Conference Agenda

Metamaterials are engineered materials with properties beyond what we encounter in nature. They thus allow unique and previously unattainable interactions between waves and matter. The talk will present current research in this area. It will focus on applications to contemporary (5G) and next-generation (6G and beyond) wireless communication systems.
Current wireless systems already use millimetre waves and will probably also use sub-Terahertz waves by the decade's end. These new, high-frequency bands will open exciting venues to develop novel wireless transmission techniques and scenarios. The talk will discuss physical insights into various electromagnetic metamaterial classes, e.g., all-dielectric effective media and resonating meta-atom structures. It will aim at explaining how they can manipulate electromagnetic waves. The presented application examples will include artificial magnetic conductors, antennas, and intelligent reflecting surfaces.
Modern antenna design benefits significantly from the intense development of new optimisation/improvement approaches and algorithms. Therefore, the talk will also explore various metamaterial optimisation strategies and computational electromagnetic modelling methods inherited from the design of conventional microwave devices and systems, e.g. antennas and body area networks. Finally, the presentation will highlight challenges and emerging open questions specific to the synthesis of metamaterials.

The digest presents a free-shape optimization method which simultaneously takes
into account magnetic and mechanical targets. It is suitable to optimize devices such as syn-
chronous reluctance machines simulated in a finite element context. The process is presented
and illustrated on the optimization of a concrete synchronous reluctance machine.

The paper describes an optimization method to design the compensation networks of a wireless power transfer system considering an electrified line with more inductor buried on the road. The Finite Element Analysis is used to compute mutual and self-inductance whereas a genetic optimization algorithm is used to improve the system efficiency and transmitted power in a car moving conditions.

The paper describes a modelling and solving methodology of a {static converter – electric motor – control} system for its sizing by optimisation, considering the dynamic thermal heating of the machine. A co-simulation is set up for the steady state electrical system aspects and the transient thermal aspects. The simulators are coupled through a master-slave relationship with adapted time step management. A sizing by optimisation example of this system is carried out with a different optimisation algorithm according to the design specification.

Optimizing electrical systems represented by ODE and events, using their frequency spectrum is an important issue for designers. This paper presents a methodology to answer to this issue. Using gradient-based optimization algorithm, the paper proposes to simulate the electrical system according time, and then to compute its frequency spectrum. To optimize it by SQP, Automatic Differentiation is mainly used to compute the model gradients.

Magnetic gears can be considered as possible power transmission systems, in substitution of classical mechanical transmissions. They are able to transmit torque, between two mechanical axes, in a contactless way, through the interaction of two coaxial permanent magnets rotors with a set of ferromagnetic poles. The performance of magnetic gears depends on several geometric and material parameters and their sizing can be approached by optimization.
In this work a multi-objective optimization procedure is used to compute the minimum volume encumbrance to meet a value of the transmitted torque. The optimization is based on a constrained single objective approach and carried out by a deterministic optimization strategy. The analysis of the magnetic structure is performed by two dimensional magnetostatic nonlinear magnetic field analysis that is used to evaluate the maximum value of transmitted torque. Optimization loop is managed by a deterministic constrained technique working on the thickness values of the active and passive parts of the two rotor structures.
Results obtained allow to size the device, finding the minimal radial and axial encumbrance of the gear needed to obtain a given value of transmitted torque.

We present a design method for a 3D printed induction heating coil that is optimized by parameterizing the coil path and cross-section. The objective functions of the optimization are the temperature rises in the heated workpiece in the angular and longitudinal directions. The coil path optimization ensures homogeneous heating in the angular direction, while the cross-section optimization enables local heating in the longitudinal direction. We combine the optimized parameters and verify that the final shape can be printed with 3D printers.

We propose an innovative technique for dealing with optimal shape design problems characterised by curved boundaries between ferromagnetic and dielectric sub-regions. The proposed approach relies on the ability of the Virtual Element Method in handling meshes with polygonal elements having curved edges. The well-known TEAM 25 benchmark problem is considered as case study.

Grid reinforcement is coming. Meanwhile, it is essential to manage the power grid optimally to ensure good power quality. Intelligent power grid management by efficiently distributing grid capacity among customer assets in a low voltage distribution grid, taking into account intermittent distributed energy resources and emerging loads, could be an approach.

Power System Optimization Models are tools policymakers and practitioners use to evaluate and plan such systems' short, medium, and long-term evolution. The size and complexity of these models have evolved alongside their real-world counterparts to the point that they pose tractability problems that hinder their usefulness and suitability for extracting actionable insights. One of the main challenges in these models arises from their temporal structure, which makes them harder to solve as it exponentially increases the number of variables in the model; to overcome this, researchers developed temporal aggregation techniques which simplify the temporal structure of the model like representative periods and temporal downsampling, thus increasing the model's tractability. These techniques, however, come at the expense of losing sight of the interaction between short and long-term dynamics that play a critical role in the real world, like ramping or the intra-day behavior of renewable energy sources. In this work, we extend the Basis-Oriented Time Series Aggregation procedure to network flow problems and show how it greatly aggregates the model while maintaining its objective function value.

This study is in the context of the design of multi-energy microgrids under uncertainty, where the objective is to determine optimal sizes for renewable generators and flexibility assets considering stochastic parameters, such as energy demand and renewable energy sources availability.

In particular, we compare existing models to generate synthetic scenarios of these parameters leveraging historical data, e.g. using Markov Chains, probability distributions, and time series analysis. The assessment focuses on their ability to generate diverse scenarios that capture key characteristics of the original data. Additionally, we conduct an application-specific assessment to examine the impact of different scenario generation methods on design optimization. This evaluation utilizes a two-stage stochastic programming approach and a three-year dataset to evaluate performance on unseen scenarios.

Robust design of microgrids is a complex optimization process requiring multiple simulations in order to integrate uncertainty variables associated with the system environment or design models. In this context, having sufficiently accurate models that are compatible with the optimization algorithms and associated computational costs represents a real challenge. In this paper, we illustrate this through the robust design of a simple microgrid with electrochemical storage. Based on battery models that couple energy efficiency and aging, we develop an approach for choosing the right level of precision to match the microgrid's optimization criteria or constraints.

This paper focuses on a methodology developed to evaluate innovative control approaches for optimizing energy management in the context of energy flexibility, rising energy consumption, and thermal discomfort. To allow the development and testing of this evaluation methodology, various architectures of Model Predictive Controllers, which are renowned for their ability to address these challenges effectively, have been implemented. To assess the impact of predictive control on energy systems management optimization, the methodology is based on a multi-objective set of KPIs. Therefore, several performance indicators are defined and implemented to evaluate the controllers' effectiveness. Evaluations are carried out by integrating the controllers with a bottom-up dynamic simulation platform dedicated to district energy calculations, as an emulation tool. This offers, at last, an architecture and a methodology for comparing the performances of controllers, especially model predictive controllers.

Recent studies in Italy have shown that Renewable Energy Communities (RECs) can provide a viable and economically beneficial alternative to traditional energy supply. In addition, the 2019 EU directive mandates that the share of energy from renewable sources will be at least 32% by 2030. As Hungary is currently in the process of developing REC regulations, this work aims to devise an optimal energy management scheme for RECs, tailored to the Hungarian economic and infrastructural environment. The scheme is based on an optimal energy management framework utilizing mixed integer linear programming (MILP) to control a community energy storage system. The scheme is extended to take into account the unique consumption patterns in Hungary, especially the high penetration of controlled loads. In Hungary, controlled loads are managed by the electricity provider through demand-side management techniques, allowing partial control over their operation to effectively manage peak hours. This article highlights the benefits of utilizing load control in REC energy management, increasing the flexibility of the REC and as a result, leading to reduced optimal community battery size. To assess the results, a case study of four low-voltage Hungarian transformer areas is conducted. The optimal ratio of photovoltaic penetration and community battery size is determined, and economic key performance indicators are evaluated to assess the financial viability of these communities.

This talk analyzes different optimization models for evaluating investments in Energy Storage Systems (ESS) in power systems with high penetration of Renewable Energy Sources (RES). First of all, two methodologies proposed in the literature are extended. The enhanced models are the ‘System States Reduced Frequency Matrix' model, and the ‘Enhance Representative Periods’ model which guarantees some continuity between representative periods, e.g. days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as a benchmark. While the system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The enhanced representative period model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

The charging of electrical vehicles is often limited by electrical grid infrastructural constraints: the larger the number of electrical vehicles the higher the value of electrical energy needed to charge them and, in a given time interval, the power requested to the grid. An optimal strategy can distribute over time the charging sessions so that the grid peak power is reduced. The present work defines the constraints of an electrical bus charging system and presents two optimization strategies: one based on heuristic strategy and another on Mixed Integer Linear Programming. The two strategies can be used alternatively or in sequence: the first providing a starting point for the second. The optimal procedures are applied to the case of charging system for a fleet of electrical buses and some preliminary results are presented.