Frequency security is a fundamental requirement for the reliable and stable operation of modern interconnected power systems (IPSs) with high wind power penetration. This paper presents an adaptive intelligent fuzzy-logic-based load frequency control scheme for wind farm-based IPSs (WFIPSs), explicitly designed to regulate system frequency and tie-line power flows under adverse communication time delays (CTDs) and denial-of-service (DoS) attacks. To achieve optimal dynamic performance, the controller parameters are tuned using a multi-objective physics-inspired optimization algorithm, referred to as RIME, which simultaneously minimizes frequency deviations, tie-line power oscillations, and control efforts. The proposed control framework is rigorously evaluated under a wide range of realistic operating conditions, encompassing stochastic wind power variability, parametric uncertainties, domestic and industrial load variations, as well as practical nonlinear constraints such as generator rate constraint, valve saturation, and governor dead band. Extensive simulation studies demonstrate that the proposed strategy provides superior robustness, faster frequency recovery, and enhanced resilience to cyber-attacks and CTDs compared with conventional approaches. Furthermore, to validate scalability and real-world applicability, the proposed method is benchmarked on IEEE-39 bus and IEEE-118 bus test systems, confirming its effectiveness for large-scale power networks.

Wind power producers (WPPs) face considerable profitability challenges in deregulated electricity markets due to the stochastic and volatile nature of wind generation and price signals. To address these challenges, this paper proposes a risk-aware decision-making framework that coordinates wind power generation with energy storage systems (ESS) for profit maximization under severe price uncertainty. The framework is built on information gap decision theory (IGDT), a non-probabilistic approach particularly suited for environments with limited historical data and unknown uncertainty distributions. A hybrid energy sale strategy is employed, wherein a portion of wind power is directly sold in the market while the remainder is stored and dispatched strategically according to prevailing market conditions. Three mathematical formulations are developed: (i) a deterministic model to establish baseline profits, (ii) a robustness model to quantify tolerance against adverse price deviations, and (iii) an opportuneness model to capture potential gains under favorable deviations. These models incorporate realistic operational constraints, including ESS dynamics, wind generation limits, and market bidding requirements. Case studies using real-world wind and price data demonstrate the efficacy of the proposed IGDT-based approach. The results reveal the trade-off between robustness and profitability, and the efficient frontiers generated provide strategic insights into decision-making under uncertainty. The robustness analysis showed that the system could tolerate profit reductions down to $15,133.81 under adverse deviations, while the opportuneness model identified profit improvements up to $46,777.22 in favorable scenarios. Overall, this study offers a comprehensive, risk-aware framework for enhancing the economic resilience of WPPs in competitive electricity markets.

The increasing penetration of renewable energy sources (RESs) into modern power networks poses a serious challenge to the stability of the system. The intermittent nature of RES output, coupled with unpredictable load demand, can lead to undesirable frequency deviations. To mitigate these deviations, an intelligent, robust, and efficient load frequency control (LFC) strategy, supported by energy storage systems (ESSs), is essential for effective disturbance rejection in RES- integrated power systems. This work aims to design a novel cascade 2DOF (FOPI)-(1+FOPD) controller for LFC in multi-source power systems, considering practical nonlinearities such as governor dead-band (GDB) and generation rate constraints (GRC), along with the presence of RESs and ESSs. Since the controller's performance is highly sensitive to its parameter settings, the Starfish Optimization Algorithm (SFOA) is employed to fine-tune the controller parameters. The superiority of the 2DOF(FOPI)-(1+FOPD) controller is established by comparing its performance against conventional PID, fractional-order PID, 2DOF-PID, 2DOF-FOPID, and recently reported controllers from the literature. Further, the performance of the SFOA is compared to the state-of-the-art optimization methods, such as the Grey Wolf Optimization, Black Widow Optimization Algorithm, and Whale Optimization Algorithm. Results confirm that SFOA outperforms these optimization methods, while the 2DOF(FOPI)-(1+FOPD) controller exhibits greater dynamic response compared to all benchmark controllers. Additionally, it is observed that incorporating ES systems improves system performance by minimizing over/undershoot, and settling time. Finally, IEEE-39 bus based validation as well as sensitivity analysis shows the scalability and robustness of the suggested controller.

Meeting net-zero emissions worldwide by 2050 will require breakthrough energy systems, and hydrogen (H2) will be a key clean energy fuel. The dynamics of production, transport and end-use are, however, limited by the issues of storage, which is central to efficient performance of production and transport. This point of view reviews the existing H2 storage technologies, including compressed gas, cryogenic liquids, and solid-based systems, and identifies their technical, economic, and safety shortcomings. Recent developments in nanostructured materials, cryo-compressed systems, and hybrid storage are described, in addition to what is known in material discovery using artificial intelligence (AI). The perspective also highlights the importance of policy, market forces, and international cooperation in surmounting deployment-related barriers. This contrasts with assertions of H2 as the missing link in the H2 economy, where storage is the enabler of the system, making it the pivoting point rather than H2. Busting the H2 storage barrier is an exercise that goes beyond the technical realm to the very existence of a carbon net-zero future.

This work puts forward an intelligent cascade control approach for frequency regulation of a low-inertia interconnected power system (IPS) under denial-of-service (DoS) attack and time delay (TD). The considered IPS incorporates a thermal power plant, solar and wind-based renewable energy source (RES), and a virtual synchronous generator (VSG) based composite energy storage unit. The proposed cascade controller combines a fuzzy-proportional-derivative plus integral (F-PD+I) controller in the outer loop and a PI controller in the inner loop. Adynamic model of the considered IPS with all practical nonlinearities and physical constraints is deduced in this work. Moreover, aphysics-inspired optimization termed as Fick’s law algorithm (FLA) is used to obtain the parameters of the proposed control approach. Extensive simulation with parametric uncertainty-based robustness assessment replicates the effectiveness of the proposed approach for enhancing the dynamic stability of IPS. Comparative assessment with existing control structure demonstrates that the proposed cascaded control scheme effectively mitigates the impact of cyberattack and load frequency control (LFC) challenges in the considered low-inertia IPS. Finally, anIEEE-118 bus system is employed to test the proposed control over a more realistic power system structure.

The net-zero transition cannot be coordinated entirely from the cloud. As energy systems become increasingly distributed, dynamic, electrified, and cyber-physical, the distance between data generation and decision-making is emerging as...

Hydrogen (H2) is the only fuel that can propel transportation to a sustainable future. By tapping the energy potential of H2, we can lead the transition to greener, cleaner, and significantly more efficient mobility across every corner of the planet. This paper explores the potential of H2 as a game changer in road, rail, air, and marine transportation systems. H2, the most energy-rich and highly abundant component in the universe, is harnessed via various schemes, each having distinct carbon footprints, ranging from natural gas-driven blue hydrogen to green-powered electrolysis. In the transition from theory to real-world applications, digital infrastructure, vehicle connectivity, and the H2 supply chain become vital. This article aims to highlight H2 as a pinnacle of energy transport, its roles in emission-free mobility, as well as storage and distribution challenges, and to underscore the near- and long-term goals to achieve zero-carbon transportation.

Nowadays, numerous renewable energy sources (RESs) have been integrated into autonomous microgrids (A mu Gs) to reduce carbon emissions and promote sustainable energy. This study presents an approximate model of an A mu G comprising solar photovoltaic, wind turbine generator, biodiesel, and biogas turbine generators as primary sources, along with aqua electrolyser, reformer, and fuel cell (FC) as energy storage elements. The proposed A mu G operates fully on RESs, aiming for a transition to an emission-free future. Surplus renewable energy is converted to hydrogen via an electrolyser, while excess biogas is reformed into hydrogen and stored for later use. During peak demand, hydrogen is utilised through the FC to generate electricity. For efficient load frequency control, an intelligent fuzzy-driven tilt integral derivative with filter (F-TIDF) controller, optimised using a novel electric eel foraging optimisation algorithm, is proposed. Its performance is validated against five advanced controllers and tested on the IEEE 39-bus system.

This article presents a cascaded multistage control architecture termed as PID with filter in combination with one plus proportional-derivative (PIDn-(1+PD)) for frequency regulation of multi-area power systems (PSs) with communication time delay (CTD). To obtain a more realistic design, nonlinearities such as dead bands, rate constraints and CTD of one second in each area are considered. A bio-inspired metaheuristic termed as starfish optimization algorithm (SOA) is chosen to select the optimal solution of the PIDn-(1+PD) controller. To assess the superiority of the proposed control scheme, a comparative analysis with SOA-tuned PID and SOA-tuned PIDn controller is performed. Robust operation of the proposed SOA: PIDn-(1+PD) controller is evaluated under parametric variations of PS’s gain and time constant of each area. Finally, to showcase the performance, overshoot (OS), undershoot (US), settling time (ts) and maximum frequency deviation (Δf) is considered against step load perturbations in each area.

In recent years, government incentives and subsidies have driven significant growth in wind power plant (WPP) capacity, establishing WPPs as key players in the evolving electricity market. However, the inherent variability of wind generation introduces challenges, including potential penalties for deviations in energy output. Energy Storage Systems (ESS) serve as a versatile solution to mitigate the uncertainties associated with wind power variability. This paper proposes a comprehensive model for the joint bidding of WPPs and ESS in competitive electricity markets. The interactions between WPPs and ESS are modeled using a stochastic Cournot framework, accounting for wind power uncertainties through inter-hour ramp constraints. A Location-Based Dual Imbalance Price (DIP) mechanism is adopted for wind-storage systems operating within a network-constrained oligopolistic electricity market. The Nash equilibrium strategy is used to optimize the WPP-ESS bids, considering the competitive behavior of other market participants. A Mixed Integer Linear Programming (MILP) model is developed to determine the Nash equilibrium by evaluating a payoff matrix. Various practical scenarios are analyzed to demonstrate the efficacy of the proposed approach. Furthermore, the study investigates the influence of different imbalance price mechanisms on WPP-ESS profits, market clearing operations, and Nash equilibrium outcomes.

Green hydrogen enables the deep decarbonization of hard-to-abate sectors, supports large-scale renewable integration, and plays a vital role in achieving global net-zero emission targets by 2050 through clean energy transition pathways.

One of the key challenges in interconnected power systems is developing an effective control strategy to mitigate frequency and power deviations caused by the intermittency of renewable energy sources (RESs) and varying load demands. This research introduces an innovative cascade control strategy featuring a PPD controller followed by a PI controller (PPD-PI) for load frequency control (LFC) in a two-area power system with photovoltaic (PV), wind, and thermal reheat power sources. The walrus optimization algorithm (WaOA) is employed to fine-tune the parameters of both the PIDn and PPD-PI controllers, with the goal of minimizing the integral time absolute error (ITAE). The study first applies the WaOA-tuned PID with filter (PIDn) controller to showcase WaOA's effectiveness in LFC, achieving the lowest objective function value of 0.3862, surpassing MFO (0.3921) and GA (0.4127). The robustness of the WaOA-tuned PPD-PI controller is then evaluated under various conditions, including step load disturbances, random load patterns, and parameter uncertainties. The proposed controller achieves significant improvements, with a 36.8% reduction in ITAE compared to the second-best CGO-tuned PIDn-PI controller in Case 2, and a 54.45% reduction in ITAE compared to the second-best COA-tuned PDn-PI controller in Case 3. To further highlight the advantages of the proposed scheme, the analysis also includes nonlinearities such as governor dead band (GDB), boiler dynamics (BD), and generation rate constraints (GRC), along with sensitivity analysis and stability testing under a +/- 25%$$ \pm 25\% $$ change in system parameters. The results strongly demonstrate the superior performance of the WaOA-optimized PPD-PI controller over existing methods.

Electric vehicles (EVs) assist in balancing load demand and power generation by serving as flexible energy storage units. The unpredictable behaviour of EV owners and the limited capacity of individual EVs have led to the concept of EV aggregators, which aim to boost EV involvement in the ancillary services market. The use of EV aggregators in frequency control operations can lead to time-varying delays in load frequency control (LFC) systems. Since the performance of the controller relies on its configurations, these configurations must be optimally designed to achieve improved outcomes in an LFC system with dynamic delays. Therefore, physics-inspired optimization called Fick’s law optimization (FLO) is proposed to tune the robust proportional-integral (PI) controller parameters. Additionally, the proposed LFC performance is assessed using integral error indices. This work simulates and analyses single and two-area LFC systems with EV aggregators and dynamic delays. Results demonstrate that the suggested scheme minimizes frequency fluctuations compared to other controllers. Furthermore, the FLO-based PI controller notably reduces overshoot and settling time of frequency changes.

Significant climatic change is a really difficult task that affects people all across the world. Rainfall is considered one of the most significant phenomena in the weather system, and its rate is one of the most crucial variables. To develop a prediction model by standard approaches, meteorological experts attempt to detect the atmospheric attributes such as sunlight, temperature, humidity and cloudiness etc. Machine Learning (ML) techniques are recently more evolved which provides results that are more satisfactory than those of traditional methods and are simple to use. This paper presents the ML classifiers such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light Gradient Boost Machine (LGBM), Cat Boost (CB), and Extreme Gradient Boost (XGB) to predict the rainfall using feature engineering framework. The Area Under the Receiver Operating Characteristic (AUROC) curve and the other statistical indicators such as recall, accuracy, precision, and Cohen Kappa are employed to predict and compare the success rate of the above-mentioned approaches. The validation results of the models in terms of AUROC values are XGB (0.94) > CB (0.93) > LGBM (0.87) >RF (0.93) >DT (0.88) > LR (0.78). Conclusively, the XGB model outperforms the other models in terms of statistical parameters.

Cross-boundary power systems are more susceptible to cyberattacks, especially time-delay attacks (TDAs), which can disrupt system functioning by delaying communications. This paper offers a resilient two-degree-of-freedom internal model control (2DOF-IMC) strategy to reduce the negative impact of TDA on power system stability. This controller provides an exceptional level of disturbance rejection and reference tracking under attack conditions. To design the proposed controller, the higher-order plant dynamics are reduced by Hankel-based model order reduction approach. The effectiveness of the proposed 2DOF-IMC method is demonstrated via simulation experiments on an interconnected power system for different TDA scenarios. Comparison with conventional IMC (C-IMC) reveals advanced strength and improved frequency control mechanism. The developed framework reduces time delay destabilizing influences, guaranteeing proper functioning of the system during hostile cyber-attack events. The evidence supports the use of resilient control technologies for defending modern power grids from these TDAs. This research provides a significant step toward enhancing the security and stability of interconnected power systems in the face of evolving cyberattack landscapes.

The research conducted for this work has focused on the design and implementation of resilient fractional-order fuzzy integral tilt derivative with filter (FOF-ITDF) controller for renewable-dominated hybrid power system (HPS). The considered HPS employs solar thermal, wind power, diesel engine, aqua electrolyzer, and fuel cell as the distributed generation system. Moreover, flywheel, battery, and ultra-capacitor (UC) are chosen as the energy storage components. The parameters of FOF-ITDF controller are tuned via a hybrid optimization termed as artificial gorilla troops optimizer (GTO) with gradient-based optimizer (GBO). This proposed controller shows enhanced performance over fuzzy proportional–integral–derivative (PID), fuzzy ITDF, and FOF-PID controllers in terms of dynamic as well as steady state. Proposed GTO-GBO: FOF-ITDF controller provides highly robust operation against rate constraint non-linearities, parameter variations, and communication time delays (CTDs). Finally, the performance assessment of the proposed control architecture is also benchmarked over real-power system data via a standard IEEE-14 bus system with its stability evaluation by bode plot. By utilizing the proposed control design, the maximum frequency deviation (Δ f) in the worst situation, i.e., CTD of 0.3 s, is − 0.024 Hz, which is much less and under the admissible range of IEEE standard.

In this study, a demand-contributed load frequency control (LFC) strategy is proposed for frequency stabilization in a solar-wind-based autonomous microgrid system (AMGS). The proposed control framework employs a structurally enhanced version of the classical proportional-integral (PI) controller, augmented with a one plus derivative filter (PI-(1 + DF)) scheme. To optimize the controller parameters, a physics-inspired metaheuristic technique known as the Fick's Law Optimization (FLO) is implemented. This controller is designed to address the complex dynamics and uncertainties of the AMGS, which comprises renewable sources (solar and wind), conventional diesel engine generator (DEG), and flexible demand-side contributors such as electric vehicles (EVs), heat pumps (HPs), and freezers. Furthermore, realistic nonlinearities like governor dead band (GDB) and generation rate constraints (GRC) are incorporated into the model to ensure practical relevance. Comparative analysis reveals that the FLO-optimized PI-(1 + DF) controller significantly outperforms recent state-of-the-art algorithms such as the Mine Blast Algorithm (MBA) and the Sine Cosine Algorithm (SCA) in terms of settling time, peak overshoot, and various objective functions. Simulation results conducted in MATLAB/Simulink confirm the efficacy and robustness of the proposed approach, successfully maintaining frequency deviation within acceptable limits even under severe disturbances. Furthermore, robustness tests with ± 50% parametric variations demonstrate the controller's resilience and adaptability in highly uncertain environments. The peak overshoots (Hz) for a ± 50% variation in MG parameters are 0.02, 0.05, and 0.06, while the corresponding undershoots (Hz) are - 0.957, -0.72, and - 0.48. Similarly, for variations in the droop constant (R) the overshoots (Hz) are 0.074, 0.065, and 0.064, and the undershoots (Hz) are - 0.724, -0.725, and - 0.729, respectively.

This study presents the design of a robust control strategy utilizing an internal model control (IMC)-based proportional integral-one plus derivative with filter (PI-(1+DF)) controller to mitigate the impact of time-delay attacks (TDAs) on renewable-integrated power system (PS). Before implementing mitigation, an adaptive recursive least square filter with a forgetting factor (ARLS-FF) is employed as an online detection mechanism for TDAs. The design methodology incorporates Kharitonov's stability theorem to identify the worst-case plant, for which the proposed controller parameters are obtained using the IMC framework. A critical feature of IMC structure is the filter coefficient $({\mu })$ , which is determined based on the maximum inverse sensitivity ${(\boldsymbol {M}_{\boldsymbol {T}}}\boldsymbol {)}$ and the characteristics of considered TDA, underscoring the proposed control scheme's enhanced efficacy. Stability under time-varying attacks is rigorously analyzed through the Lyapunov-Krasovskii function (LKF) and linear matrix inequalities (LMIs) based approach with vivid robustness assessment ensuring resilience to parametric uncertainties. Finally, benchmarking on the IEEE-39 and IEEE-118 bus systems validates the proposed controller's superiority on large-scale and realistic interconnected PSs. Note to Practitioners-In PS operations, TDAs are among the most serious cyber threats, with the potential to destabilize the system and even cause blackouts in extreme cases. As a result, TDAs have become a growing focus in PS research. This study introduces IMC-based robust load frequency control (LFC) designed to counteract TDAs, thereby improving the effectiveness of current LFC techniques. The controller is designed for a TDA of 2s but demonstrates robust performance up to 4s of TDA. The proposed IMC-PI-(1+DF) control architecture's effectiveness is evaluated under various practical scenarios, including constant TDAs, stochastic time-delayed cyber-attacks (STCAs), parametric uncertainties, nonlinearities, step load perturbations (SLPs), renewable energy (solar and wind) integrations, and in a realistic interconnected PS setting, exemplified by the IEEE-39 and IEEE-118 bus systems. Results confirm that the proposed robust control scheme maintains frequency stability within the permissible range under these diverse conditions, underscoring its applicability in practical PS engineering.

Due to the presence of Denial-of-Service (DoS) attacks and communication time delay (CTD), frequency regulation of a thermal and wind plants-based hybrid power system (HPS) with wind power fluctuations and load variations may not be guaranteed, and in the worst situation overall system may be destabilized. This paper addresses a robust proportional-integral (PI) controller to compensate for the impact of CTD and for the first time, an intelligent fuzzy logic-assisted ratio control-based virtual inertia (VI) is implemented for handling the DoS attacks. The controller's design is carried out using physics-inspired optimization called Fick's law optimization (FLO), where Kharitonov's theorem is used to obtain the maximum and minimum bounds of the controller. The effectiveness of proposed control design is assessed by considering the various practical scenarios such as cyber-attacks, parametric uncertainties, varying CTD, stochastic wind power fluctuations, industrial and domestic loads, step load perturbations and the presence of system nonlinearities such as generator dead band (GDB), valve limits and generator rate constraint (GRC). Moreover, for the stability assessment of the proposed control design, maximum sensitivity $( {{{{\bm{M}}}_{\bm{S}}}} )$ based stability evaluation is employed. Finally, the controller performance is also evaluated upon a multi-machine interconnected IEEE-39 bus system.