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06/06/2026

SMELL AGENT OPTIMIZATION FRAMEWORK FOR PHOTOVOLTAIC–WIND TURBINE–BATTERY ENERGY STORAGE SYSTEM INTEGRATION IN SMART DISTRIBUTION NETWORKS CONSIDERING LOAD MODELS AND RELIABILITY INDICES

DESIGN DETAILS
The rapid pe*******on of renewable energy resources and battery energy storage systems (BESS) in active distribution networks introduces significant operational challenges related to voltage stability, power loss minimization, and reliability enhancement. This paper presents a comprehensive optimization framework for the simultaneous allocation and optimal sizing of photovoltaic (PV) units, wind turbines (WT), and BESS using the Smell Agent Optimization (SAO) algorithm.

The proposed SAO-based approach effectively minimizes total active power loss and voltage deviation while satisfying network operational constraints. A 24-hour time-series analysis is incorporated using realistic solar irradiance and wind speed data to accurately model renewable generation variability.

Furthermore, the framework evaluates system performance under multiple load models, including constant power (CP), constant current (CI), constant impedance (CZ), and ZIP load models, ensuring robustness under diverse operating conditions. Reliability assessment is integrated through standard indices such as SAIDI, SAIFI, EENS, AENS, ASAI, ASUI, and CAIDI.

The proposed methodology is validated on the IEEE 33-bus radial distribution system considering six installation scenarios: PV, WT, BESS, PV–BESS, WT–BESS, and PV–WT–BESS. Simulation results demonstrate significant reductions in power losses, improved voltage profiles, and enhanced system reliability. Notably, the coordinated integration of PV–WT–BESS provides the best overall system performance.

The Smell Agent Optimization algorithm employs sniffing, trailing, and random search mechanisms to achieve an effective balance between exploration and exploitation, enabling efficient identification of optimal distributed energy resource configurations. The algorithm exhibits stable convergence characteristics, strong global search capability, and robustness under varying operating conditions, confirming its effectiveness for multi-source distributed energy planning in modern smart distribution networks.

MULTI-OBJECTIVE FUCNTION
The objective function,F(k)=min{w_1 f_1 (k)+w_2 f_2 (k)}
f_1 (k)=min∑_(i=1)^br▒〖R_i*I_i^2 〗 , Power Loss
f_2 (k)=V_dev=∑_(i=1)^(N_L)▒|V_i-V_i^* | , Voltage Deviation

Where,
w_1,w_2 represent the weighting factors. The summation of weights should not exceed 1 here ω_1=0.5, ω_2=0.5, f_1, and f_(2 ) by providing equal weights for objectives.

Load Model: CP Load Model, CI Load Model, CZ Load Model, ZIP Load Model.

RELIABILITY INDICES (Reference Paper-4)
System Average Interruption Frequency Index (SAIFI)
System Average Interruption Frequency Index (SAIDI)
Customer Average Interruption Duration Index (CAIDI)
Average service availability index (ASAI)
Average Service Unavailability Index (ASUI)
Expected energy not supplied (EENS)

Scenarios
Basecase(without optimization algorithm)
Optimal allocation of PV using optimization algorithm
Optimal allocation of WT using optimization algorithm
Optimal allocation of BESS using optimization algorithm
Simultaneous allocation of PV with BESS using optimization algorithm
Simultaneous allocation of WT with BESS using optimization algorithm
Simultaneous allocation of PV, WT, and BESS using optimization algorithm

Matlab Simulation Results
Active Power Loss (kW)
Reactive Power Loss (kVAr)
Minimum and Maximum Voltage (PU) @ Bus
Optimal PV, WT, and BESS Location
Optimal BESS Size
Ex*****on Time

Matlab Simulation Figures
Voltage Profile
Convergence graph

REFERENCES
Reference Paper-1: Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar.
Author’s Name: Eyad S. Oda, Amal M. Abd El Hamed, Abdelfatah Ali and, Adel A. Elbaset,
Source: IEEE
Year:2021

Reference Paper-2: 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-3: Optimal placement and sizing of photovoltaics and battery storage in distribution networks
Author’s Name: Riad Chedid and Ahmad Sawwas
Source: Wiley
Year:2019

Reference Paper-4: DG Placement Using Loss Sensitivity Factor Method for Loss Reduction and Reliability Improvement in Distribution System
Author’s Name: G.Sasi Kumar, Dr.S.Sarat Kumar, Dr.S.V.Jayaram Kumar
Source: IJET
Year: 2018

Reference Paper-5: Energy saving using D-STATCOM placement in radial distribution system under reconfigured network.
Author’s Name: Atma Ram Gupta and, Ashwani Kumar
Source: Elsevier
Year:2016

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05/06/2026

LOVE EVOLUTION ALGORITHM-BASED TWO-STAGE FUZZY MULTI-OBJECTIVE FRAMEWORK FOR OPTIMAL DG AND EVCS PLANNING IN DISTRIBUTION NETWORKS CONSIDERING ROOFTOP PV UNCERTAINTY

DESIGN OVERVIEW
The increasing pe*******on of Distributed Generators (DGs), Electric Vehicle Charging Stations (EVCSs), and randomly deployed Rooftop Photovoltaic (RTPV) systems has significantly increased operational uncertainty and planning complexity in modern distribution networks. To effectively address these challenges, this MATLAB-based study proposes a Love Evolution Algorithm–based Two-Stage Fuzzy Multi-Objective (LEA-TSFMO) framework for the optimal siting and sizing of DGs and EVCSs under high RTPV variability.

In the first stage, a fuzzy multi-objective decision model is developed to aggregate multiple conflicting planning objectives, including real power loss minimization and voltage profile enhancement, into a unified fuzzy satisfaction index. This approach enables effective handling of uncertainties and trade-offs inherent in distribution system planning.
In the second stage, the Love Evolution Algorithm (LEA) is employed to solve the constrained optimization problem. LEA is a population-based metaheuristic inspired by the dynamics of human relationship evolution and interaction processes. The algorithm incorporates encounter, stimulus, value, role, and reflection phases to effectively balance global exploration and local exploitation within a nonlinear, multi-dimensional search space. Through adaptive interaction mechanisms, compatibility evaluation, and reflection-based diversification, LEA enhances convergence performance, avoids premature stagnation, and improves solution quality.

To accurately model the stochastic behavior of rooftop PV generation, probabilistic RTPV modeling is incorporated into both stages of the framework. The optimized network configuration obtained after RTPV-based DG integration is subsequently used as the base case for EVCS allocation, ensuring coordinated planning.

The proposed LEA-TSFMO framework is validated on the 33-bus radial distribution system under multiple operating scenarios and uncertainty conditions. Simulation results demonstrate that the proposed approach significantly reduces real power losses, improves voltage stability, and enhances renewable hosting capacity when compared with conventional optimization techniques. Owing to the effective integration of fuzzy decision-making and the robust search capability of LEA, the method exhibits superior convergence characteristics, high-quality solutions, and enhanced planning flexibility. Therefore, the proposed framework serves as a reliable and practical decision-support tool for future smart distribution systems with high renewable energy pe*******on and growing electric mobility demand.

MULTI-OBJECTIVE FUNCTION
The optimization problem aims to:
• Improve substation power factor
• Minimize real power losses
• Enhance voltage profile

To achieve these objectives, fuzzy membership functions are utilized:
• Triangular membership functions → for DG pe*******on and power factor
• Trapezoidal membership functions → for power loss and voltage constraints

TEST SCENARIOS
1. Base Case
2. Only Rooftop PV–DG
3. Only EVCS
4. Simultaneous RTPV–DG and EVCS

SIMULATION OUTPUTS
Graphical Results
• Voltage Profile
• Power Loss
• Convergence Characteristics

Numerical Results (MATLAB Command Window)
• Total Power Loss
• Optimal Roof-Top PV-DG Locations
• Optimal EVCS Locations
• Minimum & Maximum Bus Voltages
• Ex*****on Time

REFERENCES
1. W***y Stephen Tounsi Fokui, Michael J. Saulo, and Livingstone Ngoo,
“Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network with Randomly Distributed Rooftop Photovoltaic Systems,” IEEE, 2021.
2. Srinivasa Rao Gampaa, Kiran Jasthia, Preetham Golib, D. Das, and R.C. Bansal,
“Grasshopper Optimization Algorithm Based Two-Stage Fuzzy Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generations, Shunt Capacitors and Electric Vehicle Charging Stations,” Elsevier, 2019.

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04/06/2026

LIVER CANCER ALGORITHM-BASED OPTIMAL CONTROL VARIABLE COORDINATION FOR CONGESTION REDUCTION AND VOLTAGE STABILITY ENHANCEMENT

DESIGN DETAILS
Increasing power demand, network congestion, and voltage instability pose significant challenges to the reliable operation of modern power systems, particularly in large, interconnected transmission networks. This work proposes a Liver Cancer Algorithm (LCA)-based optimization framework for congestion mitigation and voltage stability enhancement in the IEEE 30-bus power system. The proposed approach integrates conventional Newton–Raphson load flow analysis with the Liver Cancer Algorithm, a bio-inspired metaheuristic optimization technique that mimics the growth, mutation, and invasion mechanisms of liver cancer cells to efficiently explore and exploit the search space. The optimization framework determines the optimal settings of key control variables, including generator reactive power outputs, transformer tap positions, and reactive power compensation devices, to minimize transmission congestion and improve system voltage profiles. The objective is to reduce line overloads, enhance voltage stability margins, and maintain bus voltages within acceptable operating limits.

The effectiveness of the proposed LCA-based optimizer is evaluated on the IEEE 30-bus transmission network, a widely used benchmark system representing realistic operating conditions with multiple generators and transmission corridors. Simulation results demonstrate that the Liver Cancer Algorithm successfully alleviates congestion on heavily loaded transmission lines, improves voltage stability performance, and enhances the overall operational efficiency of the network. Comparative analyses reveal substantial reductions in line loading violations and voltage deviation indices under different loading scenarios. The results confirm that the proposed LCA-based optimization framework provides a robust, computationally efficient, and reliable solution for congestion management and voltage stability enhancement in large-scale power systems. The developed methodology offers a practical decision-support tool for power system operators in both planning and real-time operational environments.

OBJECTIVE FUNCTIONS
Minimization of Active Power Loss
Voltage Stability Enhancement
Congestion Alleviation (Branch Loading Index)

Constraints
Power flow equality constraints (NR equations)
Generator limits
Bus voltage limits (0.95–1.05 pu)
FACTS operational limits
Line thermal limits

REFERENCES
Reference Paper-1: Congestion Management in Deregulated Electricity market using FACTS &Multi-objective optimization.
Author’s Name: Atma Ram Gupta and, Ashwani Kumar
Source: Elsevier
Year:2016

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03/06/2026

ADVANCED CONTROL OF L–C FILTERED BLDC MOTOR DRIVES VIA MANTA RAY FORAGING OPTIMIZATION-BASED PARAMETER TUNING

DESIGN DETAILS
Brushless DC (BLDC) motor drives are extensively employed in high-performance industrial and automotive applications due to their high efficiency, superior power density, compact structure, and reliable operation. However, inverter-fed BLDC drives often suffer from torque ripple and current harmonic distortion, which adversely affect speed regulation, increase acoustic noise, and reduce overall drive performance. To address these challenges, this study proposes an advanced control strategy based on the Manta Ray Foraging Optimization (MRFO) algorithm for the effective reduction of torque ripple and harmonic distortion in an L–C filtered BLDC motor drive system.

An L–C filter is incorporated at the inverter output to suppress high-frequency switching harmonics and smooth the motor current waveform, thereby mitigating commutation-induced disturbances and improving power quality. The Manta Ray Foraging Optimization algorithm is employed to optimally tune the controller parameters owing to its unique chain foraging, cyclone foraging, and somersault foraging mechanisms, which provide an effective balance between exploration and exploitation while maintaining strong global search capability. The optimization objective is formulated to simultaneously minimize torque ripple, reduce harmonic distortion, and achieve accurate speed tracking under varying operating conditions.

The proposed MRFO-based control scheme is implemented and validated in the MATLAB/Simulink environment under different loading scenarios. Simulation results demonstrate a significant reduction in torque ripple and Total Harmonic Distortion (THD), along with enhanced transient response, improved steady-state performance, and superior speed regulation compared with conventional control approaches. Furthermore, the proposed method exhibits strong robustness against load disturbances, parameter uncertainties, and operating condition variations.
Overall, the integration of the Manta Ray Foraging Optimization algorithm with an L–C filtering mechanism provides an efficient, reliable, and robust solution for improving power quality, dynamic performance, and control effectiveness in BLDC motor drive applications.

REFERENCES
Reference Paper-1: Harmonics and Torque Ripple Minimization using L-C Filter for Brushless DC Motors
Author’s Name: A. Albert Rajan and Dr. S. Vasantharathna2
Source: IEEE
Year: 2009

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02/06/2026

SOCIAL ENGINEERING OPTIMIZER FOR INTEGRATED NETWORK RECONFIGURATION AND OPTIMAL DG–CAPACITOR PLANNING IN DISTRIBUTION SYSTEMS

DESIGN DETAILS
The rapid growth in load demand and the increasing pe*******on of distributed energy resources have introduced significant operational challenges in modern smart distribution systems. Improving network efficiency, maintaining voltage stability, and minimizing power losses require coordinated optimization of multiple control variables within the distribution network. This design presents a unified optimization framework based on the Social Engineering Optimizer (SEO) for the simultaneous implementation of distribution network reconfiguration and optimal placement and sizing of distributed generators (DGs) and shunt capacitors.

The proposed approach is inspired by social engineering strategies, where individuals adapt their behavior by learning from and moving toward the most successful solution within the population. This mechanism enables an effective balance between global exploration through stochastic mutation and local exploitation via leader-guided search. In the developed framework, switch configurations, DG locations and capacities, and capacitor placements are optimized simultaneously to achieve coordinated control of network topology, active power injection, and reactive power compensation. The optimization objective is formulated to minimize total active power losses while enhancing voltage profiles and ensuring compliance with radiality and operational constraints of the distribution network.

The effectiveness of the proposed SEO-based strategy is validated on standard radial distribution test systems using a backward–forward sweep load flow method to accurately capture network behavior under varying operating conditions. The algorithm demonstrates competitive convergence characteristics and reliable solution quality due to its simplified yet effective search mechanism. Three distinct operating scenarios are investigated:
1.Base case (existing configuration without enhancements)
2.Reconfiguration followed by optimal DG and SC placement
3.Fully simultaneous reconfiguration with DG and SC allocation

Simulation results demonstrate significant improvements in power loss reduction, voltage regulation, and overall network performance compared with conventional planning approaches and several existing metaheuristic techniques. The results confirm that the SEO-based simultaneous optimization framework provides a robust and computationally efficient solution for smart distribution system planning and operational enhancement.

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

SIMULATION FIGURES
Voltage Profile
Active Power Loss
Reactive Power Loss
Convergence Graph

SIMULATION RESULTS
Active Power Loss(kw)
Reactive Power Loss(kvar)
Network Tie Switch
Optimal DG Location
Optimal Capacitor Location
Optimal DG size P(kw)
Optimal DG size Q(kva)
Optimal Capacitor size Q(kvar)
Total DG size P(kw)
Minimum Voltage @ Bus Location
Maximum Voltage @ Bus Location
Ex*****on Time

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01/06/2026

KANGAROO ESCAPE OPTIMIZER–BASED OPTIMAL CAPACITOR PLACEMENT AND SIZING FOR POWER LOSS REDUCTION AND VOLTAGE STABILITY ENHANCEMENT IN ACTIVE DISTRIBUTION NETWORKS WITH DG, EV, AND DEMAND RESPONSE INTEGRATION

OVERVIEW
The rapid modernization of electric power distribution systems has resulted in the increasing pe*******on of distributed generation (DG), renewable energy resources, electric vehicle (EV) charging infrastructure, and demand response (DR) programs within active distribution networks (ADNs). These emerging technologies improve system sustainability, operational flexibility, and energy efficiency; however, they also introduce several operational challenges such as voltage instability, increased reactive power demand, feeder overloading, network congestion, and elevated system power losses. Reactive power compensation through optimal capacitor placement and sizing remains one of the most economical and effective approaches for improving voltage profiles and minimizing power losses in radial distribution systems.

Conventional deterministic and analytical optimization techniques are often unable to solve complex nonlinear multi-objective optimization problems associated with modern smart distribution networks. Consequently, metaheuristic optimization algorithms have gained considerable attention due to their strong global search capability and ability to handle mixed-integer nonlinear optimization problems. Among these algorithms, the Kangaroo Escape Optimizer (KEO), inspired by the intelligent escape behavior and adaptive movement strategies of kangaroos in hostile environments, has demonstrated promising optimization capability for power system applications due to its balanced exploration–exploitation characteristics, adaptive search mechanism, and strong ability to avoid local optima. The KEO algorithm enhances global search efficiency while maintaining convergence stability, making it highly suitable for solving large-scale nonlinear optimization problems in highly constrained active distribution systems with renewable energy integration and dynamic operating conditions.

The framework incorporates realistic active distribution system operating conditions including:
Solar photovoltaic distributed generation,
Wind turbine generation,
Diesel distributed generation,
Small hydro distributed generation,
EV charging demand,
Demand response operation,
Generation curtailment mechanisms.

A MATLAB-based 24-hour techno-economic optimization platform has been developed using:
BIBC–BCBV based Backward–Forward Sweep load flow,
Multi-objective optimization,
Renewable energy modeling,
Smart-grid demand-side management,
Cost-benefit analysis.

The framework simultaneously minimizes:
Real power loss,
Voltage deviation,
Total annual operational cost,
while maximizing:
Voltage Stability Index (VSI).
The methodology will be validated on IEEE 33-bus bus radial distribution systems under multiple smart-grid operating scenarios.

OBJECTIVE FUNCTIONS
The optimization problem is formulated as a weighted multi-objective function including:
1. Real Power Loss Minimization
F_1=min⁡(P_loss)
2. Voltage Deviation Minimization
F_2=min⁡(VDI)
3. Voltage Stability Maximization
F_3=max⁡(VSI)
4. Total Annual Cost Minimization
F_4=min⁡(TAC)
Weighted Multi-Objective Function
F=w_1 F_1+w_2 F_2+w_3 F_3+w_4 F_4

Techno-Economic Analysis
Economic evaluation includes:
Capacitor installation cost,
Capacitor purchase cost,
Operational cost,
Energy loss cost,
Annual savings.

The following economic indicators are evaluated:
Net Present Value (NPV)
NPV=∑(B_t-C_t)/(1+r)^t
Benefit–Cost Ratio (BCR)
BCR=Benefits/Costs
Payback Period (PP)
PP=Initial Investment/Annual Savings

RENEWABLE DG MODELING
a) Solar Photovoltaic Model
The PV output power is modeled as a function of:
Solar irradiance,
Ambient temperature.
P_PV=P_rated (1+η∣T_ref-T_amb∣) G/1000
where:
Gis solar irradiance,
T_ambis ambient temperature.

b) Wind Turbine Model
The wind turbine output is modeled using a cubic wind-speed relationship:
P_WT=P_rated ((v-v_cutin)/(v_rated-v_cutin ))^3
where:
v_cutin= cut-in wind speed,
v_rated= rated wind speed,
v_cutout= cut-out wind speed.

c) Diesel Generator Model
Diesel DGs provides both active and reactive power support:
P_dg^min≤P_dg≤P_dg^max
Q_dg^min≤Q_dg≤Q_dg^max
Q=Ptan⁡(〖cos⁡〗^(-1) (PF))
where:
PFis generator power factor.

d)Small Hydro DG Model
Hydro DG active power depends on:
Rated power,
Load factor.
Reactive power is computed using generator power factor relationships.
P_SHPP=P_(rated,SHPP)×LF_SHPP
Q_SHPP=P_( SHPP)×tan⁡(cos^(-1)⁡〖PF_SHPP 〗)

Modeling of Embedded Loads
Additional embedded loads representing:
EV charging demand,
Residential load growth are connected to selected buses of the radial distribution system.
EV charging loads are modeled as additional active power demand integrated into the hourly load flow simulation. The demand response mechanism dynamically modifies hourly load demand during the 24-hour simulation horizon.

This approach enables realistic evaluation of:
Voltage stability,
Network loading,
Power loss variation,
Smart-grid operational flexibility.

Demand Response Modeling
The demand response strategy dynamically modifies bus demand as:
P_DR (i,t)=P_load (i,t)×(1-DR(i,t))
where:
DR(i,t)represents the demand response participation factor.
Randomized DR participation values are generated for each bus over the 24-hour operating horizon.
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31/05/2026

OPTIMAL PLACEMENT AND SIZING OF MULTIPLE SHUNT CAPACITORS USING THE MUD RING ALGORITHM FOR POWER LOSS MINIMIZATION IN RADIAL DISTRIBUTION SYSTEMS

DESIGN DETAILS
Efficient reactive power compensation is essential for improving voltage stability, reducing real power losses, and enhancing the overall operational performance of modern distribution networks. This MATLAB-based study presents an intelligent optimization framework based on the Mud Ring Algorithm (MRA) for determining the optimal placement and sizing of shunt capacitor banks in radial distribution systems. MRA is a nature-inspired metaheuristic optimization algorithm inspired by the collective movement and adaptive foraging behavior observed in mud-ring forming marine organisms, enabling an effective balance between global exploration and local exploitation through leader-guided search and nonlinear position updating mechanisms. In the proposed method, the capacitor allocation problem is formulated as a constrained nonlinear optimization task, with the objective of minimizing total real power losses and improving voltage profiles while satisfying operational, bus-voltage, and capacitor-capacity constraints. A load-flow engine integrated with the MRA search mechanism evaluates the network performance corresponding to candidate capacitor configurations. The algorithm employs adaptive exploration and exploitation strategies, where search agents update their positions based on the best-performing solution (leader) and nonlinear search operators that enhance population diversity and prevent premature convergence. The effectiveness of the proposed MRA-based approach is validated on standard radial distribution test systems, demonstrating fast convergence characteristics, improved voltage profiles, and significant real power loss reduction compared to conventional and recent optimization techniques. The results confirm that MRA is a robust, reliable, and computationally efficient tool for reactive power compensation and optimal capacitor planning in practical distribution networks.

OBJECTIVE FUCNTION
Minimization of Active Power Loss
Average Voltage Deviation Index (AVDI) improvement
Voltage Stability Index (VSI) enhancement

The multi-objective function,F(k)=min{w_1 f_1 (k)+w_2 f_2 (k)+w_3 (1/(f_3 (k)))}
where,
w_1,w_2 ,w_3 represent the weighting factors. The summation of weights should not exceed 1 here ω_1=0.333, ω_2=0.333, ω_3=0.333 considered f_1,〖 f〗_2, and f_(3 ) by providing equal weights for objectives.

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

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