Search Results (237)

Modern distribution networks increasingly face reverse power flow, heavy loading or overloading, and voltage violations as distributed generation and flexible demand introduce large spatiotemporal variations in active-power injections and withdrawals. This paper proposes a planning-operation consistent DC-AC time-series optimal power flow (OPF) method for flexible-resource planning and operation optimization to mitigate these problems. The DC module optimizes investment decisions with embedded DG curtailment and flexible-load regulation to improve the operational relevance. The AC module further considers resource reactive-power flexibility and optimizes their operation under voltage constraints. The consistent design of the two modules in objective structure, operating constraints, and flexible-resource representation allows the planning results to be parsed as the initial schedule for AC operation refinement, improving operation-optimization efficiency. Furthermore, the model introduces discrete type-and-number BESS planning, endogenous initial state of charge (SOC) optimization, and a unified flexible-load model to improve operability and economic relevance. The method is implemented in a CloudPSS-based DSLab environment and tested on a real distribution feeder and the IEEE 123-node benchmark. The real-feeder case demonstrates coordinated mitigation of reverse-power export, branch overloads, and voltage violations. In the IEEE 123-node benchmark, the 8760 h AC operation case converges in 376.31 s, confirming tractability for long-horizon time-series optimization. Full article

In transitional electricity markets that operate without explicit financial or physical transmission rights but retain priority generation schedules and rigidly executed medium- and long-term (MLT) contracts, network congestion gives rise to settlement imbalance funds that are difficult to allocate fairly. This paper proposes an allocation framework that couples proportional-sharing power-flow tracing with priority-based implicit transmission rights (PBITRs). Flow tracing quantifies each transaction’s contribution to the realized flows on constrained lines, while the priority rank converts dispatch and contract-execution rules into a direction-dependent settlement weight. The clearing inputs are produced by a joint 24-period DC optimal power flow over a modified IEEE 30-bus system, and representative off-peak, peak, and flat periods are recalculated by AC optimal power flow to test the robustness of constrained-line identification, contribution ranking, and allocation shares. In the recalculated case study, the daily congestion-surplus pool is CNY 19,380 and the proposed method assigns 60.6% of it to the MLT A-to-C transaction that physically dominates the constrained corridor, compared with 21.1% under energy-proportional allocation. For the balancing congestion charge pool of CNY 42,492, the fund-specific responsibility set excludes the LMP-settled spot increment and the proposed method assigns 81.2% to the MLT A-to-C transaction. Across the three AC-OPF checks, the constrained-line sets coincide with the DC results at the 0.95 loading threshold, the transaction contribution rankings are identical, and the maximum DC-AC share differences are 1.01 percentage points for congestion surplus and 0.72 percentage points for balancing congestion charge. Sensitivity tests over the priority exponent and loading threshold show smooth and interpretable changes. The framework contributes an ex-post, settlement-neutral tool for markets that clear energy with LMPs before tradable transmission rights are introduced. Full article

Electrohydrodynamic (EHD) actuators offer a promising approach for active particulate matter (PM) control in heavy-duty and marine exhaust systems. However, continuous DC corona discharge often leads to excessive energy consumption and is susceptible to aerodynamic re-entrainment in high-velocity flows. This study introduces an idealized transient advection mechanism combining a macroscopic corrugated duct geometry with high-frequency pulsed EHD actuation. A fully coupled, time-dependent multiphysics model—integrating RANS turbulent flow, Poisson-Nernst-Planck space charge transport, and Lagrangian discrete particle tracing—was developed to analyze the physical kinetics of 0.2 µm soot particles. The results demonstrate that the corrugation troughs act as effective aerodynamic dead zones with partial electrostatic shielding, creating aerodynamic and electrostatic dead zones. During active microsecond voltage pulses (25 kV peak), intense Coulombic forces rapidly overcome turbulent drag, driving kinetic injection of particles into the corrugation troughs. During the resting phase, particles remain securely trapped by aerodynamic shielding, significantly mitigating the risk of aerodynamic re-entrainment under the simulated conditions. A comprehensive parametric analysis revealed that an optimized 500 Hz pulse with a 5% duty cycle maintains a robust 82.7% trapping efficiency. Compared to standard continuous DC precipitators, this pulsed actuation strategy requires an idealized active corona power of 15.3 mW. This study provides fundamental physical insights into transient EHD flows and establishes optimized design criteria for fundamental EHD transport models. Full article

The increasing penetration of direct-current (DC) technologies in power transmission and distribution systems necessitates efficient and robust tools for steady-state analysis. This paper presents a comparative evaluation of the Chebyshev–Halley (CH) family of multipoint iterative methods against the classical Newton–Raphson (NR) method for power flow calculation in monopolar DC networks. Both methods were implemented in MATLAB and tested on four radial test systems of increasing complexity (10, 21, 33, and 69 nodes) under three distinct initialization scenarios: optimal (flat start), adverse ($V^{\left(0\right)}=0.5$ p.u.), and random ($V^{\left(0\right)}\sim \mathcal{U}[0.8,1.2]$ p.u.). Performance was assessed using key metrics including iteration count, CPU time, solution accuracy, and convergence failure rate. The results demonstrate that the cubic convergence of CH consistently reduces the number of iterations by one when compared to NR across all systems. However, this reduction does not translate into computational savings, as CH exhibits median CPU times 1.36 to 2.44 times higher than those of NR, given its higher cost per iteration, which involves solving two additional linear systems. Under adverse starting conditions, both methods converge for the 10-, 21-, and 33-node systems, but CH fails on the 69-node network due to severe Jacobian ill-conditioning, from which NR recovers through an implicit regularization mechanism. Under random initializations, both methods show high failure rates, reaching 100% in the 69-node network. It is concluded that, while CH offers superior convergence order and final accuracy, NR remains more computationally efficient for small- to medium-scale networks under flat-start conditions. The CH family is best justified in high-precision applications or larger networks where the iteration reduction may offset its per-step overhead. Future work should focus on extending CH to meshed and multi-source DC networks, developing quasi-Newton variants to reduce its computational cost, and designing hybrid NR-CH strategies that combine global robustness with local cubic convergence. Full article

Against the backdrop of the transition toward power systems with high shares of renewable energy and power electronics and the rapid growth of urban load, large receiving-end urban grids are fed by multiple line-commutated-converter HVDC (LCC-HVDC) links, so that their maximum receiving capability is frequently limited by the static-voltage-stability margin. To assess the receiving capability of such large urban grids, this paper proposes a method for evaluating the maximum receiving capability of a receiving-end urban grid that incorporates a Modular-Multilevel-Converter-based multi-terminal embedded DC (MMC-MTEDC) system. First, a quasi-steady-state model of the receiving-end urban grid with LCC infeed and an embedded MMC-MTEDC system, in which the DC-network equations characterize the mutual coupling among the AC active-power injections of the receiving-end converter stations, is established. Second, an augmented extended Jacobian that incorporates the MMC control equations and the DC power-flow equations is constructed; its minimum singular value is adopted as the static-voltage-stability index, and the corresponding sensitivities are derived to reveal the mechanism by which the receiving capability is formed. On this basis, a unit-commitment optimization model that centers on the stability-margin constraint and accounts for the converter-capability curve, the bus-voltage limits, and the line-loading limits is built; the model is solved iteratively by a column-and-constraint-generation (CCG) method, and the feasibility of the unit commitment is used to estimate the maximum receiving capability. A modified IEEE 39-bus system is used as a case study, which quantitatively verifies the effectiveness of the MMC-MTEDC in enhancing the receiving capability of the receiving-end urban grid. Full article

To improve the efficiency of TIG (Tungsten Inert Gas) welding, our team developed a novel fast-frequency pulsed twin-TIG welding power source and matched welding procedures to overcome the drawbacks of conventional high-efficiency TIG welding. After parameter optimization, stable, high-efficiency and high-quality welding of 316L stainless steel can be realized. Compared with traditional DC TIG welding, the mechanical properties of joints are greatly improved: the weld grain size is refined by 38% under moderate current, while tensile strength, elongation and microhardness rise by 13.6%, 26% and 10% respectively, which achieves simultaneous improvement in strength and ductility. Numerical simulations were carried out to analyze the evolution of molten pool temperature field and velocity vector flow field. The simulation results are highly consistent with experimental data, which verifies the reliability of the model and lays a foundation for the study of molten pool behavior. Combined with molten pool flow characteristics and weld microstructure, the evolution mechanism of microstructure and texture as well as grain refinement in this welding process is revealed. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

In the context of spot electricity markets, the fluctuation characteristics of node electricity prices play a crucial role in guiding the operational strategies of thermal power plants. However, constrained by the inelastic demand for heat, the strong coupling between electricity and heat in combined heat and power (CHP) units limits their ability to regulate electricity generation. These conditions present considerable difficulties for the economic feasibility and carbon reduction performance of these units, especially with high levels of renewable energy integration and during intensive peak-load shaving operations. In response to these challenges, this paper introduces an optimized dispatch method for renewable energy–electricity–heat coupled systems in thermal power plants with thermal storage, which incorporates the coordinated clearing of nodal electricity prices. First, a spot market clearing mechanism is established based on a DC optimal power flow model, and node electricity price signals reflecting network congestion characteristics are endogenously generated through the Lagrange multiplier of the node power balance constraint. Next, by introducing node injection power as a coupling variable between the grid clearing model and the CHP plant scheduling model, a co-optimization framework with bidirectional feedback between electricity prices and unit output is constructed. In conclusion, the integration of node electricity prices, deep peak-shaving costs, and carbon emission costs into a unified optimization objective leads to the development of a scheduling model for the renewable energy–electricity–heat coupled system, which includes CHP units, thermal storage, and grid interactions. The simulation results show that the proposed method can effectively improve the performance of the electric–thermal coupling system under the condition of a high proportion of renewable energy access. Under the typical daily load and new energy output conditions, the total cost of the system is reduced by about 9.7%, the carbon emission is reduced by about 18.3%, and the peak shaving capacity is increased from 25 MW to 58 MW, thus enhancing the flexible scheduling ability and market adaptability of the heat storage thermal power plant. Full article

Building electrical systems are becoming increasingly complex as designers evaluate AC, DC, and hybrid distribution architectures, integrate distributed energy resources, and maintain alignment with evolving performance and reliability goals. Existing design tools are typically limited, non-interoperable, and unable to support continuous modeling across design phases, resulting in fragmented workflows and significant manual effort. This paper presents a template-based workflow that automates the generation of high-fidelity Modelica simulation models of building electrical systems from semantic models. The workflow supports both basic safety analysis and the power-flow simulation of AC, DC, and hybrid system architectures. A Python-based middleware (RDF2EMO) was developed to automate data extraction, template instantiation, and parametric model generation, enabling rapid and consistent iteration through schematic design, design development, and construction documentation phases. Verification of the middleware automation (RDF2EMO) using a reference medium-sized office building demonstrates that the generated Modelica model is internally consistent with the Building Information Model. A case study demonstrates how the workflow supports design decisions, including system architecture selection, equipment sizing impacts and optimization, and reliability analysis. Full article

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but its strong parameter dependence is a key factor limiting the development of MPC. Therefore, a model-free predictive control (MFPC) method combining an ultra-local model with model predictive control is proposed to solve the problem of strong dependence of traditional MPC on system model parameters. Firstly, establish the ultra-local mathematical model of the DAB converter. The system’s lumped disturbances are identified using the residual prediction method and substituted into the discrete model of the system at the next time step to achieve model-free prediction. Secondly, a minimum back-flow power constraint is added to the cost function to improve the steady-state performance of the converter. Thirdly, in the extended phase shift modulation, the Lagrange multiplier method (LMM) is proposed to reduce the current stress, ultimately achieving the collaborative optimization of the comprehensive performance of the DAB. Finally, a simulation model is built using MATLAB/Simulink, and compared with traditional control methods, the voltage ripple has been reduced by 51.3%, 89.1%, and 85.1%, respectively; the current stress significantly decreases both when the output voltage reference value changes and when the load resistance changes abruptly, and both can basically achieve zero back-flow power operation. The validity and superiority of the proposed strategy have been verified. Full article

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction. Full article

The provision of power and grid services requires the co-ordination between Day-Ahead Market (DAM) and Ancillary Service Market (ASM) to attain reserve services and technically feasible operating conditions for market players and for the network. In this context, this work proposes a multi-stage approach to evaluate the dispatched power to balance the forecast updates of renewable energy sources and load from DAM to ASM, taking into account network and Unit Commitment (UC) constraints. The DAM is solved considering a zonal market framework and neglecting the UC constraints. Then, a mechanism to adjust the ASM bids is developed, defining time-varying costs for each regulation. Finally, the ASM is modelled as a network-constrained UC and economic redispatch (NCUCER) optimization problem, aiming at minimizing the overall cost, in order to procure secondary reserve requirement and to adjust the DAM schedules, taking into account network and UC constraints and balancing forecast updates. DC load flow sensitivity factors are exploited to evaluate the influence of redispatch actions and forecast updates on the observed power flow. This procedure is applied to NREL 118-Bus Test System assessing its performances throughout a yearly time horizon. Full article

Cascaded H-bridge converters are the prevalent option for classic energy storage converters due to their excellent battery integration and current sharing capabilities. However, this scheme requires numerous IGBT switchings and exhibits high losses in low-voltage, high-power applications due to high current flowing through the batteries. Furthermore, the limited DC voltage regulation capability makes it difficult to obtain sufficient DC voltage for modulation when the battery is continuously discharging, resulting in shortened continuous discharge duration. To address these issues, this paper proposes a novel energy storage converter based on controllable DC buses. The proposed controllable DC bus consists of cascaded half-bridges and a bidirectional DC converter, where the former topology is designed to preserve voltage and current balancing between batteries, as well as boost the DC voltage—thereby reducing the current flowing through the batteries and minimizing losses. The latter topology is implemented to maintain DC bus voltage during battery discharge, thereby increasing the continuous operating time of the proposed energy storage converter. Moreover, the control and modulation of the proposed controllable DC bus have been optimized, and its effectiveness and performance are verified through simulation results. Full article

Multi-output, single-switch, hard-switched Pulse-Width Modulated (PWM) converters suffer from high switching losses, which strictly limit their power density. To significantly reduce these losses, this work proposes a novel family of non-isolated multi-output DC-DC converters based on a quasi-resonant, single-switch cell operating in the megahertz (MHz) range. Sixteen configurations are derived to enhance power density and minimize component stress. A comprehensive analysis derives the fundamental analytical expressions for operation, switching conditions, and power flow. These expressions form the basis of a design tool that facilitates parametric component selection and optimization. The developed tool calculates voltage and current stresses, alongside power losses, using RMS current analysis and user-defined parameters such as ESR and semiconductor non-idealities. Finally, experimental results from prototypes operating at approximately 1 MHz in both full-wave and half-wave modes, with step-up and step-down capabilities, confirm the accuracy of the analytical design tool and the simulation model. Full article