IFAC-PapersOnLine最新文献

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.

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 recent years, last-mile delivery research has shifted from focusing solely on cost minimization to a broader consideration of sustainability and environmental impact. This paper explores the novel trend of evaluating the social impact of last-mile delivery, emphasizing factors such as road safety, congestion, and noise pollution. A key strategy that has garnered attention in addressing these multifaceted concerns is Of-Peak Hours Delivery (OPHD), which involves scheduling deliveries during non-peak hours, often in the evenings or at night. This study investigates implementing a city-wide policy inspired by OPHD, strategically encouraging or discouraging deliveries in specific urban areas and time slots. The objectives of such a policy span from environmental considerations to nuanced social factors, potentially leading to dynamic shifts in time slot preferences. Adopting such a policy aligns with the growing citizen concern for environmental and social issues, leveraging changing attitudes toward sustainable practices. To evaluate this policy, we formalize it as a bi-objective mixed-integer programming model, aiming to strike a balance between the economic interests of retailers and the municipality’s overarching goals. Through realistic instances, the paper offers managerial insights, providing valuable perspectives on the practical effects of the proposed policy on delivery operations. This facilitates a deeper understanding of the ramifications of integrating social considerations into the optimization framework.

The reduction of Green House Gases (GHGs) emissions has been the top priority of the European Union (EU) in recent times. The increase in use of Electric Vehicles (EVs) to combat climate change is viable only with the use of Renewable Energy Sources (RESs) to power them. Since the Paris Agreement in 2015, the integration of RESs into the charging infrastructure has increased in the EU. The increasing market share of EVs in various categories has called for immediate changes in the EV charging infrastructure and in the operation of charging hubs. This paper aims to present an Energy Management System (EMS) to efficiently manage an EV charging hub fed by RESs, reducing the daily costs of operation and GHG emissions. The mathematical model of the EMS is developed in Matlab as a Mixed Integer Linear Programming (MILP) model. The MILP-based EMS is applied to the real-world case of an innovative EV charging hub located in the Ligurian region of Italy and the results of the optimization are reported.

This article introduces a method for enhancing territorial risk management through dysfunctional analysis supported by the SADT tool. It discusses the importance of functional analysis in identifying risks within a territory, emphasizing the need for a comprehensive approach to understanding the various functions and activities within a territory. By applying these principles to emergency response plans the article highlights the use of incidence matrices in risk assessment and process modeling to improve preparedness and response to natural or anthropogenic disasters.

The article delves into modeling the Li-ion cell and the prerequisites for identifying system parameters. Initially, we construct a model that encapsulates the nonlinearity of our cell, employing a PNGV model encompassing dynamic parameters like state of charge (SOC), open-circuit voltage OCV, C_b, R₀, R₁ and C₁. Subsequent to this, pertinent battery discharge tests are conducted to discern the model parameters.

Commencing with establishing the relationship between state of charge (SOC) and the variable open-circuit voltage (OCV) using the least squares method, we progressed. Following determining thise relationship, cell discharge tests were employed to ascertain the values of R₀, R₁ and C₁. Once the various parameters of the PNGV Model are identified, validation of this established battery model is performed via experimental discharge tests. The resulting error indicates a mean absolute error (MAE) of 0.0176V for modeling, alongside a root mean square error (RMSE) of 0.0232V. These outcomes underscore the remarkable accuracy of our cell model.

Increased deployment of distributed renewable energy on distribution networks has become a popular approach in the effort towards decarbonisation. The paper addresses the challenges related to voltage deviation and power loss resulting from higher levels of photovoltaic systems (PV) penetration. Battery Energy Storage Systems (BESS) provide a flexible solution to enhance network performance. The paper develops a novel methodology for the optimal placement and sizing of a set number of BESS units on the network. The methodology is intended to assist Distribution Network Operators (DNOs) in planning the optimal deployment of a specified number of BESS units. The problem is modelled as a mixed-integer linear program with conic relaxation approach. Numerical simulations are carried out to demonstrate the usefulness of the proposed approach with the help of the IEEE 33-bus network utilising 3, 5 and 7 BESS units. The paper examines the BESS requirements and performance as the set number of units deployed is varied.

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.

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.

This paper describes a fully open-source toolchain for the modelling and simulation of phasor-based, balanced 3-phase AC system models, based on the OpenModelica compiler, the PowerGrids 2.0.0 Modelica library, and the open-source solvers IDA, Kinsol, and KLU. The toolchain includes a graphical user interface for model editing and result visualization. A notable feature is the automatic generation and solution of the initial static power flow, which provides good initial guess values for the steady-state initialization of the dynamic model, ensuring robust initialization; this feature can be disabled, and the results of external power flow tools can be used instead. The toolchain currently handles models with up to about 100 buses, making it attractive for teaching and research use, but is planned to be able to handle models with thousands of buses in the near future. This will be achieved thanks to new code generation features currently under development in OpenModelica and will open the way to industrial use.