Vol. 5 No. 1 (2026): April 30
Full Issue
Research Articles
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Article: 38Published: 23.01.2026 pp. 1-16
Hybrid Adaptive MPPT Algorithm with Intelligent Exploration and Dither-Based Tracking for PV Systems
- 4,321Views 324PDF access
AbstractThis paper proposes an improved maximum power point tracking (MPPT) approach by developing a hybrid Perturb and Observe (P&O) and Incremental Conductance (INC) algorithm based on adaptive control, considering both existing influential perturbations, such as temperature and insolation changes, and integrating the kinks. The proposed method leverages P&O's fast decision-making capability for dynamic response and INC's slope estimation technique for higher precision around the maximum power point (MPP). Moreover, dither-gradient estimation and an adaptive exploration strategy are included to make the model more robust against noise and local maxima in partial shading. The controller works in two coordinated modes—Track and Explore—for achieving rapid convergence as well as steady-state stability. The oscillations are suppressed, and re-explorations can be made when the environment changes a lot, with a combination of dynamic reference power, exponential moving average (EMA) filtering, and irradiance-drop detection. The MATLAB/simulation results verified that the introduced hybrid P&O–Inc algorithm provides faster MPP tracking, better steady-state performance, and better tracking ability in PSC as well as under changing irradiances than other conventional detached MPPT methods.
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Article: 42Published: 23.01.2026 pp. 17-36
Optimal Sizing and Techno-Economic Analysis of Battery Energy Storage System for Peak Shaving and Voltage Profile Improvement on a Rural Distribution Network
- 1,203Views 1,739PDF access
AbstractDistribution networks are increasingly strained by peak load demands and voltage regulation problems. This paper benchmarks Particle Swarm Optimization, Boda-Boda Optimization, and Adaptive Boda-Boda Optimization Algorithm with Fuzzy Logic (ABBOA-Fuzzy) to size and site Battery Energy Storage System (BESS) as a Non-Wires Alternative for rural 11kV feeder support. Baseline analysis of the feeder indicated a peak load of 99.6%, and a voltage drop to 0.936 p.u. The optimization process demonstrated the superiority of the ABBOA-Fuzzy algorithm, which converged faster. The optimized solution guided the selection of a commercially available 400kW/1200kWh BESS which reduced peak demand by 15.5% and raised the minimum voltage to 0.952 p.u. A 15-year techno-economic analysis using the System Advisor Model, accounting for battery degradation confirmed the economic viability with a Net Present Value of $43,643 and an Internal Rate of Return of 15.54%. The study recommends this framework for utility BESS planning.
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Article: 43Published: 23.01.2026 pp. 37-56
Automated Conversion of Unstructured Geospatial Feeder Data into Analytical Models: An 11 kV Case Study
- 994Views 336PDF access
AbstractThe modernization of power distribution grids, driven by the integration of DER, necessitates advanced modelling capabilities. A critical challenge is the semantic gap between the extensive geospatial asset data and the detailed electrical models required for engineering analysis. This data is often stored in static, unstructured KMZ formats that contain inherent topological errors. To address this, this paper presents a novel low-cost, Python framework that fully automates the conversion of this raw GIS data into solvable mathematical models for computation. This process generates executable files for industry-standard, script-based simulators such as MATPOWER and OpenDSS. The framework's core technical contributions include an XPath and Regex-based engine for metadata extraction. A graph-theory pipeline then utilizes a Minimum Spanning Tree (MST) algorithm to algorithmically heal topological disconnections. A Dijkstra-based method is then used for model abstraction. The core methodology was first validated using a bidirectional, reverse-engineering process on a 4-bus test case. This confirmed a lossless round-trip conversion to and from a data-rich KMZ format. Subsequently, it was applied to a complex, real-world 11 kV Kenyan distribution feeder. The generated model converged in a Newton-Raphson power flow. This demonstrated its utility as a powerful diagnostic instrument by enabling detailed feeder voltage profiling and loss analysis. Results were cross-validated against DIgSILENT PowerFactory, a graphic-based simulator, showing excellent numerical agreement. This study validates a scalable framework that transforms static, error-prone GIS data into dynamic, multi-platform diagnostic models. This approach provides a feasible pathway to accelerate grid modernization.
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Article: 45Published: 23.01.2026 pp. 57-71
Integrated Planning and Coordinated Allocation of DG and DSTATCOM Considering Radial Distribution Network Reconfiguration
- 484Views 550PDF access
AbstractIn order to enhance the operational efficacy of distribution power networks (DPNs) across techno economic and environmental aspects within a real-time operational framework, meticulous regulation of active as well as reactive power is imperative. In the present study, a Comprehensive Teaching-Learning based Optimization (CTLBO) algorithm is employed for network reconfiguration (NR) and optimal allocation of Distributed generations (DGs) along with Distribution Static Synchronous Compensators (DSTATCOMs) for single-objective in the IEEE 33-bus radial distribution systems (RDSs). Several case studies demonstrate that simultaneous NR and DGs along with DSTATCOM allocation is the most effective solution for reduction of network active power losses ultimately reduces operational costs and emission. The results further demonstrates the superiority in terms of convergence characteristics, solution robustness and global optimality of the CTLBO algorithm under complex , multi-criteria constraints for NR and DGs along with DSTATCOM allocation in RDS against established bio-inspired metaheuristics such as the Gravitational Search Algorithm (GSA), Fireworks algorithm (FWA), Harmony Search Algorithm (HSA), Genetic Algorithm (GA) and Refined genetic algorithm (RGA).
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Article: 46Published: 23.01.2026 pp. 72-99
Profit Maximization for Generating Companies Using Chaotic Whale Optimization with Inertia Weight
- 449Views 286PDF access
AbstractOptimization of power generation scheduling is critical for generating companies (GENCOs) operating in deregulated electricity markets, where maximizing profit under operational constraints is challenging. Traditional metaheuristic algorithms often suffer from premature convergence and poor exploration-exploitation balance. Here we propose an improved Whale Optimization Algorithm incorporating chaotic mapping and inertia weight (CWOA-IW) to enhance population diversity and convergence behaviour. The algorithm is applied to the profit maximization problem of GENCOs, considering market prices and system constraints. Validation on 23 benchmark functions and three power system test cases (3-unit 10-bus, IEEE 39-bus, IEEE 118-bus) demonstrates superior accuracy, stability, and faster convergence compared to standard WOA, Grey Wolf Optimizer, and Particle Swarm Optimization. The results indicate that CWOA-IW provides a robust and scalable optimization framework, offering practical benefits for GENCOs in competitive electricity markets.
