This research presents a comparative performance evaluation of two advanced maximum power point tracking (MPPT) methodologies, namely sliding mode control (SMC) and the Kalman filter (KF), specifically applied to a standalone photovoltaic water pumping system (PVWPS). To achieve economic viability, the system is designed for storage-less operation, driving a three-phase induction motor (IM) via a high-dynamic direct torque control (DTC) scheme and a three-level inverter. The core technical contribution addresses the critical challenge of maximizing energy yield under partial shading conditions (PSCs). PSCs result in a complex, non-convex power–voltage (P−V) characteristic, containing multiple peaks, where conventional MPPT algorithms fail to consistently locate the global maximum power point (GMPP). To overcome this deficiency, we implemented the SMC-based MPPT algorithm to exploit its inherent robustness and rapid dynamic response, and compared it with the Kalman filter MPPT, which relies on stochastic state estimation to achieve accurate tracking and effective disturbance rejection. MATLAB/Simulink analysis compares the proposed techniques with the perturb and observe (P&O) MPPT method. The comparison considers tracking efficiency, convergence speed, and steady-state ripple under various shading conditions to identify the most effective control strategy for improving PVWPS performances. The reported performance evaluations are based on numerical simulations conducted within the MATLAB/Simulink environment, using a validated system model.
This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions.
Lithium-ion battery (LIB)-powered locomotives have emerged as a promising alternative to conventional rail electrification by reducing dependence on overhead catenary systems, lowering infrastructure costs, and improving operational flexibility. However, train failures involving power loss or mechanical faults present significant challenges for battery-powered rail systems, particularly on single-track corridors where alternate route options are limited. Consequently, the implementation of a reliable limp-home strategy is more complex than in road electric vehicles (EVs), requiring consideration of battery availability, rescue logistics, and track accessibility. This study investigates limp-home operation and rescue planning for a battery-powered historic trolley operating on a 20 km heritage route in North Carolina. The trolley is powered by a dedicated LIB trailer and supported by battery-charging (BC) infrastructure based on inductive power transfer (IPT) technology. A comprehensive framework is developed that integrates time–space analysis, cellular automata (CA)-based failure-risk modeling, and battery-energy assessment to evaluate train-failure scenarios and recovery strategies. The results identify critical failure regions along the route and demonstrate the benefits of strategically deploying additional LIB rescue trailers and wireless power transfer (WPT) infrastructure. A revised rescue strategy incorporating two additional LIB trailers, together with static and dynamic WPT systems, substantially reduces recovery time and improves the likelihood of maintaining scheduled excursions. Emergency energy analysis further shows that WPT-assisted operation can significantly extend limp-home capability under low state-of-charge conditions. Based on the integrated analysis, a tiered limp-home decision framework is developed to support operator decision-making during train failures. The proposed methodology provides a practical approach for enhancing the resilience, recoverability, and operational reliability of battery-powered heritage rail systems and can serve as a foundation for future battery-electric rail applications.
This paper presents a novel master–slave stochastic optimization framework for the optimal siting and sizing of fixed-step capacitor banks in medium-voltage distribution networks, explicitly addressing the inherent variability of load demand that is typically neglected in conventional deterministic approaches. The proposed methodology integrates a scenario-based stochastic optimization model with a Chu and Beasley genetic algorithm (CBGA) as the master stage, which handles discrete placement decisions, and a successive-approximation power flow method (SAPF) as the slave stage, which evaluates the technical and economic performance of each candidate solution under multiple load scenarios. To capture demand uncertainties, 365 daily load realizations are generated using independent Gaussian noise with a relative standard deviation of 10% applied to each load point. These are subsequently reduced to ten representative scenarios via k-means clustering, reducing the number of power-flow evaluations per candidate solution from 365 to 10 (a 36.5-fold reduction); the reduced scenarios exhibit a low mean absolute error (MAE: <2%) with respect to the original mean, indicating faithful representation of the average load behavior, while the silhouette score is modest (approximately 0.25), consistent with the unimodal nature of the generated data and implying that the clusters are not well separated. Extensive simulations on a 33-bus test feeder considering three energy-cost-escalation scenarios (0%, 10%, and 20%) over a 20-year planning horizon demonstrate that both the deterministic and stochastic approaches reduce the total net present cost by 16.52% to 17.34% compared to the uncompensated network; the stochastic approach consistently delivers solutions that are either superior or comparable to deterministic planning (yielding up to approximately 0.16% additional cost reduction) while offering enhanced robustness against load variability. The stochastic framework offers distinct advantages, including robust solutions across a wide range of operating conditions, an inherent ability to adjust investment levels in response to probabilistic load distributions, and the ability to quantify uncertainty in decision making, with the most significant benefits observed when energy costs are low and load variability is high. The convergence of both approaches at a 20% escalation level further validates the reliability of high-resolution deterministic modeling when economic factors strongly dominate the optimization objective. This study underscores the importance of probabilistic modeling for modern distribution network planning, providing a practical and computationally efficient decision-support tool for utility planners to enhance grid resilience and operational efficiency in the context of increasing demand variability and renewable energy integration.
The availability of reliable digital models of low-voltage (LV) networks is a prerequisite for operation, planning, hosting-capacity, and asset-management studies. In practice, however, utility geodatabases often contain geometric discontinuities, implicit relationships among assets, and incomplete connectivity, which hinders their direct use as topological models for electrical studies. This work proposes a reproducible framework for topological-quality and fitness-for-use screening, applied to a real LV network derived from a utility geographic information system (GIS). The methodology converts geospatial layers into a graph representation, audits load-to-pole and endpoint-to-known-node distances, evaluates structural sensitivity to the endpoint connection tolerance, computes a load-level screening confidence score, characterizes transformer-level coverage under an explicit demand scenario, and evaluates structural sensitivity under synthetic perturbations of the line geometry. The case study comprises 38 urban blocks, 155 poles, 13 distribution transformers, 429 loads, 777 overhead LV segments, and 23 underground LV segments. The mean load-to-pole distance is 10.91 m and the mean endpoint-to-known-node distance is 4.39 m. The fixed 30 m load-to-pole assignment rule retains 416 loads (96.97%) throughout the tolerance sweep, while the number of connected components decreases from 245 to 82 and the number of virtual nodes from 817 to 439 as the endpoint tolerance increases from 0.5 to 5.0 m. The confidence score classifies 32.40% of loads as High, 59.44% as Medium, 5.13% as Low, and 3.03% as Review/Unassigned. Under the stated residential screening assumptions, four transformers exceed the adopted 80% loading threshold. Synthetic perturbations leave the load-to-pole control metrics unchanged by construction, because only line-segment geometry is perturbed, but the number of connected components varies from 78 to 236, with a maximum relative change of 187.80% with respect to the 5 m base graph. The contribution is therefore a transparent GIS quality-screening procedure, not an exact reconstruction or electrical validation of the physical LV topology.
The increasing penetration of variable renewable energy sources into electric power systems requires advanced optimization tools to address the complexity of hybrid hydrothermal scheduling while minimizing generation costs and carbon emissions. This study investigates the application of four deep learning architectures—Kolmogorov–Arnold networks (KANs), long short-term memory (LSTM), gated recurrent unit (GRU), and deep feedforward (DFF)—to solve the hydrothermal scheduling problem in hybrid power systems that incorporate wind power generation and pumped-storage hydropower (PSH) plants. The methods were evaluated on a 10-generator test system over a 24-h planning horizon in three objective-weighting scenarios, considering economic dispatch only, pure emission minimization only, and balanced objectives. All architectures successfully solved the integrated problem and satisfied the system constraints. This study reports the first application of the KAN to the hydrothermal scheduling problem, demonstrating its viability and interpretability potential for future applications in electric power systems.
The DC–DC boost converter is a challenging control target because of its nonlinear dynamics, wide operating range, and non-minimum-phase behavior under continuous conduction mode. These control challenges are particularly pronounced under large-signal transients, parameter variations, constant-power-load effects, and hardware constraints. This review examines deep reinforcement learning-based control of DC–DC boost converters from an engineering-oriented perspective. It covers learning-assisted classical control, direct duty-cycle control, and hybrid architectures, with attention to action design, reward formulation, observation timing, safety constraints, and validation fidelity. A structured search of Scopus, Web of Science Core Collection, and IEEE Xplore was used to identify boost-specific studies and transferable adjacent-converter evidence. Rather than ranking algorithms alone, the review organizes the literature around converter-aware and hardware-oriented learning control. The review argues that recent progress should not be interpreted as a simple replacement of classical control by deep reinforcement learning. Accordingly, algorithm choice, physical knowledge, action and reward design, observation timing, safety constraints, and validation fidelity are treated jointly. The available evidence suggests that progress toward credible practical deployment requires integrating converter physics, bounded or hybrid control authority, explicit safety constraints, and hardware-oriented validation.
Traditional Digital Twins (DTs) in energy sectors lack cyber-threat awareness, while cybersecurity DTs overlook downstream physical impacts. Loosely coupled co-simulations attempt to bridge this gap but introduce computational lags that mask critical cross-domain vulnerabilities. To address these limitations, this paper proposes a unified, tightly coupled DT framework that integrates energy systems and cybersecurity domains into a single environment. The methodology models the precise mathematical, thermal, and electrical constraints of key assets to capture cross-domain feedback loops. Specifically, a power transformer and a microgrid-connected inverter serve as case studies to map cyberattack vectors directly onto physical definitions. Numerical validation evaluates multiple threat scenarios, including supervisory, measurement, and physical-level (harmonic) attacks on the transformer, alongside short-circuit and hybrid phase-harmonic attacks on the inverter. Results demonstrate how subtle digital disruptions propagate past communication layers to induce physical degradation and operational stress. By explicitly detailing the governing equations and providing sensitivity analyses, this work delivers a transparent, high-fidelity methodology for protecting critical cyber-physical infrastructures from asset-destructive manipulations.
This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations.
This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and post-dispatch battery-aging analysis. The operating-cost objective coordinates hourly BESS active-power exchanges while enforcing storage and AC-network constraints. A parallel Coyote Optimization Algorithm (COA) is compared with parallel GWO, GA, PSO, and MVO implementations under a common formulation, correction procedure, evaluator, and computational environment. Validation uses modified 33-, 69-, and 136-bus radial feeders: 100 independent runs for the deterministic 33-bus benchmark, 100 independently optimized Monte Carlo scenarios for the 69-bus assessment, and seven representative daily profiles for the 136-bus weekly case. COA achieved an average cost reduction of 1.0084%, with the lowest dispersion of σ=0.0070%, in the 33-bus system; a mean scenario-wise reduction of 1.8337% in the 69-bus system; and a weekly reduction of 0.4402% in the 136-bus system. It obtained the lowest operating costs among the evaluated calibrated configurations, and all pairwise comparisons remained significant after Holm’s step-down adjustment applied separately within each system, although COA required greater computational effort than PSO. The reported schedules satisfied the imposed BESS and AC-network limits. Battery aging was evaluated only after scheduling and was not included in the optimization objective. The resulting cost-oriented schedules produced equivalent full-cycle values near 0.8 day−1 and projected 80% SoH lifetimes of approximately 6–8 years. These results provide an AC-feasible basis for comparing economic performance and post-dispatch battery-health implications under the evaluated conditions.
Arc flash faults in industrial substations can release high levels of incident energy, particularly on the line side of incoming circuit breakers, where opening the local breaker alone does not eliminate the source contribution. This paper proposes and experimentally evaluates an IEC 61850-based methodology to mitigate this protection gap by transferring arc flash trip signals between substations using GOOSE messages. The methodology comprises laboratory validation of the complete GOOSE-based protection chain and communication network, followed by validation in an operating industrial substation. Laboratory tests demonstrated satisfactory performance for both homogeneous and multivendor IED configurations, although longer operating times were observed when the test current approached the overcurrent pickup setting. The communication network achieved a mean transfer time of 5.31 ms and a maximum of 6.00 ms. In the industrial case study, the maximum protection operating time was 90 ms. Considering a conservative total fault-clearing time of 107.5 ms, the incident energy was reduced from 7.34 cal/cm2 to 2.04 cal/cm2, corresponding to a reduction of approximately 72%. These results demonstrate the feasibility of IEC 61850 GOOSE communication for high-speed trip transfer, reducing fault-clearing time and mitigating incident energy under critical line-side fault conditions.
Maintaining frequency stability in modern interconnected power systems (PSs) has become increasingly challenging due to the high penetration of renewable energy sources (RESs) and the dynamic nature of generation and demand. To address these issues, this paper proposes a novel load frequency control strategy that integrates a Dung Beetle Optimizer (DBO)-tuned fractional-order Proportional–Integral–Derivative (FOPID) controller with a newly developed multi-source interconnected power system. This model combines PV, thermal, hydro, nuclear, and advanced storage (HAE and fuel cells). Unlike existing methods, the proposed approach simultaneously leverages DBO’s balanced search mechanism and FOPID’s fractional dynamics to enhance frequency stability under high renewable penetration. The performance of the proposed controller is validated through a comparative analysis with Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO) methods. Simulation results show that the DBO-based controller significantly improves dynamic response, achieving reductions in settling time of 9.5% and 4.7% compared to PSO and ALO, respectively. Furthermore, the proposed approach enhances frequency regulation and tie-line power stability over other optimization methods and controllers, demonstrating strong robustness and adaptability for future high-RES power systems.
Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A-D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7-5.9% to approximately 2.2-2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE approximate to 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3-4%, while irregular users exhibited much higher errors, exceeding 18-21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management.
As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 +/- 0.0086 to 0.0048 +/- 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder.
False data injection attacks (FDIAs) pose a growing threat to distributed energy resource (DER) aggregators because a compromised aggregator can expose and affect a number of enrolled DERs. However, DER aggregators only have access to limited measurements from DERs and the systems. This makes existing FDIA detection methods ineffective in this setting due to two main limitations: (1) they are grounded in full observability of a microgrid or distribution system and therefore are incompatible with the limited observability of a DER aggregator; (2) they can only either detect anomalies or explain why a deviation occurs, but not both simultaneously. To address these limitations, we propose a physics-guided fusion-based cyberattack detection method specifically designed for DER aggregators. This approach integrates two complementary modules: a forecasting-assisted residual method for rapidly anomaly detection, and a PV-aware sensitivity-based method to diagnose and explain their underlying physical causes. A gradient boosting machine (GBM) is then leveraged to fuse these outputs, optimizing the precision–recall tradeoff. The proposed method is tested on one microgrid test system with different bus observability levels across static and gradual attack scenarios with multiple levels of attack sophistication. Across the scenarios, the proposed method achieves a 0.91–0.93 precision–recall area under the curve score (PR-AUC), demonstrating the method’s effectiveness in securing DER aggregators with partial system visibility.
Large Language Models (LLMs) are rapidly transitioning from research concepts to transformative artificial intelligence components within the power and energy domain. Their ability to fuse diverse data, spanning SCADA logs, real-time sensor readings, and regulatory documentation enables unprecedented capabilities in forecasting, operator decision support, anomaly detection, and wide-area situational awareness for future intelligent grids. However, the integration of LLMs into safety-critical and highly regulated power systems introduces a convergence of novel and severe security risks. Beyond exhibiting model-intrinsic vulnerabilities like hallucination, prompt injection, and data poisoning, these models are susceptible to system-level threats that could compromise grid stability, distort energy market operations, or facilitate the leakage of sensitive operational data. Moreover, integrating LLM workloads into cloud or hybrid architectures necessitates strict compliance with critical standards and emerging governance frameworks like the EU AI Act. While existing surveys address AI security in power systems, general LLM security, and AI in smart grids separately, this paper bridges these threads by providing a unified treatment of LLM-specific risks, power-system deployment constraints, and emerging governance frameworks—a combination not covered in prior surveys. We provide a systematic taxonomy of risks across five dimensions: cybersecurity, privacy, robustness, explainability, and governance. We synthesize technological advances, clarify the complex interplay between LLM failure modes and grid security, and propose a forward-looking research agenda to guide future investigation. This work aims to be an indispensable resource for researchers, utility operators, and policymakers in designing resilient, trustworthy, and compliant AI-enabled energy infrastructures.
The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3-87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s.
Solar tracking systems (STSs) are widely adopted in photovoltaic (PV) installations to increase energy yield by maintaining favorable module orientation relative to the sun’s trajectory. This paper presents a systematic review of STSs from an electrical engineering perspective, focusing on electrical performance, control strategies, and system integration aspects relevant to grid-connected PV applications. Fixed-tilt, single-axis, and dual-axis configurations are comparatively assessed in terms of output power, annual energy yield, influence on I–V and P–V characteristics, and auxiliary power consumption. The analysis emphasizes net energy gain rather than gross energy improvement. Control strategies are classified as open-loop, closed-loop, hybrid, and intelligent approaches. Their impact on tracking accuracy, actuator duty cycles, electrical stability, and coordination with maximum power point tracking (MPPT) algorithms is critically examined. A bibliographic and scientometric analysis is conducted to identify research trends, dominant themes, and existing gaps. The results indicate that single-axis tracking often provides the most favorable balance between energy gain and auxiliary consumption in utility-scale systems, while dual-axis configurations achieve higher absolute yield at increased complexity. The review highlights the need for standardized net-energy evaluation and grid-aware tracking strategies.
The growing demand for energy-efficient urban rail transit has led to the increasing deployment of reversible substations (RS) in traction power supply systems. These substations, equipped with bidirectional converter devices (BCDs), involve high initial costs and complex parameter optimization challenges. This paper presents a coordinated optimization method for BCD-equipped RS using a two-layer model. In the upper layer, the model determines the siting of RS and the capacity of BCD to minimize life-cycle cost (LCC). In the lower layer, it adjusts the control parameters of BCDs to reduce annual operating cost. An improved salp swarm algorithm (ISSA), incorporating Tent chaotic mapping and Levy flight, is developed to solve the model. A case study based on an 18.2 km subway line shows that the optimized configuration reduces overall cost by 5.12% and electricity cost by 10.53% compared with a conventional rectifier system. Moreover, it achieves a 1.19% reduction in electricity cost over a system with fixed control parameters, while maintaining rail potential and catenary voltage within safe limits. These findings demonstrate that the proposed method strikes an effective balance between initial investment and long-term operational benefits, contributing to improved energy efficiency and economic performance.
The increasing penetration of low-inertia renewable energy sources and distributed generation has significantly reduced system inertia, making frequency stability a critical challenge in modern power systems. Traditional Under-Frequency Load Shedding (UFLS) schemes often fail to adapt to varying operating conditions and load behaviors, leading to either insufficient or excessive disconnections. This paper presents an optimization-based UFLS scheme that integrates dynamic simulations in DIgSILENT PowerFactory with Python programming through the Particle Swarm Optimization (PSO) algorithm. The proposed methodology optimizes key UFLS parameters—frequency thresholds, intentional delays, and load-shedding percentages—under different ZIP load model configurations (constant power, constant current, and constant impedance). Simulation results on the IEEE 39-bus test system demonstrate that the type of load model has a significant impact on frequency recovery performance and the total amount of load shed. The constant power model achieved system stability with the lowest load disconnection, whereas the constant impedance model required a greater amount of shedding to restore nominal frequency. The results validate the effectiveness of the proposed optimization tool and highlight the importance of considering load characteristics in UFLS design to enhance operational reliability and resilience in modern power systems.
