Search Results (99)

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

Schmitt Triggers are essential building blocks in noise-resilient systems and are useful in managing switching behavior in low-power designs. Yet, as CMOS technologies scale down, their designs become increasingly challenging. This paper presents a comprehensive investigation into the performance and reliability of multiple Schmitt Trigger topologies across two CMOS technology nodes (180 nm and 45 nm), with a particular focus on transistor sizing and layout optimization through multi-finger transistor structures. A series of pre-layout and post-layout simulations reveal that fingered implementations significantly enhance hysteresis robustness, switching speed, and delay consistency in PVT variations. Notably, post-layout results in 45 nm technology demonstrate remarkable improvements in both speed and power efficiency. This highlights the inadequacy of schematic-level models to predict the true behavior of fingered transistor configurations. Additionally, we explored the implications of finger designs on reliability concerns including electromigration and IR drop to determine the tradeoff between interconnect reliability optimization and internal routing. The findings establish practical design guidelines for optimizing number of fingers based on device width and technology node, offering new insights into layout-aware Schmitt Trigger design for high-performance and area-constrained applications. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

The increasing incorporation of renewable energy sources, such as photovoltaic and wind power, results in considerable variability and uncertainty within modern power systems, thereby complicating load frequency control. Conventional controllers, including PI and PID, often fail to provide sufficient performance in dynamic conditions. This study introduces a Two-Degree-of-Freedom PID (2DOF-PID) controller optimized through the Bee Algorithm (BA) for Load Frequency Control (LFC) in a two-area interconnected power system that includes renewable energy sources. The BA is employed to enhance controller parameters according to two objective functions: the Integral of Time-weighted Absolute Error (ITAE) and the Integral of Time-weighted Squared Error (ITSE). Simulation studies utilizing MATLAB/Simulink are conducted to evaluate the comparative effectiveness of PI, PID, and 2DOF-PID controllers. The results demonstrate that the 2DOF-PID controller consistently outperforms conventional PI and PID controllers in terms of frequency stability. The ITAE optimization of the 2DOF-PID results in a reduction in the ITAE index by more than 95% compared to PI and PID controllers, a decrease in settling time by approximately 40–60%, and a near elimination of overshoot and undershoot. Through ITSE optimization, the 2DOF-PID achieves an error reduction exceeding 90% and ensures smooth convergence with minimal oscillations. The PID controller has slightly improved effectiveness in minimizing tie-line power deviation, whereas the 2DOF-PID demonstrates greater resilience and damping capability in frequency regulation across both regions. The findings confirm that the Bee Algorithm-tuned 2DOF-PID controller serves as a robust and effective approach for frequency management in systems primarily reliant on renewable energy sources. Future research should incorporate multi-objective optimization algorithms that concurrently address frequency and tie-line power variations, thereby providing a more equitable control framework for practical Automatic Generation Control (AGC) operations. Full article

Low-frequency oscillation modes are studied in small-signal angular stability because, if not adequately damped, they can cause power system instability in the event of a contingency. The interconnection and expansion of large power systems has led to the emergence of multiple local and inter-area modes and required new damping control strategies for these modes. The expansion of the use of Phasor Measurement Units in power systems has led to the development of new control strategies such as Wide-Area Damping Controllers (WADCs) that use data from PMUs to dampen low-frequency oscillations. Although the benefits of WADCs are promising, there are challenges in designing a WADC. This paper proposes a two-stage algorithm for the robust design of a WADC for modern power systems. The first stage consists of solving an optimization model and finding the WADC parameters that maximize the damping ratios of all modes of the linearized system model for a set of operating points. The second stage consists of refining the WADC parameters through an iterative algorithm. Cases were studied for a set of IEEE 68-bus operating points through modal analysis and time-domain simulations. The results obtained demonstrated the good performance of the proposed two-stage algorithm compared with an existing WADC design method based on a Linear Quadratic Regulator. Full article

Under the backdrop of low-carbon energy transition, the increasing integration of grid-following (GFL) and grid-forming (GFM) converters into power systems is profoundly altering transient synchronous stability. A critical challenge lies in analyzing synchronous stability in grids with high penetration converters and improving converter control strategies to enhance stability. This paper selects virtual synchronous generator (VSG)-based converters as representative GFM units to investigate synchronous stability and control in hybrid systems with both VSG and GFL converters. To simplify stability analysis, this study proposes a novel distribution scheme of power supplies based on an assessment of the ability of different sources to reshape synchronous stability. Specifically, synchronous generators (SGs) and GFL converters are located in the power sending area, while VSGs are deployed in the power receiving area. Under this configuration, synchronous risk is predominantly determined by the power-angle difference between VSGs and SGs. Subsequently, the mechanism by which voltage stability affects synchronous stability between SGs and VSGs is revealed. Furthermore, enhanced control strategies for both VSG and GFL converters are proposed which adjust their transient active/reactive power response characteristics to enhance synchronous stability between SGs and VSGs. Finally, the theoretical analysis and control strategies are validated through simulations on a multi-machine, two-area interconnected power system. Under the proposed enhanced control strategies for GFLs and VSGs, the first-swing power-angle amplitude between VSGs and SGs is reduced by 60% and 49%. Full article

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

The combined application of Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR), known as Automatic Generation Control (AGC), manages active and reactive power to ensure system stability. This study presents a novel hybrid controller with a (1+PDD²)-(1+TI) structure, optimized using the Mirage Search Optimization (MSO) algorithm. Designed for dual-area power systems, the controller enhances both LFC and AVR by coordinating voltage and frequency loops. MSO was chosen after outperforming five algorithms (ChOA, DOA, PSO, GTO, and GBO), achieving the lowest fitness value (ITSE = 0.028). The controller was tested under various challenging conditions: sudden load disturbances, stochastic variations, nonlinearities like Generation Rate Constraints (GRC) and Governor Dead Band (GDB), time-varying reference voltages, and ±20% to ±40% parameter deviations. Across all scenarios, the (1+PDD²)-(1+TI) controller consistently outperformed MSO-tuned TID, FOPID, FOPI-PIDD², (1+PD)-PID, and conventional PID controllers. It demonstrated superior performance in regulating frequency, tie-line power, and voltage, achieving approximately a 50% improvement in dynamic response. MATLAB/SIMULINK results confirm its effectiveness in enhancing overall system stability. Full article

Load frequency control (LFC) remains a critical challenge in ensuring the stability of modern power grids. The integration of nonlinear dynamics into LFC design is paramount to achieving robust performance, which directly underpins grid reliability. This study introduces a novel hybrid control strategy—a fuzzy fractional-order proportional–integral–derivative (Fuzzy FOPID) controller augmented with a proportional–integral (PI) compensator—for LFC applications in two distinct dual-area interconnected power systems. To optimize the controller’s parameters, the recently developed Catch Fish Optimization Algorithm (CFOA) is employed, leveraging the Integral Time Absolute Error (ITAE) as the primary cost function for precision tuning. A comprehensive comparative analysis is conducted to benchmark the proposed controller against the existing methodologies documented in the literature. Nonlinear elements’ impact on the system stability is also investigated. The investigation evaluates the impact of critical nonlinearities, including governor dead band (GDB) and generation rate constraints (GRCs), on system performance. The simulation results demonstrate that the CFOA-tuned Fuzzy FOPID + PI controller exhibits superior robustness and dynamic response compared to conventional approaches, effectively mitigating frequency deviations and maintaining grid stability under nonlinear operating conditions. Furthermore, the CFOA demonstrates marginally superior convergence and tuning accuracy relative to the widely adopted Particle Swarm Optimization (PSO) algorithm. These findings underscore the proposed controller’s potential as a high-performance solution for real-world LFC systems, particularly in scenarios characterized by nonlinearities and interconnected grid complexities. This study advances the field by bridging the gap between fractional-order fuzzy control theory and practical power system applications, offering a validated strategy for enhancing grid resilience in dynamic environments. Full article

Renewable energy sources (RESs) are increasingly combined into the power system due to market liberalization and environmental and economic benefits, but their weather-dependent variability causes power production and demand mismatches, leading to issues like frequency and regional power transmission fluctuations. To maintain synchronization in power systems, frequency must remain constant; disruptions in the proper balance of production and load might produce frequency variations, risking serious issues. Therefore, a mechanism known as load frequency control (LFC) or automated generation control (AGC) is needed to keep the frequency and tie-line power within predefined stable limits. In this study, advanced proportional–integral–derivative PID controllers such as fractional-order PID (FOPID), cascaded PI(PDN), and PI(1+DD) for LFC in a two-area power system integrated with RES are optimized using the catch fish optimization algorithm (CFA). The controllers’ optimal gains are attained through using the integral absolute error (IAE) and ITAE objective functions. The performance of LFC with CFA-tuned PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers is compared to other optimization techniques, including sine cosine algorithm (SCA), particle swarm optimization (PSO), brown bear algorithm (BBA), and grey wolf optimization (GWO), in a two-area power system combined with RESs under various conditions. Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). In the complex dynamics of a two-area interconnected power system, the results show how robust the suggested CFA-tuned PI(1+DD) control strategy is and how well it can stabilize variations in load frequency and tie-line power with a noticeably shorter settling time. Finally, the results of the simulation show that CFA performs better than the GWO, BBA, SCA, and PSO strategies. Full article

The penetration rate of distributed photovoltaic (PV) in mountainous distribution networks is increasing year by year, and the assessment of distributed PV hosting capacity (PVHC) in distribution networks in mountainous areas is also becoming more and more important. To this end, this paper proposes a robust assessment method for distributed PVHC of flexible distribution networks in mountainous areas. The method utilizes soft open point (SOP) and energy storage to realize the flexible interconnection of distribution networks in mountainous areas, connecting the low-voltage nodes at the end of distribution networks in mountainous areas and improving the overall power quality of distribution networks. Secondly, the output curves of distributed PV output and load demand are analyzed and the distributed PV uncertainty model is drawn, so as to construct a two-layer robust assessment model of distributed PVHC for mountainous flexible distribution networks. Finally, the dual-layer robust assessment model, which cannot be solved directly, is transformed into a solvable mixed-integer linear programming model using the pairwise method, and the effectiveness of this paper’s method is verified by the simulation results of the IEEE 33-node distribution network system. Full article

This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems. Full article

A virtual power plant (VPP) is a potential alternative that aggregates the distributed energy resources (DERs) and addresses the prosumer’s power availability, quality, and reliability requirements. This paper reports the optimized scheduling of an interconnected VPP in a multi-area framework established through a tie-line connection comprising multiple renewable resources. The scheduling was initially performed on a day ahead (hourly basis) interval, followed by an hour ahead interval (intra-hour and real time), i.e., a 15 min and 5 min time interval for the developed VPP in a multi-area context. The target objective functions for the selected problem were two-fold, i.e., net profit and emission, for which maximization was performed for the former and reduction for the later, respectively. Since renewables are involved in the energy mix and the developed problem was complex in nature, the proposed multi-area-based VPP was tested with an advanced nature-inspired metaheuristic technique. Moreover, the proposed formulation was extended to a multi-objective context, and multiple scheduling strategies were performed to reduce the generated emissions and capitalize on the cumulative profit associated with the system by improving the profit margin simultaneously. Furthermore, a comprehensive numeric evaluation was performed with different optimization intervals, which revealed the rapid convergence in minimal computational time to reach the desired solution. Full article

Asynchronous interconnected systems connected by High-Voltage Direct Current (HVDC) transmission lines struggle with frequency support between interconnected areas, increasing the risk of frequency instability and resulting in low efficiency in frequency resource utilization. This study establishes a frequency dynamic analysis model for asynchronous interconnected power grids and develops an HVDC frequency controller for frequency control in these systems. It analyzes the control effects of HVDC frequency under two scenarios: without Automatic Generation Control (AGC) and with AGC. The research conducts an in-depth study on the system stability and frequency control parameter optimization for HVDC-asynchronous interconnected systems, significantly improving the system’s response speed and accuracy under various conditions through parameter optimization. Furthermore, the introduction of AGC demonstrates good adaptability, and a comparative analysis of HVDC frequency control effects under different AGC control modes is conducted. Finally, case studies validate the effectiveness and robustness of the proposed optimization scheme for frequency stability control in asynchronous interconnected systems under various fault scenarios. Full article

With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. Full article