined objective of TGC and Emissions (TGCE). The TGC including both thermal and REG costs, in which the cost related to stochastic PV and wind power generation encompasses direct, reserve, and penalty costs due to overestimation and underestimation of available PV and wind power. Scenario 2 introduces the Thyristor-Controlled Series Capacitor (TCSC) and the Static Var Compensator (SVC) to evaluate their impact on three objective functions. The performance of the algorithms is evaluated on a modified IEEE 30-bus system. The results show that the BSLO algorithm consistently achieves the best TGC, APL, and TGCE values at 781.1209 $/h, 1.9960 MW, and 810.7376 $/h, respectively. These outcomes highlight its effectiveness and competitive performance in the first scenario. The integration of FACTS devices in the second scenario results in a 6.73% reduction in APL with the insertion of TCSC, a 1.86% reduction with the insertion of SVC, and a 6.10% reduction with the insertion of both TCSC and SVC, compared to the APL value in the case without FACTS devices (1.9960 MW). The study comprehensively analyzes how different optimization techniques and FACTS devices enhance power system performance with stochastic REG integration.

10.1177/0309524x241307313

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Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

Wind Engineering (2024)

Ramzi Kouadri Souhil Mouassa Francisco Jurado

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.

Minification

10.1177/0309524x241253848

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Optimal Location of TCSC for Reactive Power Dispatching Using Kepler Optimization Algorithm

Imadeddine Benhabsa Ramzi Kouadri Linda Slimani

10.1109/iceeac61226.2024.10576342

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OPF for large scale power system using ant lion optimization: a case study of the Algerian electrical network

IAES International Journal of Artificial Intelligence (2020)

Ramzi Kouadri Ismail Musirin Linda Slimani Tarek Bouktir

<span lang="EN-US">This paper presents a study of the optimal power flow (OPF) for a large scale power system. A metaheuristic search method based on the Ant Lion Optimizer (ALO) algorithm is presented and has been confirmed in the real and larger scale Algerian 114-bus system for the OPF problem with and without static VAR compensator (SVC) devices. To get the highest impact of SVC devices in terms of improving the voltage profile, minimize the total generation cost and reduction of active power losses, the ALO algorithm was applied to determine the optimal allocation of SVC devices. The results obtained by the ALO method were compared with other methods in the literature such as DE, GA-ED-PS, QP, and MOALO, to see the efficiency of the proposed method. The proposed method has been tested on the Algerian 114-bus system with objective functions is the minimization of total generation cost (TGC) with two different vectors of variables control.</span>

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Static VAR compensator

Ant colony

10.11591/ijai.v9.i2.pp252-260

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Optimal Power Flow Solution for Wind Integrated Power in presence of VSC-HVDC Using Ant Lion Optimization

Indonesian Journal of Electrical Engineering and Computer Science (2018)

Ramzi Kouadri Linda Slimani Tarek Bouktir Ismail Musirin

<span>This paper studies the impact of incorporating wind power generation WPG on the power system on prsence of voltage source converter based high voltage DC (VSC-HVDC). A new meta-heuristic optimization technique are use for solving of the optimal power flow (OPF) problem, this technique optimization namely Ant Lion Optimizer (ALO). The optimization method is the Ant Lion Optimizer (ALO) method for resolve the optimal power flow (OPF) with incorporating of wind power generation on prsence of VSC-HVDC. And we used weibull distribution model of the wind farm. The ALO-OPF method has been examined and tested on standard test systems IEEE 30 bus with objective functions is minimization of cost total of production TPC are contain the sum of thermal and wind generation cost.</span>

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10.11591/ijeecs.v12.i2.pp625-633

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Optimal Placement of HVDC-VSC in AC System Using Self-Adaptive Bonobo Optimizer to Solve Optimal Power Flows: A Case Study of the Algerian Electrical Network

Electronics (2024)

Houssam Eddine Alouache Samir Sayah Alessandro Bosisio Abdellatif Hamouda Ramzi Kouadri

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy.

Bonobo

Electrical network

10.3390/electronics13193848

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A new Metaheuristic Method for Emission and Cost generation Reduction considering Stochastic Solar Power

Imadeddine Benhabsa Rahmani Alaa Eddine Ramzi Kouadri Linda Slimani

Cost reduction

10.1109/icaecot62402.2024.10829125

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Optimal power flow incorporating stochastic wind power using Artificial gorilla troops optimizer

2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) (2022)

Ramzi Kouadri Souhil Mouassa

This paper presents the application of a new nature-inspired metaheuristic technique to solve optimal power flow (OPF) problems considering stochastic wind power. This algorithm is called the Artificial gorilla troops optimizer (GTO) which is inspired by the behaviors of gorillas. The main objective of this work is to reduce the total cost of generation (TGC) according to the optimal scheduling of thermal and wind units, satisfying the equality and inequality constraints. The Weibull probability density (PDF) function will be included in the optimization problem to model the wind power output uncertainty. The function of wind power cost includes the direct cost, the penalty cost for underestimation and the reserve cost for overestimation of available wind power. To assess the performance of the GTO algorithm, the IEEE-30 bus system will be used incorporating wind farms. A comparative study suggests that GTO produces very competitive performance compared to the Artificial hummingbird algorithm (AHA).

10.1109/ssd54932.2022.9955921

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SLIME MOULD ALGORITHM FOR PRACTICAL OPTIMAL POWER FLOW SOLUTIONS INCORPORATING STOCHASTIC WIND POWER AND STATIC VAR COMPENSATOR DEVICE

Electrical Engineering & Electromechanics (2020)

Ramzi Kouadri Linda Slimani Tarek Bouktir

Purpose. This paper proposes the application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired by the swarming behavior and morphology of slime mould in nature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flow problems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reduction of active power losses as well as improving voltage profile. Methodology. The objective function of our problem is considered to be the minimum the total costs of conventional power generation and stochastic wind power generation with satisfying the power system constraints. The stochastic wind power function considers the penalty cost due to the underestimation and the reserve cost due to the overestimation of available wind power. In this work, the function of Weibull probability density is used to model and characterize the distributions of wind speed. Practical value. The proposed algorithm was examined on the IEEE-30 bus system and a large Algerian electrical test system with 114 buses. In the cases with the objective is to minimize the conventional power generation, the achieved results in both of the testing power systems showed that the slime mould algorithm performs better than other existing optimization techniques. Additionally, the achieved results with incorporating the wind power and static VAR compensator devices illustrate the effectiveness and performances of the proposed algorithm compared to the ant lion optimizer algorithm in terms of convergence to the global optimal solution.

Static VAR compensator

10.20998/2074-272x.2020.6.07

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Slime Mould Algorithm for Practical Optimal Power Flow Solutions Incorporating Stochastic Wind Power and Static VAR Compensator Device

SSRN Electronic Journal (2020)

Ramzi Kouadri Linda Slimani Tarek Bouktir

Static VAR compensator

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