Inter national J our nal of A pplied P o wer Engineering (IJ APE) V ol. 15, No. 3, September 2026, pp. 1475 ∼ 1483 ISSN: 2252-8792, DOI: 10.11591/ijape.v15.i3.pp1475-1483 ❒ 1475 Impr o v ed micr ogrid ener gy coordination via h ybrid PSO and bidir ectional EV integration: perf ormance comparison against genetic algorithm Bilal Amghar , T ouk Azib, Khelil Sidi Brahim EST A CA LAB, EST A CA P aris-Saclay , Montign y-le-Bretonneux, France Article Inf o Article history: Recei v ed No v 5, 2025 Re vised Jul 4, 2026 Accepted Aug 15, 2026 K eyw ords: Bidirectional char ging Electric v ehicle inte gration Genetic algorithm Microgrid ener gy management P article sw arm optimization V ehicle-to-grid ABSTRA CT This paper presents a comparati v e study between h ybrid particle sw arm optimization (PSO) and genetic algorithm (GA) for ener gy management in residential microgrids equipped with photo v olt aic generation, stationary battery storage, and bidire ctional electric v ehicles (V2G). The system comprises 40 apartments, 1000 m² solar panels, 1 M Wh battery storage, and 15 electric v ehicles with V2G capability . A multi-objecti v e optimization frame w ork minimizes daily operational costs while satisfying mobility requirements, state-of-char ge constraints, and battery aging cons iderations. Simulation results demonstrate that h ybrid PSO signicantly outperforms GA, achie ving a net daily prot of 279 C (compared to 100 C cost for GA) through strate gic ener gy arbitrage and massi v e grid sales (2232 kWh/day vs 3.7 kWh/day for GA). The PSO-based approach achie v es 28% ener gy autonomy while generating substa ntial re v enue from feed-in tarif fs. The methodology pro vides a scalable frame w ork for real-w orld V2G-inte grated microgrids, with ongoing e xperimental v alidation at the EST A CA V2G testbed. This is an open access article under the CC BY -SA license . Corresponding A uthor: Bilal Amghar EST A CA LAB, EST A CA P aris-Saclay 12 Rue P aul Delouvrier , 78180 Montign y-le-Bretonneux, France Email: bilal.amghar@estaca.fr 1. INTR ODUCTION The global transition to w ard rene w able ener gy has accelerated o v er the past decade, with solar and wind no w accounting for nearly 30% of w orldwide generation [1]. Ho we v er , their intermittent nature poses challenges for residential grid stability . Rooftop PV systems often e xport 30–50% of midday generation due to limited storage, only to import during e v ening peaks [2], diminishing economic and en vironmental benets. Electric v ehicles (EVs) of fer a v ersat ile solution through their substantial battery capacities (40–100 kWh) and gro wing adoption of bidirectional v ehicl e-to-grid (V2G) technologies [3], [4]. EVs can absorb e xcess PV , pro vide demand response, and inject ener gy during peak periods. Early research focused on frequenc y re gulation [5], peak sha ving [4], and cost minimization using time-of-use tarif fs, b ut often ne glected mobility and battery health constraints [6]. Despite progress, four critical g aps remain. First, limited algorithm benchmarking: syste matic comparisons between algorithms (PSO, GA, DE, MILP) for V2G scheduling are scarce; most studies e v aluate a single algorithm without quantitati v e benchmarking. Second, simplied EV mobility: e xisting w orks often assume ideal a v ailability or use simplistic schedules [7], [8], underrepresented realistic trip distances and user J ournal homepage: http://ijape .iaescor e .com Evaluation Warning : The document was created with Spire.PDF for Python.
1476 ❒ ISSN: 2252-8792 constraints. Third, incomplete economic analysis: feed-in tarif fs and grid sale re v enues are often i gnored or underv alued, with most studies focusing on cost minimization rather than prot maximization. F ourth, limited scalability v alidation: studies typically v alidate on small systems (5-20 households) wi thout demonstrating scalability to lar ger communities [9], [10]. This paper addresses these g aps with four contrib utions: i) systematic algorithm comparison benchmarking h ybrid PSO ag ainst GA under identical conditions (40 apartments, 15 EVs, 1 MWh battery), pro viding quantitati v e metrics including con v er gence speed, solution quality , and rob ustness; ii) realistic EV a v ailability modeling with specic trip schedules, distance-based consumption (0.18 kWh/km), and state-of-char ge constraints ensuring mobility readiness; iii) comprehensi v e economic analysis incorporating realistic feed-in tarif fs (0.08-0.37 C/kWh) enabling protable grid sales and re v enue generation; and i v) scalability demonstration v alidated for 40 households with consistent performance from 10 to 80 households. Section 2 presents the system architecture and mathematical modeling. Section 3 details the h ybrid PSO and GA optimization frame w orks. Section 4 presents results including algorithm comparison, sensiti vity analysis, and statistical v alidation. Section 5 concludes with limitations and future w ork. 2. METHOD 2.1. System conguration The considered microgrid represents a residential community comprising 40 apartments equipped with 1000 m² solar panels, 1 MWh stationary battery capacity , and 15 bidirectional electric v ehicles. Figure 1 illustrates the complete system archite cture with po wer and communication o ws. Ov erall microgrid system architecture sho wing po wer o w directions (solid lines) and communication links (dashed lines) between smart char ging stations, consumers, rene w able sources, and the electrical grid. T able 1 summarizes the complete conguration. Figure 1. System architecture with po wer and communication o ws T able 1. Microgrid system conguration (40 apartments) Component P arameter V alue Component P arameter V alue PV system Area 1000 m² Electric v ehicles Number 15 v ehicles Ef cienc y 18% Capacity per v ehicle 60 kWh Peak irradiance 900 W/m² V2G po wer 11 kW/v ehicle Daily production ≈ 180 kWh T otal V2G capacity 165 kW / 900 kWh Stationary battery Capacity 1000 kWh SOC limits 20-95% Po wer rating 200 kW Ef cienc y 92% Ef cienc y 95% Building load Apartments 40 units SOC limits 10-95% Daily consumption ≈ 900 kWh Peak demand ≈ 80 kW Int J Appl Po wer Eng, V ol. 15, No. 3, September 2026: 1475–1483 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Appl Po wer Eng ISSN: 2252-8792 ❒ 1477 2.2. EV a v ailability modeling T able 2 presents the realistic EV a v ailability pattern used in this study . V ehicles are a v ailable for V2G services e xcept during three daily tra v el periods. The morning commute, lunch break, and e v ening trip represent periods when the EV is a w ay and therefore una v ailable for V2G operation. T able 2. EV a v ailability and tra v el schedule Period T ime Status Ener gy consumption Morning commute 8:00-10:00 A w ay 15 km (2.7 kWh) Lunch break 12:00-14:00 A w ay 12+8 km (3.6 kWh) Ev ening trip 17:00-18:00 A w ay 18 km (3.2 kWh) All other hours - A v ailable - 2.3. P o wer balance f ormulation The fundamental po wer equilibrium at each time interv al is as in (1). P grid ( t ) + P PV ( t ) + P dch bat ( t ) + P dch EV ( t ) = P load ( t ) + P transport ( t ) + P ch bat ( t ) + P ch EV ( t ) (1) Where P grid ( t ) is positi v e for import (purchase) and ne g ati v e for e xport (sale), P PV ( t ) is solar generation, P ch/dch bat and P ch/dch EV are battery and EV char ging/dischar ging po wers, P load ( t ) is b uilding demand, and P transport ( t ) is EV tra v el ener gy . 2.4. Solar generation model The photo v oltaic output follo ws a normalized solar prole, as (2). P PV ( t ) = A PV · η PV · G peak · I norm ( t ) (2) W ith A PV = 1000 m², η PV = 0 . 18 , G peak = 900 W/m², and I norm ( t ) the normalized irradiance prole peaking at solar noon. 2.5. Storage dynamics The state of char ge (SOC) progression for stationary battery , as in (3). SOC bat ( t + 1) = SOC bat ( t ) + η ch bat P ch bat ( t ) − P dch bat ( t ) C bat · ∆ t (3) F or EV eet (aggre g ated): SOC EV ( t + 1) = SOC EV ( t ) + η ch EV P ch EV ( t ) − P dch EV ( t ) N EV · C EV · ∆ t − E tra v el ( t ) N EV · C EV (4) Where E tra v el ( t ) is total tra v el ener gy at time t , N EV = 15 , C EV = 60 kWh. Operational constraints: SOC min bat ≤ SOC bat ( t ) ≤ SOC max bat (5) SOC min EV ≤ SOC EV ( t ) ≤ SOC max EV (6) 0 ≤ P ch bat ( t ) ≤ P max bat (7) 0 ≤ P dch bat ( t ) ≤ P max bat (8) 0 ≤ P ch EV ( t ) ≤ N EV · P max EV ,unit (if a v ailable) (9) 0 ≤ P dch EV ( t ) ≤ N EV · P max EV ,unit (if a v ailable) (10) Impr o ved micr o grid ener gy coor dination via hybrid PSO and bidir ectional EV inte gr ation: ... (Bilal Amghar) Evaluation Warning : The document was created with Spire.PDF for Python.
1478 ❒ ISSN: 2252-8792 2.6. Electricity pricing T ime-of-use purchase and feed-in tarif fs are presented in T able 3. The electricity prices v ary across dif ferent tim e periods, reecting the dynamic nature of electricity tarif fs throughout the day . The purchase price represents the cost of electricity from the grid, while the sell price indicates the re v enue from supplying electricity back to the grid. T able 3. Dynamic electricity pricing ( C/kWh) Hour Purchase price Sell price Incenti v e 0-5 0.10-0.12 0.08-0.10 Moderate 6-7 0.13-0.16 0.11-0.14 Moderate 8-10 0.18-0.22 0.16-0.20 High 11-14 0.25-0.30 0.22-0.27 High 15-17 0.20-0.25 0.17-0.22 Moderate 18-20 0.35-0.40 0.32-0.37 V ery high 21-23 0.25-0.18 0.22-0.15 Moderate 2.7. Optimization pr oblem f ormulation 2.7.1. Decision v ariables The decision v ector co v ers 24 hours with 5 v ariables per time step (120 total): x = [ P ch bat (1) , P dch bat (1) , P ch EV (1) , P dch EV (1) , P grid (1) , . . . , P grid (24)] (11) 2.7.2. Objecti v e function Minimize daily net cost: J = 24 X t =1 C grid ( t ) + C aging ( t ) + λ · Imbalance ( t ) 2 + ϕ · SOC penalty (12) where: C grid ( t ) = ( P grid ( t ) · π purchase ( t ) · ∆ t, P grid ( t ) > 0 P grid ( t ) · π sell ( t ) · ∆ t, P grid ( t ) ≤ 0 (13) C aging ( t ) = P ch bat ( t ) + P dch bat ( t ) · c bat · ∆ t + P ch EV ( t ) + P dch EV ( t ) · c EV · ∆ t (14) Imbalance ( t ) = P grid ( t ) − P load ( t ) + P transport ( t ) + P ch bat ( t ) + P ch EV ( t ) − P PV ( t ) − P dch bat ( t ) − P dch EV ( t ) (15) SOC penalty = max(0 , SOC tar get − SOC EV (24)) · κ (16) P arameter v alues: c bat = 0 . 005 C/kWh, c EV = 0 . 003 C/kWh, λ = 10 , ϕ = 50 , κ = 50 , SOC tar get = 0 . 8 . 2.8. Hybrid PSO algorithm description 2.8.1. Algorithm o v er view The h ybrid PSO combines global e xploration of particle sw arm optimization with local renement using gradient-based methods. The multi-objecti v e nature is addressed using the weighted sum approach, a widely used and v alidated technique in the literature [11], [12]. The algorithm proceeds as follo ws: – Step 1 - Initialization: Ge n e rate 100 particles with random positions x i within bounds and random v elocities v i . – Step 2 - Ev aluation: Ev aluate objecti v e function J ( x i ) for each particle. – Step 3 - Personal best update: If J ( x i ) < J ( p i ) , update personal best p i = x i . – Step 4 - Global best update: Find particle with minimum objecti v e: g = arg min i J ( p i ) . – Step 5 - V elocity update: v k +1 i = w v k i + c 1 r 1 ( p k i − x k i ) + c 2 r 2 ( g k − x k i ) (17) W ith inertia weight w = 0 . 729 , cogniti v e coef cient c 1 = 1 . 496 , social coef cient c 2 = 1 . 496 [13], [14]. Int J Appl Po wer Eng, V ol. 15, No. 3, September 2026: 1475–1483 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Appl Po wer Eng ISSN: 2252-8792 ❒ 1479 – Step 6 - Position update: x k +1 i = x k i + v k +1 i . – Step 7 - Constraint handling: Project infeasible positions to nearest feasible boundary . – Step 8 - T ermination check: Repeat Steps 2-7 until maximum iterations (200) or function tolerance (1e-6). – Step 9 - Local renement: Apply fmincon to global best solution g for gradient-based local optimization. This h ybrid approach, combining a heuristic with a deterministic opti mizer , is a common practice to impro v e solution quality [15], [16]. 2.8.2. Constraint handling strategy Inequality constraints are handled via penalty functions: J total = J ( x ) + ρ X j max(0 , g j ( x )) 2 (18) Where g j ( x ) are constraint violations and ρ = 10 4 is the penalty coef cient. 2.9. Genetic algorithm implementation F or comparison, we implement a standard GA with: i) population size: 100; ii) generations: 200; i ii) selection: tournament (size 3); i v) crosso v er: uniform (rate 0.8); v) mutation: Gaussian (rate 0.05); vi) elite count: 5. 3. RESUL TS AND DISCUSSION 3.1. Algorithm comparison: po wer o w analysis Figure 2 sho ws t he PSO-optimized po wer o ws sho wing PV generation, load consumption, battery and V2G po wer , and gri d e xchange. Positi v e grid v alues indicate purchas e, ne g ati v e indicate sale. Dri ving periods sho wn as shaded re gions. Figure 3 sho ws the GA-optimized po wer o ws. Note the signicant dif ference in grid sale v olume compared to PSO. The shaded re gions indicate EV tra v el periods when v ehicles are una v ailable for V2G services. – PSO per formance: The PSO-optimized schedule demonstrates aggressi v e yet strate gic grid sales during high-price periods (18:00-20:00), achie ving 2232.3 kWh/day sold. Battery and V2G dischar ge are coordinated to maximize re v enue while maintaining SOC constraints [17], [18]. – GA performance: In contrast, GA achie v es only 3.7 kWh/day sold, indicating f ailure to e xploit the prot opportunity from feed-in tarif fs [19]. T i m e ( h o u r s ) 0 5 1 0 1 5 2 0 P o w e r ( k W ) - 3 0 0 - 2 0 0 - 1 0 0 0 1 0 0 2 0 0 P P V L o a d B a t t e r y V 2 G G r i d B u y G r i d S e l l O u t o f G r i d Figure 2. PSO-optimized po wer o ws T i m e ( h o u r s ) 0 5 1 0 1 5 2 0 P o w e r ( k W ) - 2 0 0 - 1 0 0 0 1 0 0 2 0 0 3 0 0 P P V L o a d B a t t e r y V 2 G G r i d B u y G r i d S e l l O u t o f G r i d Figure 3. GA-optimized po wer o ws 3.2. State-of-char ge e v olution Figures 4 and 5 present SOC e v olution for stationary battery and EV eet. Figure 4 sho ws the battery c ycles between 10-95%, while EV SOC maintains mobility readiness ( > 20%) and reaches tar get 80% by day end. The both storage systems in Figure 5 sho w less strate gic c ycling, with EV SOC remaining hi gher (less V2G participation). Impr o ved micr o grid ener gy coor dination via hybrid PSO and bidir ectional EV inte gr ation: ... (Bilal Amghar) Evaluation Warning : The document was created with Spire.PDF for Python.
1480 ❒ ISSN: 2252-8792 T i m e ( h o u r s ) 0 5 1 0 1 5 2 0 S t a t e o f C h a r g e ( % ) 0 2 0 4 0 6 0 8 0 1 0 0 B a t t e r y E V F l e e t O u t o f G r i d Figure 4. PSO-optimized SOC e v olution T i m e ( h o u r s ) 0 5 1 0 1 5 2 0 S t a t e o f C h a r g e ( % ) 0 2 0 4 0 6 0 8 0 1 0 0 B a t t e r y E V F l e e t O u t o f G r i d Figure 5. GA-optimized SOC e v olution 3.3. Quantitati v e perf ormance comparison The results presented in T able 4 demonstrate the comparati v e performance of the h ybrid PSO and GA approaches in terms of economic performance, ener gy utilization, and computat ional ef cienc y . Hybrid PSO achie v es a lo wer net daily cost and f aster con v er gence than GA, while GA pro vides higher ener gy autonomy . Ov erall, h ybrid PSO of fers better economic performance and computational ef cienc y , whereas GA pro vides greater ener gy autonomy . 3.4. Statistical v alidation T able 5 presents results across 30 independent runs to assess rob ustness. Hybrid PSO achie v es a lo wer net cost and higher ener gy sales than GA. It also e xhibits shorter con v er gence time and a higher success rate compared with GA. These results conrm the superior rob ustness and consistenc y of h ybrid PSO. T able 4. Quantitati v e comparison: h ybrid PSO vs GA Metric Hybrid PSO GA Metric Hybrid PSO GA Net daily cost ( C/day) -279.14 100.07 Net grid balance ( C/day) -315.63 75.24 Ener gy sold (kWh/day) 2232.3 3.7 Ener gy autonomy (%) 28.0 67.9 Ener gy purchased (kWh/day) 739.2 329.0 Con v er gence time (s) 124 156 Re v enue from sales ( C/day) 486.11 1.30 Iterations to con v er ge 87 142 Purchase cost ( C/day) 170.49 76.54 T able 5. Statistical results (30 runs, mean ± std) Metric Hybrid PSO GA Net cost ( C/day) -278.9 ± 8.4 101.2 ± 15.7 Ener gy sold (kWh/day) 2231.8 ± 45.2 4.1 ± 2.3 Con v er gence time (s) 125.3 ± 12.1 158.4 ± 18.6 Success rate (feasible solutions) 100% 96.7% Int J Appl Po wer Eng, V ol. 15, No. 3, September 2026: 1475–1483 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Appl Po wer Eng ISSN: 2252-8792 ❒ 1481 3.5. Sensiti vity analysis The sensiti vity analysis sho wn as belo w: i) Impact of feed-in tarif fs: Reducing feed-in tarif fs by 20% decreases PSO re v enue to 389 C/day b ut still yields net prot (-189 C/day). GA remains unprotable (85 C/day) [20], [21]; ii) Impact of solar v ariability: Under ±15% solar irradiance uncertainty , PSO net cost ranges from -251 to -305 C/day , demonstrating rob ustness. GA remains positi v e (85-115 C/day) [22]; iii) System size scalability: Results for 10, 20, 40, and 80 households sho w consistent PSO adv antage: cost reduction of 65-72% compared to GA across all scales [23], [24]. 3.6. Discussion: wh y PSO outperf orms GA The superior performance of h ybrid PSO can be attrib uted to three f actors: i) memory-based search with personal and global best memories, ii) gradient local renement via fmincon, and iii) ef fecti v e constraint handling through v elocity clamping and boundary projection [25]. 4. CONCLUSION This paper presented a systematic comparison between h ybrid particle sw arm optimization and geneti c algorithm for V2G-enabled microgrid ener gy management applied to a 40-apartment residential system with 1000 m² of solar panels, 1 MWh of stationary battery storage, and 15 bidirectional electric v ehicles. The simulation results demonstrate that h ybrid PSO signicantly outperforms GA across all economic and technical metrics. Specically , the PSO-based approach achie v es a net daily prot of 279 C, whereas GA incurs a net daily cost of 100 C, representing a performance g ap of 379 C per day . This remarkable dif ference is primarily dri v en by PSO’ s ability to e xploit protable grid sales, generating 486 C per day in re v enue compared to only 1.30 C per day for GA. Be yond economic performance, PSO also demonstrates superior computational ef cienc y , con v er ging in 124 seconds (87 iterations) compared to 156 seconds (142 iterations) for GA, while maintaining 100% feasibility across 30 independent runs v ersus 96.7% for GA. Despite these promising results, se v eral limitations mus t be ackno wledged: i) the current fr ame w ork assumes perfect forecasts of PV generation and load demand, ii) battery de gradation is modeled using a li near ener gy-throughput approach, iii) EV a v ailability is modeled using a determ inistic schedule, i v) distrib ution-le v el grid impacts (v oltage de viations, transformer loading) are ignored, v) the optimization horizon is limited to 24 hours. Future w ork will address these limitations through stochastic optimization, nonlinear aging models, probabilistic mobility modeling, po wer o w inte gration, and multi-horizon planning. Ongoing e xperimental v alidation at the EST A CA V2G testbed will further v alidate the proposed frame w ork. A CKNO WLEDGMENTS The authors w ould lik e to thank EST A CA LAB for pro viding the research f acilities and the V2G testbed infrastructure used in this study . FUNDING INFORMA TION Authors state no funding in v olv ed. A UTHOR CONTRIB UTIONS ST A TEMENT This journal uses the Contrib utor Roles T axonomy (CRediT) to recognize indi vidual author contrib utions, reduce authorship disputes, and f acilitate collaboration. Name of A uthor C M So V a F o I R D O E V i Su P Fu Bilal Amghar ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ T ouk Azib ✓ ✓ ✓ ✓ ✓ ✓ ✓ Khelil Sidi Brahim ✓ ✓ ✓ ✓ ✓ ✓ ✓ C : C onceptualization I : I n v estig ation V i : V i sualization M : M ethodology R : R esources Su : Su pervision So : So ftw are D : D ata Curation P : P roject Administration V a : V a lidation O : Writing - O riginal Draft Fu : Fu nding Acquisition F o : F o rmal Analysis E : Writing - Re vie w & E diting Impr o ved micr o grid ener gy coor dination via hybrid PSO and bidir ectional EV inte gr ation: ... (Bilal Amghar) Evaluation Warning : The document was created with Spire.PDF for Python.
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Int J Appl Po wer Eng ISSN: 2252-8792 ❒ 1483 BIOGRAPHIES OF A UTHORS Bilal Amghar recei v ed his Ph.D. in Automatic Control, Electronics, and Electrical Engineering from ENSEA (Uni v ersity of Cer gy , France) i n 2013. He joined EST A CA in 2024 after six years at an ECAM group engineering school and v e years at ESTP . He w as responsible for se v eral scientic projects including BCAPP (Qua lied Business Check Application) and participated in academic and industrial research projects (Hydro+, Seiner gy Lab, and SMAR T GRID). He has also supervised se v eral P h.D. theses on ener gy mix optimization (microgrid, smartgrid, and embedded ener gy). He is currently an associate professor and researcher in the eld of ener gy (smart city , smart grids, and smart char ging) at EST A CA P aris-Saclay Campus, France. He can be contacted at email: bilal.amghar@estaca.fr . T ouk Azib recei v ed his Engineer de gree in Electrotechnics (Electrical Control option) from Ferhat Abbas Uni v ersity Setif (UF AS), Algeria (2001-2006). He recei v ed his M.Sc. in Electrical and Electronic Engineering (po wer electronics and control track) from ENSEM - INPL Nanc y , France (2006-2007). He earned his Ph.D. in Electrical Engineering from the Uni v ersity of P aris Sud XI (Laboratoire de G ´ enie Electrique de P aris - LGEP) in 2010, with the dissertation titled ”Contrib ution ` a l’Etude d’Electro-g ´ en ´ erateur ` a Pile ` a Comb ustible. Conception d’Architectures et de Leurs Commandes” (with highest honors). He obtained his Habilitation ` a Diriger des Recherches (HDR) from Uni v ersit ´ e P aris-Saclay (Doctoral S chool EOBE n°575) on the topic ”Cont rib ution ` a la conc eption optimale des syst ` emes ´ ener g ´ etiques embarqu ´ es: pr ´ e-dimensionnement et gestion d’ ´ ener gie. ” He is currently a professor at EST A CA P aris-Saclay and head of the EST A CA LAB ener gy group. His research focuses on po wer electronics, electric v ehicle char ging infrastructure, and smart grid inte gration. He can be contacted at email: touk.azib@estaca.fr . Khelil Sidi Brahim recei v ed his Ph.D. in Automatic Control from the Uni v ersity of Picardie Jules V erne, Amiens, France, in 2023. From 2022 to 2023, he w as a T emporary T eaching and Research Attach ´ e (A TER) at the LIS Laboratory and the Computer Science and I nteractions Department of Aix-Marseille Uni v ersity . From 2023 to June 2024, he held a contract teaching position in the Industrial Engineering and Maintenance Department of IUT Char tres. Since July 2024, he has been an associate profess or and researcher at EST A CA P aris-Saclay Campus. His current research focuses on the de v elopment of rob ust adapti v e control mechanisms, trajectory tracking, autonomous v ehicles, drones, and relat ed applications. He can be contacted at email: khelil.sidibrahim@estaca.fr . Impr o ved micr o grid ener gy coor dination via hybrid PSO and bidir ectional EV inte gr ation: ... (Bilal Amghar) Evaluation Warning : The document was created with Spire.PDF for Python.