Search Results (613)

As grids decarbonize and end-use sectors electrify, the rapid penetration of artificial intelligence (AI) and hyperscale data centers reshapes the electrical load profile and power quality requirements. This leads not only to higher consumption but also coincident demand in constrained urban nodes, steeper ramps and tighter power quality constraints. The article investigates to what extent a compute-additionality covenant can reduce resource inadequacy (LOLE) at an acceptable $/kW-yr under realistic grid constraints, tying interconnection/capacity releases to auditable contributions (ELCC-accredited firm-clean MW in-zone or verified PCC-level services such as FFR/VAR/black-start). Using two worked cases (mature market and EMDE context) the way in which tranche-gated interconnection, ELCC accreditation and PCC-level services can hold LOLE at the planning target while delivering auditable FFR/VAR/ride-through performance at acceptable normalized costs is illustrated. Enforcement relies on standards-based telemetry and cybersecurity (IEC 61850/62351/62443) and PCC compliance (e.g., IEEE/IEC). Supply and network-side options are screened with stage-gates and indicative ELCC/PCC contributions. In a representative mature case, adequacy at 0.1 day·yr⁻¹ is maintained at ≈$200 per compute-kW-yr. A covenant term sheet (tranche sizing, benefit–risk sharing, compliance workflow) is developed along an integration roadmap. Taken together, this perspective outlines a governance mechanism that aligns rapid compute growth with system adequacy and decarbonization. Full article

High-renewables grids are planned in min but judged in milliseconds; credible studies must therefore resolve both horizons within a single model. Current adequacy tools bypass fast frequency dynamics, while detailed simulators lack multi-hour optimization, leaving investors without a unified basis for sizing storage, shifting demand, or upgrading transfers. We present a two-layer Hierarchical Model Predictive Control framework that links 15-min scheduling with 1-s corrective action and apply it to Germany’s four TSO zones under a stringent zero-exchange stress test derived from the NEP 2045 baseline. Batteries, vehicle-to-grid, pumped hydro and power-to-gas technologies are captured through aggregators; a decentralized optimizer pre-positions them, while a fast layer refines setpoints as forecasts drift; all are subject to inter-zonal transfer limits. Year-long simulations hold frequency within ±2 mHz for 99.9% of hours and below ±10 mHz during the worst multi-day renewable lull. Batteries absorb sub-second transients, electrolyzers smooth surpluses, and hydrogen turbines bridge week-long deficits—none of which violate transfer constraints. Because the algebraic core is modular, analysts can insert new asset classes or policy rules with minimal code change, enabling policy-relevant scenario studies from storage mandates to capacity-upgrade plans. The work elevates predictive control from plant-scale demonstrations to system-level planning practice. It unifies adequacy sizing and dynamic-performance evaluation in a single optimization loop, delivering an open, scalable blueprint for high-renewables assessments. The framework is readily portable to other interconnected grids, supporting analyses of storage obligations, hydrogen roll-outs and islanding strategies. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

Matroid is a mathematical structure that extends the concept of independence. The fuzzy graphic binary matroid serves as a generalization of linear dependence, and its properties are examined. Power grid networks, which manage the generation, transmission, and distribution of electrical energy from power plants to consumers, are inherently a complex system. A key objective in analyzing these networks is to ensure a reliable and uninterrupted supply of electricity. However, several critical issues must be addressed, including uncertainty in communication links, detection of redundant or sensitive circuits, evaluation of network resilience under partial failures, and optimization of reliability in interconnected network systems. To support this goal, the concept of a fuzzy graphic binary matroid is applied in the analysis of power grid communication network, offering a framework that not only incorporates fuzziness and binary conditions but also enables systematic identification of weak circuits, redundancy planning, and reliability enhancement. This approach provides a more realistic representation of operational conditions, ensuring better fault tolerance and improved efficiency of the grid. Full article

The increasing integration of renewable energy sources (RES) and rising energy demand have created challenges in maintaining stability in interconnected power systems, particularly in terms of frequency, voltage, and tie-line power. While traditional load frequency control (LFC) and automatic voltage regulation (AVR) strategies have been widely studied, they often fail to address the complexities introduced by RES and nonlinear system dynamics such as boiler dynamics, governor deadband, and generation rate constraints. This study introduces the Arithmetic Optimization Algorithm (AOA)-optimized PI(1+DD) controller, chosen for its ability to effectively optimize control parameters in highly nonlinear and dynamic environments. AOA, a novel metaheuristic technique, was selected due to its robustness, efficiency in exploring large search spaces, and ability to converge to optimal solutions even in the presence of complex system dynamics. The proposed controller outperforms classical methods such as PI, PID, I–P, I–PD, and PI–PD in terms of key performance metrics, achieving a settling time of 7.5 s (compared to 10.5 s for PI), overshoot of 2.8% (compared to 5.2% for PI), rise time of 0.7 s (compared to 1.2 s for PI), and steady-state error of 0.05% (compared to 0.3% for PI). Additionally, sensitivity analysis confirms the robustness of the AOA-optimized controller under ±25% variations in turbine and speed control parameters, as well as in the presence of nonlinearities, demonstrating its potential as a reliable solution for improving grid performance in complex, nonlinear multi-area interconnected power systems. Full article

As states within the United States respond to future grid development goals, there is a growing demand for reliable and resilient nighttime generation that can be addressed by low-cost, long-duration energy storage solutions. This report studies the potential of including concentrating solar power (CSP) in the technology mix to support California’s goals as defined in Senate Bill 100. A joint agency report study that determined potential pathways to achieve the renewable portfolio standard set by the bill did not include CSP, and our work provides information that could be used as a follow-up. This study uses a capacity expansion model configured to have nodal spatial fidelity in California and balancing-area fidelity in the Western Interconnection outside of California. The authors discovered that by applying current technology cost projections CSP fulfills nearly 15% of the annual load while representing just 6% of total installed capacity in 2045, replacing approximately 30 GWe of wind, solar PV, and standalone batteries compared to a scenario without CSP included. The deployment of CSP in the results is sensitive to the technology’s cost, which highlights the importance of meeting cost targets in 2030 and beyond to enable the technology’s potential contribution to California’s carbon reduction goals. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

Under the high penetration of new energy, multi-machine interconnected grid-connected inverters (GCIs) are prone to lose stability due to the interaction with the power grid. To enhance their adaptability to the grid, a stability improvement method for multi-machine interconnected GCI systems with flexible control bandwidth design is proposed. First, based on the design principles of the converter control bandwidth, the transfer function models of the voltage outer loop and current inner loop are constructed, and the inner and outer loop control bandwidths that meet the requirements are designed. Secondly, to mitigate the adverse effects caused by the interaction of control bandwidths among multi-machine interconnected inverters, By analyzing the influence mechanism of the inner and outer loop bandwidths on the stability of the multi-machine interconnection system under different ratios, a flexible control bandwidth allocation scheme for different inverters in the multi-machine interconnection scenario is designed., thereby enhancing the stability of multi-machine-interconnected GCI through differentiated control bandwidth design. Unlike methods that add extra control loops, the method proposed in this paper does not require sampling new physical variables or modifying the control structure. Instead, it only necessitates adjusting the controller parameters of the multi-machine interconnection system, specifically the optimized distribution of bandwidth, to enhance the stability of the multi-machine interconnection system. Finally, simulation results are presented to verify the correctness and effectiveness of the proposed control method. Full article

Recently, the demand for electricity in Iraq has increased; therefore, there is an increased demand for generating and transmitting electrical power. Furthermore, Iraq’s electricity grid is interconnected with neighboring countries to supply electricity and compensate for the shortage in energy production. This work investigates the reliability assessment of secondary power transmission systems in the Iraqi Electricity Power Distribution Network (IEPDN). A fault tree analysis (FTA) technique based on fault tracing paths was applied to assess the reliability of the secondary power system in Iraq, due to its ability to perform a computation for all failure probabilities of the network components. The reliability of the Northern Secondary Power System (NSPS) and the Southern Secondary Power System (SSPS) was calculated based on the fault probability index. Findings indicate the reliability of both the Northern and Southern secondary power systems. Therefore, the Southern and Northern power grids can be used to connect Iraq with neighboring countries to meet Iraq’s energy needs. Full article

Distribution networks are facing increasing challenges due to the growing share of renewable energy sources (RESs), particularly because of the volatile nature of the available power. In addition to targeted grid expansion measures, the concept of a dynamic grid topology offers an additional layer of flexibility in the power system. Furthermore, there are concepts to use active coupling methods in distribution grids, such as medium-voltage direct current (MVDC) systems, which enable horizontal power flows between distribution grids and thus active control. This paper investigates the potential of combining dynamic passive and active coupling between two distribution grids. Particle swarm optimization (PSO) is used to determine both an optimized operating point of two MVDC interconnections as well as the most efficient switch configuration within both networks. The goal of the optimization is to reduce both network losses and power exchange between the different voltage levels. To evaluate its potential, various use cases are simulated using a representative feed-in of photovoltaics while considering grid constraints. Individual and combined impacts of dynamic AC switching and DC coupling are compared using a modified IEEE-123 test feeder. The results show a significant optimization potential, especially with an increase in RES penetration within the grid. In the best scenarios, the power losses can be decreased by 33.73% and the power transfer can be reduced by 8.75%. Full article

The types of carbides and their effects on the fatigue failure mechanism in carburized CSS-42L steel were systematically studied in the present investigation. The results indicate that the main carbides in carburized CSS-42L steel are Cr-rich M₂₃C₆ carbides and Mo-rich M₆C carbides. M₂₃C₆ carbides precipitate along grain boundaries and interconnect, forming network carbides. Rolling contact fatigue (RCF) tests reveal that fatigue cracks in CSS-42L steel can initiate both at the contact surface and within the subsurface. During RCF, the spalling of large-sized, networked M₂₃C₆ carbides creates micro-spalling pits on the contact surface, inducing local stress concentration that triggers the initiation of surface cracks. The surface cracks initially propagate perpendicularly to the contact surface and then shift to propagate parallelly to the contact surface, ultimately causing large-scale spalling of the surface layer. Subsurface cracks initiate at a position approximately 100 μm below the contact surface, with their propagation direction roughly parallel to the contact surface. Meanwhile, the development of subsurface cracks can connect with surface cracks, leading to the expansion of surface micro-pitting. Network carbides facilitate the propagation of secondary cracks, leading to the formation of grid-distributed crack networks. Full article

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts. Full article

Electric power systems are increasingly becoming more decentralized. Many communities depend on isolated power systems that operate independently of the main grid. Remote, islanded, and isolated systems face challenges due to the intermittency and unpredictability of renewable energy sources. This paper reviews the current status of renewable integration and control in stand-alone power systems. It examines techniques to enhance system reliability through energy storage, hybrid systems, and advanced predictive models. Additionally, the issues related to connecting stand-alone systems, focusing on reliability and renewable penetration, are discussed. The scalability of stand-alone power systems is analyzed based on classifications of small-, medium-, and large-scale systems, highlighting their differences and specific challenges. The South West Interconnected System of Western Australia is used as a case study at a large scale to illustrate the complexities of operating a power system with high levels of rooftop solar and wind units. This paper also reviews various methodologies for modeling the uncertainty associated with these systems, which are categorized into stochastic, fuzzy, hybrid, Information Gap Decision Theory, robust, interval, and data-driven approaches. The advantages and limitations of each method in uncertainty modeling are discussed. Full article