IFAC-PapersOnLine最新文献

Traditional passive-assist rehabilitation, using a fixed trajectory, often falls short in engaging patients with residual muscle strength, leading to substandard outcomes. This work proposes a novel ‘assist-as-needed’ control strategy for a pediatric lower-limb exoskeleton system. It employs variable admittance control (VAC) based on the neuro-fuzzy inference technique, encouraging realistic subject-exoskeleton interactions and participation. Thereafter, a robust adaptive backstepping sliding mode (ABSM) control with a rapid reaching law (RRL) for gait tracking is introduced. The stability of the proposed position control is established using carefully selected Lyapunov candidate functions. Through numerical simulations, we compare our approach (ABSM-VAC) with an adaptive backstepping-fixed admittance control (AB-FAC), demonstrating improved tracking, compliance, and safety in active-assist gait training. This innovative method can significantly enhance rehabilitation outcomes, particularly for those with partial muscle strength, providing a more engaging and effective therapy experience.

The commercial and industrial microgrid (CIM) based on renewable energy sources is a solution to the environmental problems. This paper examines CIM based on renewable energy sources connected to the power system by one power transmission line. The power flow from the power system and the limits of its deviations are determined in advance. Compliance with the limits is a basic condition when design of daily load profiles in CIM, because this guarantees uninterrupted power supply from the power system at normal prices. The research is aimed at solving the problem of generating daily load profiles for CIM. The problem is solved using the resources of active consumers registered in the demand management program based on the “load shifting” technique and considering the resources of energy storage systems. An algorithm for generating daily load profiles is developed, which allows the use of various methods for calculating the optimal allocation of shifted load. The proposed algorithm was tested on a 6-node circuit. The resulting daily load profiles satisfy the specified conditions.

This study employs a Mixed-Integer Linear Programming model to optimize energy transactions in an energy community. This research promotes energy sharing through grid and peer-to-peer transactions by incorporating energy storage systems. The article also integrates fairness metrics to evaluate economic equity. The case study examines households with varying equipment acquisition power, highlighting the impact of pricing structures on economic fairness. Analyzing optimal results, the study focused on a community of ten households, six of which are prosumers with PV and energy storage of different capacities. Results show that combining peer-to-peer transactions and high-capacity PV and storage can lead up to a 183% improvement in energy bills. On the other hand, members participating in energy sharing without PV and storage within the community only achieve ~13% improvement in their energy bill, clearly showing a disparity in cost savings linked to equipment capacity acquisition.

This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions’ concentrations to the equilibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions’ concentrations and be further used for SoC accurate estimation.

In this article, an open-source expansion planning tool for energy hubs, based on the Python library PyPSA, is presented. The expansion planning optimization uses an operational optimization based on hourly timeseries. The energy hub model contains detailed energy generation, conversion, conditioning, and storage components for electricity, heat, and various chemicals, including CH₄, H₂, and CO₂. For heat, multiple temperature levels are considered. As an example, the tool is used to optimize the expansion planning for a Waste-to-Energy CHP plant and district heating (DH) network in Germany. Techno-economical conditions for 2023 and projected conditions for 2050 are used as inputs. Results are analysed using timeseries and frequency analysis. This provides insight in how each planned component should be used to optimize revenues. The analyses show the importance of using hourly timeseries as input, as certain processes are operated in close alignment with periodic fluctuations in electricity prices or heat demand. Parameters are varied to evaluate the robustness of the investment decisions. The optimization results show that the studied CHP plant could transform to an energy hub, benefiting from synergetic effects when coupling multiple energy sectors using Power-to-X technologies. The tool can be used to assist operators in their investment planning to shape the future of CHP sites in Europe.

The rapid deployment of renewable energy resources has led to the widespread use of power electronics in modern power systems. As these systems transition from being dominated by large synchronous machines to increasingly incorporating inverter-based resources (IBRs), traditional transmission simulation tools that do not model the dynamics of distribution networks with a large amount of Distributed Energy Resources (DERs) are becoming inadequate. Addressing this challenge, this paper introduces a scalable phasor-domain transmission and distribution (T&D) co-simulation framework that accurately captures system dynamic behaviors under various configurations of grid-forming and grid-following inverters based on open-source software. The main focus is on the validation of this co-simulation framework against the PSCAD Electromagnetic Transient (EMT) analysis tool for a three-phase line-to-ground fault scenario. The validation results clearly demonstrate the framework’s high fidelity and a computational time speed-up of 60 to 100 times, marking a pioneering validation effort between phasor-domain and EMT simulation in T&D co-simulation research.

This paper describes a digital sliding mode control (SMC) technique applied to a three-phase four-wire rectifier operating at a fixed frequency for ultra-fast charging of electric vehicle (EV) battery. The control algorithm employs three decoupled sliding mode controllers to achieve loss-free resistor (LFR) behavior in each phase for power factor correction (PFC). The design of the sliding mode controller is twofold. The first one is to guarantee convergence of the sliding variable to zero. The equivalent control and the discrete-time dynamic model of the rectifier are obtained by imposing sliding-mode regime in discrete-time. The second one is to stabilize the inner-loop under the obtained control law. Theoretically, the resulting inner-loop is stable with a deadbeat behavior in the inner current loop. The results are validated by numerical simulations using on a 350 kW AC-DC rectifier for EV battery ultra-fast charging applications. The numerical simulation results performed on the switched model implemented in PSIM© software are in close agreement with the theoretical analysis.

The integration of renewable energy sources into the power system is an important step towards a sustainable energy transition. This transition could subsequently introduce substantial variability that critically impacts key operational parameters, such as frequency and voltage. This variability poses significant challenges, especially within microgrid configuration, both in grid-connected and isolated modes. Therefore, ensuring the stability of these parameters I paramount of their operational efficiency, reliability, and longevity. Despite these challenges, recent advancements in the field have led to the development of numerous advanced methodologies and control strategies designed to mitigate the impact of renewable sources on microgrid frequency stability. This paper provides a comprehensive overview of these state-of-the-art technologies and methodologies, including cutting-edge technologies such as adaptive load frequency control and Time-series prediction-based approaches. It offers insights into their application, effectiveness, and advantages in frequency control during microgrid operation, contributing to the ongoing discourse on integrating renewable energy sources with enhanced grid stability. Moreover, the discussion section provides insights and barriers impacting the implementation of these technologies in the current microgrid system and the power grid in general.

This paper discusses a fractional-order controller applied to an AC/AC matrix converter when it is driving a Permanent Magnet Synchronous Motor (PMSM). The process of design and simulation of the controller is shown. A numerical simulator composed of detailed models of the main components AC/AC Matrix Converter, PMSM, power electronics and control system are employed to probe the performance of the controller. The simulation was carried out in MATLAB/Simulink. Simulation results show the effectiveness of the controller to reduce current and voltage harmonics of the AC/AC matrix converter. These simulations were performed using changes on velocity and also with balanced and unbalanced input voltages.

In this paper, the control problem of a single-phase, grid-tied photovoltaic (PV) system consisting of a three-level neutral point clamped (3L-NPC) inverter and an LCL filter is considered. The proposed controller aims at achieving threefold control objectives: (i) guaranteeing the PF correction (PFC) objective by enforcing the current of the grid to track a sinusoidal reference signal in phase with the grid voltage; (ii) controlling the DC bus capacitor voltages to follow a reference value provided by the maximum power point tracking (MPPT) algorithm to achieve the maximum available PV power; (iii) ensuring a balanced power exchange through the regulation of the two input capacitor voltages to the same values. To achieve these aims, a multi-loop nonlinear controller is synthesized, including three control loops, specifically the inner PFC requirement loop designed through the integral backstepping approach, the outer voltage control loop formulated using a filtered proportional integral (PI) regulator, and the power balance loop built up using a PI regulator. The control problem under consideration entails several difficulties as the existence of numerous state variables that are inaccessible to measurements. This challenge is addressed by adding a high-gain observer to estimate the current of the solar panels. The performance of the proposed controller against climate changes is demonstrated via numerical simulations using the MATLAB/Simulink platform.