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09/05/2026

MULTI-OBJECTIVE OPTIMAL POWER FLOW USING SCUBA DIVER OPTIMIZATION ALGORITHM FOR ENHANCED ECONOMIC AND VOLTAGE PERFORMANCE: A CASE STUDY ON THE IEEE 30-BUS SYSTEM

DESIGN DETAILS
Optimal Power Flow (OPF) plays a critical role in ensuring the secure, economical, and reliable operation of modern power systems under complex operational constraints. This Matlab design presents an efficient OPF solution based on the Scuba Diver Optimization Algorithm (SDOA) integrated with effective constraint-handling techniques. The proposed framework incorporates boundary correction, penalty functions, and feasibility restoration strategies to strictly satisfy system constraints, including generator limits, voltage magnitude bounds, transformer tap settings, and reactive power compensation.

The SDOA demonstrates strong optimization capability through its adaptive search mechanism inspired by diver behavior, where oxygen-level-driven depth transitions effectively balance global exploration and local exploitation. This dynamic search strategy enhances the algorithm’s ability to avoid premature convergence and efficiently handle highly nonlinear and non-convex OPF problems. Moreover, the inclusion of adaptive communication and reset mechanisms improves population diversity and convergence reliability under varying operating conditions.

The developed model optimally regulates key control variables while satisfying nonlinear AC power flow equations and network security constraints. The proposed approach is validated on the standard IEEE 30-bus test system under multiple objective scenarios, including fuel cost minimization, emission reduction, voltage profile enhancement, and active power loss minimization.

Simulation results demonstrate that the proposed SDOA-based OPF method achieves high-quality feasible solutions with improved convergence speed and robustness compared to conventional optimization techniques. Significant improvements are observed in operational cost reduction, voltage stability enhancement, and overall system performance. These findings confirm that the proposed approach is a reliable, flexible, and computationally efficient tool for solving complex OPF problems in modern power systems.
Case 1: Minimization of fuel cost
Case 2: Minimization of cost considering multi-fuels
Case 3: Enhancement of voltage stability of the network
Case 4: Minimization of emission
Case 5: Minimization of real power loss
Case 6: Minimization of fuel cost considering valve point effect
Case 7: Minimization of fuel cost and real power loss
Case 8: Minimization of fuel cost and voltage deviation
Case 9: Minimization of fuel cost and enhancement of voltage stability
Case 10: Minimization of fuel cost, emission, voltage deviation and losses
Case 11: Minimization of voltage deviation

REFERENCES
Reference Paper-1: Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques
Author’s Name: Partha P. Biswas, P.N. Suganthan, R. Mallipeddi and, Gehan A.J. Amaratunga
Source: Elsevier
Year:2017

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08/05/2026

QUADRATIC INTERPOLATION OPTIMIZATION–BASED PLANNING OF RENEWABLE DISTRIBUTED GENERATION IN SMART DISTRIBUTION NETWORKS USING DUAL LOAD FLOW METHODS

DESIGN DETAILS
The increasing integration of renewable Distributed Generation (DG) sources such as solar photovoltaic (PV) and wind turbine (WT) systems in modern distribution networks requires effective tools for planning and operation. The presence of multiple DG units creates complex nonlinear interactions, making accurate load flow analysis and efficient optimization essential for reliable system performance. To address this, this study proposes an integrated framework that combines two load flow methods—the Current Injection Method (CIM) and the Single-Matrix Load Flow (SMLF)—with Quadratic Interpolation Optimization (QIO) for optimal placement and sizing of DG units.

The CIM method is used due to its high accuracy and strong convergence in radial and weakly meshed networks with high DG pe*******on. It models power injections using current components, improving stability under heavily loaded conditions. The SMLF method is employed as a faster alternative, using a single matrix formulation to reduce computational complexity, making it suitable for repeated load flow calculations during optimization.

For DG allocation, the Quadratic Interpolation Optimization (QIO) algorithm is applied. QIO uses three candidate solutions to construct a quadratic model and estimate the optimal solution. This approach improves convergence speed and accuracy compared to purely random methods. It also balances global exploration and local exploitation, allowing efficient search of the solution space.

The main objective is to minimize real power loss while improving voltage stability and maintaining voltage limits. The optimization variables include the locations and sizes of PV and WT units. System constraints such as DG capacity and voltage limits are considered to ensure practical feasibility.
A 24-hour analysis is carried out using varying solar irradiance, wind speed, and load demand to evaluate system performance under different operating conditions. Proper DG placement reduces power losses and improves voltage profiles, while poor placement may lead to issues such as reverse power flow and increased losses.

The proposed CIM–SMLF–QIO framework is implemented in MATLAB and tested on the IEEE 30-bus system. Results show significant reduction in power loss and improvement in voltage profile. The comparison between CIM and SMLF confirms the accuracy and efficiency of the approach. Overall, the QIO-based method provides an effective and scalable solution for renewable-integrated distribution system planning.

OBJECTIVE FUNCTION
Power Loss
AVDI
VSI

SIMULATION RESULTS VALUES IN MATLAB COMMAND WINDOW
1. Power Loss
2. VSI
3. AVDI
4. Optimal PV and WT location
5. Optimal PV and WT Size
6. Minimum and Maximum Voltage @ Bus
7. Ex*****on Time

SIMULATION FIGURES
1. Voltage Profile
2. Branch Current
3. Power Loss
4. Convergence Graph

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07/05/2026

A STUDENT PSYCHOLOGY BASED OPTIMIZATION APPROACH FOR MULTI-DG ALLOCATION TO MINIMIZE LOSSES AND IMPROVE VOLTAGE STABILITY IN THE 24-BUS BISHOFTU (ETHIOPIA) RADIAL NETWORK

DESIGN DETAILS
The optimal placement and sizing of distributed generators (DGs) are critical for improving the operational efficiency of radial distribution systems through the reduction of real power losses and enhancement of voltage profiles. This study proposes a MATLAB-based optimization framework for the optimal allocation of multiple DG units in a 24-bus Bishoftu (Ethiopia) radial distribution feeder using the Student Psychology Based Optimization (SPBO) algorithm.

SPBO is a nature-inspired metaheuristic that models the adaptive learning behaviors of students, incorporating mechanisms such as competitive learning, guided improvement, and self-learning to achieve an effective balance between exploration and exploitation. The algorithm categorizes candidate solutions into different learning groups best, good, and average students—thereby enabling dynamic search strategies that enhance convergence performance and solution quality.

The optimization objective is to minimize real power losses while simultaneously improving key performance indices, including the Average Voltage Deviation Index (AVDI) and Voltage Stability Index (VSI). The problem is solved under practical operational constraints such as bus voltage limits, DG capacity bounds, and system power balance requirements. The network performance corresponding to each candidate solution is evaluated using the backward–forward sweep load flow method.

The effectiveness of the proposed SPBO-based framework is validated by varying the number of DG units from one to ten. Its performance is assessed in terms of convergence behavior, loss minimization capability, and voltage profile enhancement. Simulation results demonstrate that the SPBO algorithm consistently provides superior optimization performance, achieving significant reductions in real power losses, faster convergence rates, and improved voltage stability compared to conventional and benchmark optimization techniques.

REFERENCES
Reference Paper-1: Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms
Author’s Name: Venkata K. Babu Ponnam and K. Swarnasri
Source: ETASR
Year: 2020

Reference Paper-2: Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm
Author’s Name: D.B. Prakasha and C. Lakshminarayanab
Source: Elsevier
Year: 2016

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06/05/2026

A NOVEL WILD HORSE OPTIMIZER–BASED STRATEGY FOR EMISSION-CONSTRAINED ECONOMIC DISPATCH CONSIDERING RENEWABLE UNCERTAINTY IN ISOLATED MICROGRIDS

DESIGN DETAILS
Modern power systems are facing unprecedented challenges due to the continuous rise in electricity demand, rapid depletion of fossil-fuel reserves, and increasing global pressure to reduce greenhouse gas emissions. In this context, microgrids (MGs) localized clusters integrating distributed energy resources (DERs) such as wind turbines, photovoltaic (PV) systems, diesel generators, and combined heat and power (CHP) units have emerged as an efficient and sustainable solution. Microgrids enhance system reliability, reduce transmission losses, and facilitate the seamless integration of renewable energy sources.

A critical operational problem in microgrids is the Combined Economic Emission Dispatch (CEED), which aims to determine the optimal allocation of generation units such that both total operating cost and environmental emissions are minimized while meeting system load demand and satisfying all unit constraints. The complexity of this problem intensifies in isolated microgrids, where the absence of grid support necessitates optimal internal scheduling for secure and economical operation.
However, solving CEED becomes increasingly challenging under high renewable pe*******on, nonlinear generator characteristics, stochastic variations in demand, and stringent emission constraints. These factors result in a highly nonlinear, non-convex, and multimodal optimization landscape, limiting the effectiveness of conventional deterministic and mathematical programming approaches.

To address these challenges, this study proposes a novel solution to the Emission-Constrained Economic Dispatch (ECED) problem using the Wild Horse Optimizer (WHO), a nature-inspired metaheuristic algorithm modeled on the social hierarchy and herd behavior of wild horses. The proposed WHO-ECED framework efficiently minimizes total generation cost and emission levels while satisfying operational constraints of diesel generators, photovoltaic systems, wind turbines, and energy storage units. WHO employs a unique mechanism involving stallion leadership, group-based exploration, adaptive grazing behavior, and dynamic exploitation strategies, ensuring an effective balance between global exploration and local exploitation.

Simulation results on an isolated microgrid test system demonstrate that the proposed WHO approach achieves faster convergence, reduced operational cost, and lower emission levels compared with conventional optimization techniques. The results validate the robustness, stability, and computational efficiency of WHO under varying load demands and renewable generation uncertainties, establishing it as a promising tool for real-time energy management in modern microgrids.

REFERENCES
Reference Paper-1: An Emission Constraint Environment Dispatch Problem Solution with Microgrid using Whale Optimization Algorithm
Author’s Name: Indrajit N. Trivedi and Pradeep Jangir
Source: IEEE
Year: 2016

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05/05/2026

OSPREY OPTIMIZATION ALGORITHM–BASED MULTI-OBJECTIVE FRAMEWORK FOR FACTS-ASSISTED OPTIMAL POWER FLOW IN MODERN POWER SYSTEMS

DESIGN DETAILS
Enhancing the security, reliability, and operational efficiency of modern power systems necessitates advanced optimization techniques capable of addressing nonlinear characteristics, operational uncertainties, and multi-objective trade-offs inherent in Optimal Power Flow (OPF) problems. This study proposes an Osprey Optimization Algorithm (OOA) based framework for the optimal placement and parameter tuning of Flexible AC Transmission System (FACTS) devices, aimed at improving voltage stability, minimizing real power losses, and reducing overall system operating costs. The proposed OOA-FACTS methodology integrates a Newton–Raphson–based power flow analysis and evaluates system performance using multiple objective indices, including active power loss, voltage deviation, investment cost, fuel cost, and severity index, in accordance with contemporary FACTS-oriented OPF formulations.

The OOA employs a biologically inspired hunting mechanism that effectively balances global exploration and local exploitation through adaptive position updating strategies, enabling robust navigation of the highly nonlinear and constrained FACTS optimization landscape. Unlike conventional metaheuristic approaches, the algorithm demonstrates enhanced convergence behavior and reduced susceptibility to local optima.

The proposed framework is implemented in MATLAB and validated on the IEEE 30-bus test system with STATCOM compensation under various operating scenarios, demonstrating its effectiveness in achieving superior system performance and optimization accuracy.

1. Normal (base-case) operation
2. Contingency conditions
o Generator outage
o Line outage
o System overloading
3. FACTS device placement scenario

Simulation results confirm that the proposed OOA-FACTS strategy achieves substantial reductions in real power losses, enhanced voltage regulation across buses, and lower fuel costs relative to the uncompensated network. Moreover, the OOA algorithm demonstrates strong convergence robustness and high solution consistency, attributed to its dynamic fragrance-based search mechanism.

REFERENCES
Reference Paper-1: Power system security enhancement in FACTS devices based on Yin–Yang pair optimization algorithm
Author’s Name: A. Amarendra, L. Ravi Srinivas and R. Srinivasa Rao
Source: Springer
Year: 2022

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04/05/2026

WAR STRATEGY OPTIMIZATION ALGORITHM–BASED INTEGRATED FRAMEWORK FOR NETWORK RECONFIGURATION AND OPTIMAL ALLOCATION OF DG AND D-STATCOM IN RADIAL DISTRIBUTION SYSTEMS

DESIGN DETAILS
The increasing stress on modern distribution systems, driven by rising load demand and the growing pe*******on of distributed energy resources, necessitates advanced network management strategies to enhance system reliability, minimize operating losses, and improve voltage quality. This design presents a MATLAB-based optimization framework employing the War Strategy Optimization Algorithm (WSO) for the optimal allocation and sizing of Distributed Generators (DGs) and Distribution Static Compensators (D-STATCOMs) in a reconfigured radial distribution network.

The proposed WSO is a population-based metaheuristic inspired by strategic war dynamics, incorporating dual-leader guidance (King and Commander), adaptive learning mechanisms, and dynamic exploration–exploitation balance to effectively navigate complex, nonlinear, and multimodal search spaces.

The developed framework simultaneously determines the optimal network reconfiguration (switching states), DG placement and sizing, and D-STATCOM location and capacity with the objective of minimizing total real power losses while satisfying operational constraints and maintaining radial topology. Load flow analysis is performed using a reconfiguration-compatible backward/forward sweep method, and candidate solutions are validated through robust constraint-handling procedures.

Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed WSO-based approach achieves significant reduction in power losses, enhanced voltage profiles, and improved convergence characteristics. The coordinated optimization of network reconfiguration, DG integration, and reactive power compensation using D-STATCOM is shown to be highly effective.

The results confirm that the War Strategy Optimization Algorithm is a robust, efficient, and promising tool for advanced planning and operational optimization of modern smart distribution systems.

The objective function, F(k)=min∑_(i=1)^br▒〖R_i*I_i^2 〗

REFERENCES
Reference Paper-1: Flower Pollination Algorithm based Sizing and Placement of DG and D-STATCOM simultaneously in RDS system under reconfigured network.
Author’s Name: Vittal Bhat. M and Manjappa.N
Source: IEEE
Year:2018

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03/05/2026

A POINT ESTIMATE METHOD BASED STOCHASTIC OPTIMIZATION APPROACH USING LIGHT SPECTRUM OPTIMIZER FOR PV–WIND–BATTERY–EV INTEGRATED MICROGRIDS

DESIGN DETAILS
The rapid integration of renewable energy resources and electric vehicles (EVs) has significantly increased uncertainty and operational complexity in modern microgrids and smart residential energy systems. To effectively address these challenges, this paper proposes a MATLAB-based advanced energy management framework that integrates photovoltaic (PV) systems, wind turbines (WTs), battery energy storage systems (BESSs), and EV charging infrastructures under stochastic operating conditions. The uncertainties associated with renewable generation, load demand, and electricity market prices are accurately modeled using the Point Estimate Method (PEM), which offers a reliable probabilistic representation with reduced computational complexity.

The energy management problem is formulated as a constrained nonlinear optimization model aimed at minimizing the total operational cost, including generation, emission, and reliability costs, while simultaneously maximizing the profit from energy trading with the utility grid. The proposed model incorporates essential operational constraints, such as power balance, distributed energy resource (DER) limits, battery and EV state-of-charge (SoC) constraints, and grid import/export limits. Owing to the highly nonlinear and nonconvex nature of the problem, a Light Spectrum Optimizer (LSO) is employed to obtain the optimal scheduling strategy. The LSO algorithm, inspired by the physical principles of light refraction, reflection, and spectral dispersion, employs optical propagation-based search dynamics, adaptive stochastic perturbations, and multi-stage scattering mechanisms to effectively balance global exploration and local exploitation, thereby ensuring robust convergence and enhanced solution quality while mitigating premature convergence.

Simulation results demonstrate that the proposed PEM-based stochastic framework integrated with LSO significantly enhances the operational performance of the microgrid. The coordinated scheduling of EVs and distributed energy resources enables cost-effective operation, improved system reliability, and efficient utilization of renewable energy resources. The findings confirm that the proposed approach offers a scalable, computationally efficient, and practically viable solution for next-generation smart grid and smart home energy management systems.

REFERENCES:
Reference Paper-1: Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System with Integration of Electric Vehicles
Author’s Name: Melhem FY, Grunder O, Hammoudan Z, Moubayed N
Source: Canadian Journal of Electrical and Computer Engineering
Year: 2017

Reference Paper:2- Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method
Author’s Name: Alavi SA, Ahmadian A and, Aliakbar-Golkar
Source: Energy Convers Manage
Year: 2015

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02/05/2026

OPTIMAL SIZING OF HYBRID PHOTOVOLTAICS–WIND TURBINE –BATTERY SYSTEMS USING MULTI-OBJECTIVE HORSE HERDING OPTIMIZATION WITH PARETO FRONT ANALYSIS

Design Overview
This study proposes a comprehensive multi-objective optimization framework for the optimal sizing of a hybrid photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS) using a Horse Herding Optimization (HOA) algorithm. The framework simultaneously minimizes the Levelized Cost of Energy (LCOE) and Loss of Power Supply Probability (LPSP), while maximizing Net Present Value (NPV), thereby achieving an effective balance between economic feasibility and system reliability. The HOA algorithm is inspired by the social behavior and hierarchical movement patterns of horse herds, incorporating mechanisms such as grazing, sociability, hierarchy, imitation, and random exploration. These coordinated behavioral strategies enable both global exploration and local exploitation of the search space, allowing the algorithm to efficiently identify high-quality solutions while avoiding premature convergence.

Hybrid renewable energy systems integrating PV, wind, and battery storage offer a sustainable and reliable solution for modern power systems. However, optimal system design remains a complex task due to the intermittent nature of renewable energy sources, battery degradation characteristics, curtailment strategies, and the inherent trade-offs among cost, reliability, and profitability in multi-objective planning. To address these challenges, the proposed framework incorporates realistic PV and wind generation models, detailed battery state-of-charge dynamics, degradation modeling, and comprehensive techno-economic formulations.

The multi-objective optimization problem is solved using a Pareto-based framework that generates a set of non-dominated solutions representing optimal trade-offs among the considered performance indices. The integration of HOA with Pareto dominance and grid-based repository mechanisms ensures diversity preservation and effective convergence toward the true Pareto front. Simulation results demonstrate that the HOA-based optimization approach provides robust convergence characteristics, superior exploration capability, and improved solution diversity compared to conventional local search methods. The results confirm that the Horse Herding Optimization algorithm is a powerful and reliable tool for the techno-economic planning of hybrid renewable energy systems under complex economic and reliability constraints.

Simulation Results
The algorithm produces a well-distributed Pareto front showing trade-offs between:
• Lower LCOE → Higher battery size
• Higher NPV → Increased curtailment
• Lower LPSP → Larger storage
Increasing battery size improves reliability but increases LCOE.

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01/05/2026

DANDELION OPTIMIZER–BASED OPTIMAL ALLOCATION OF PV–WIND GENERATION AND OLTC CONTROL FOR LOSS MINIMIZATION AND VOLTAGE PROFILE ENHANCEMENT

DESIGN DETAILS
On-load tap changer (OLTC) transformers are widely employed to regulate voltage in distribution networks; however, frequent adjustments in response to fluctuating power generation lead to excessive tap operations, accelerating mechanical wear and increasing maintenance requirements. This study aims to minimize real power losses and reduce OLTC tap operations while enhancing the overall voltage profile under varying levels of renewable energy pe*******on, specifically from photovoltaics (PVs) and wind turbines (WTs). To accomplish this, the Dandelion Optimizer (DO) is integrated with the Newton–Raphson load flow method, utilizing real-time data that captures the stochastic behavior of solar irradiance and wind speed under both sunny and cloudy conditions over a 24-hour period. The proposed approach is implemented in MATLAB and validated on the IEEE 118-bus test system under diverse loading scenarios to determine the optimal allocation of renewable energy resources. The results demonstrate improved voltage stability, reduced power losses, and a significant decrease in OLTC tap operations.

The multi-objective function,F(k)=min{f_1 (k)+f_2 (k)+f_3 (k)}
Where,
f_1 (k)=min∑_(i=1)^br▒〖R_i*I_i^2 〗 , Power Loss
f_2 (k)=1/b ∑_(k=1)^b▒|1-V_k |^2 , Voltage Deviation Index
〖f_3 (k)="V" 〗_"t" ^"tap" "=1+" 〖"Tap" 〗_"t" ^"tr" ("∆" "V" _"step" )/"100" , Transformer Tap voltage.

REFERENCES
Reference Paper-1: Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer
Author’s Name: Hamid K. Ali, Ahmed M. A. Haidar, Norhuzaimin Julai &Andreas Helwig Source: Taylor and Francis
Year:2023

Reference Paper-2: Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance.
Author’s Name: Eyad S. Oda, Amal M. Abd El Hamed, Abdelfatah Ali, Adel A. Elbaset, Montaser Abd El Sattar, and Mohamed Ebeed
Source: IEEE
Year:2021

Reference Paper-3: Energy Exchange Control in Multiple Microgrids with Transactive Energy Management
Author’s Name: Mohammadreza Daneshvar, Behnam Mohammadi-Ivatloo, Mehdi Abapour, and Somayeh Asadi
Source: IEEE
Year:2020

Reference Paper-4: Integrated Volt-VAr Optimization with Distributed Energy Sources to Minimize Substation Energy in Distribution System
Author’s Name: Saran Satsangi & Ganesh Balu Kumbhar
Source: Electric Power Components and Systems, Taylor & Francis Group, LLC
Year: 2019

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