The use of Energy Storage Systems (ESSs) is pervasive in many application domains such as e-mobility, robots and drones, renewable energy systems, and micro/nanogrids. ESSs are complex and expensive systems. In addition to the physical storage device, they also encompass Battery Management Systems (BMSs) to ensure optimal operation, as well as suitable AC/DC or DC/DC power electronic converters to process the power flow. One of the current challenges is to identify models of ESSs, in particular for batteries, that strike a balance between accuracy and ease of implementation in real control systems. Therefore, advanced mathematical modelling, physical electrochemical based-models, new simulation approaches to system dynamics, and online algorithms for diagnostics and estimation of State of Charge/Health (SOC/SOH) play a pivotal role in understanding and predicting the behavior of ESSs at short, medium, and long timescales. As such, they are fundamental to devise suitable control systems for BMSs and power converters to ensure adequate ESS performance in terms of efficiency, autonomy, reliability, and safety.
This special session is aimed at presenting the latest advances and developments in ESS technologies of different kind (batteries, supercapacitors, hydrogen storage, flywheels, etc.) with particular reference to advanced modeling, accurate simulation, high-performance power electronic converters, high-performance linear and nonlinear control systems, accurate SOC/SOH estimation methods, and advanced ESS diagnostics.
More sustainable energy sources and effective energy management techniques are being sought for in response to growing environmental concerns on a worldwide scale. Renewable energy sources have less of an influence on the flexibility of conventional generation since they are being used more frequently. New sources of flexibility must be included into the system, such as the demand flexibility offered by demand response initiatives and the utilization of storage.
Distributed storage is a crucial feature of efficient local energy resource management, particularly in energy communities, together with distributed generation, demand response, and electric car management. In the current environment, different optimization techniques, including artificial intelligence (AI), can provide prospective solutions to the challenging energy management problem. The focus of this special session discussion will be on the efficient application of intelligent and/or AI-based solutions in rural, industrial, and urban energy.
Environmental issues and increasing power demand promote the development of AC, DC or AC/DC microgrid (MG) technology. Currently, several nearby MGs can be connected and operated in a coordinated way to form a microgrid cluster (MGC). The key goal of the MGC is to increase the penetration ratio of MGs in the utility grid, and achieve the efficient and stable operation of large-scale renewable energy technologies. The design, control and operation of MGs have been extensively studied but few efforts have been conducted on studying such issues in MGCs. There is, therefore, a need of updating existing techniques and tools to this novel paradigm of MGs based on clusters, as well as investigating novel methodologies, models, control schemes and operational tools, among others.
Moreover, there is currently a growing interest in the so-called multi-energy microgrids (MEMGs) to use clean alternatives such as renewable energy technologies that supply electric loads, natural gas or hydrogen loads. This kind of MGs normally involve the interaction of different subsystems or energy vectors such as electrical, natural gas and hydrogen. The efficiency of a multi-energy system is greater than a system with a single source due to the complementarity of its resources, since electrical, gas and hydrogen systems can take advantage of RETs and low energy costs of the networks. There is a need for the study of MEMGs integrating different energy vectors, such as electricity, hydrogen and gas, and the development, for this kind of MEMGs, of novel methodologies, models, control schemes, operational tools and stability solutions.
Renewable energy sources (RES) have rapidly gained prominence as key contributors to sustainable energy solutions and climate change mitigation. However, their intermittent and variable nature presents challenges for grid stability and effective energy management. Accurate nowcasting, which involves short-term forecasts (up to 30 minutes ahead), is crucial in providing real-time insights into renewable energy generation patterns. By focusing on this critical area, we aim to foster collaboration and knowledge exchange among researchers, practitioners, and industry experts in the field.
The "Nowcasting in Renewable Energy Systems" special session aims to cater to a diverse audience, including researchers, engineers, and practitioners actively involved in renewable energy research, grid integration, energy management, and related disciplines. This platform will be invaluable for individuals at all career stages, offering insights into cutting-edge developments in nowcasting technology and its practical applications within renewable energy systems.
This session will provide an introduction to multiport power converter topologies and the features that make them suitable for a range of distribution network applications. A sizing optimisation of a multiport power converter for an enhanced soft open point application will be presented. Finally, the session will analyse a non-isolated low voltage multiport power converter.
Integration of the decentralized energy resources in the form of non-dispatchable renewable sources is considered as one of the most crucial aspects for carbon naturalization in the smart grid. However, the energy efficiency and carbon impact of their integration are affected by numerous factors which can limit their anticipated benefits. The concept of the microgrid is introduced to facilitate their adaption into the distributed electricity grid with the aim of reducing cost and carbon emissions. Therefore, the challenges and opportunities on planning and the operation of the microgrids requires detailed techno-economic investigation including environmental impact before the implementation of the developed novel algorithms.
In most of the cases, although the capacity of the renewable resources (with/without storage) is increased, the produced renewable energy either curtailed or sold back to main grid which limits the self-sufficiency of the microgrids. Additionally, based on the utilized technology for renewables, studied geography, and energy-mix of the main grid, they might even increase the carbon impact of the microgrids unexpectedly. To this end, this Special Session is devoted to discussing latest advancements in planning and operation of self-sufficient microgrids with novel control/management methods considering the environmental impact of deployed technologies.
The integration of renewable energy sources into the power grid is a pivotal component of global efforts to mitigate climate change and ensure a sustainable energy future. However, these systems are not free of failures, breakdowns, and producibility issues that lead to early system degradation and a reduction in energy, economic and environmental (3E) advantages. For this reason, the use of the most innovative technologies to predict these issues as early as possible can solve not only the technical challenges but also to optimize the 3E aspects.
This special session aims to ensure the reliability and the 3E sustainability of renewable energy systems by exploiting smart strategies and methodologies. The session emphasizes the crucial need for a comprehensive approach that considers both technical, environmental and financial aspects to facilitate the seamless integration of renewable energy into power grids. Researchers, engineers and experts in the field will discuss cutting-edge methods, including advanced modeling, data analytics, fault detection and classification, and smart grid technologies to enhance the performance and profitability of renewable energy installations.
Monitoring and identifying faults in electrical machinery is both a technical and financial concern driven by the aim to enhance reliability and operational efficiency in electrical drives. The diagnosis of faults in electrical drives constitutes a vital aspect of a comprehensive monitoring system designed to enhance reliability and serviceability.
This special session covers the different methodologies employed for detecting faults in electrical drives, encompassing electrical, thermal, and mechanical issues of the electrical machine, as well as malfunctions of the static converter and energy storage unit.
Besides that, the analysis of electrical machines constitutes a fundamental topic in electrical engineering, typically encompassing the comprehension of transformers and rotating apparatus. Various simulation software can offer a robust foundation, aiding electrical engineers in devising practical solutions with precise load predictions and cost efficiencies. This special session encompasses also the advances in this domain and, especially, its connection to diagnosis.
Electric storage devices, like batteries, supercapacitors, and electric vehicles, are usually connected to the grid for cogeneration or energy conservation for future use. This connection is made through power electronics interfaces that should guarantee high stability, voltage regulation, power flow control, and low electromagnetic emission, along with high power density, low cost, and high reliability. To increase the power density, passive devices that are considered the bulkiest components in these systems should be reduced or avoided. This can be achieved by considering multilevel topologies that would comply with power quality requirements without the need for passive filters.
This session is dedicated to the various solutions adopted for high quality energy management at the storage or EV charging levels. More specifically, it will present advanced power electronics topologies used for power quality enhancement in such applications. Model-based or intelligent control algorithms ensuring a compliance with grid requirements, especially regarding power quality and V2G connectivity, and EV-related standards are also considered as major topics in this session.
As the energy mixes contributing to the grid have evolved over time, new standards have been established with respect to energy sources. This shift has required grid-connected inverters to comply with increasingly complex demands, such as coordinating systems with photovoltaics and energy storage via batteries and/or fuel cells. This kind of application can be addressed via control strategy adaptations applied to existing topologies. Still, this approach can lead to sub-optimal results if the right topologies are not chosen.
The scope of this session is to investigate the most suitable topologies for grid connection of most modern power systems and the control-strategy\topology interactions that yield the most desirable characteristics in terms of power quality (compliance to IEEE 1547 and IEC 61727), the possibility of interaction with different power sources and inherent fault tolerance. Converters specifically designed to meet requirements dictated by particular power source requirements, such as low voltage DC PV, hybrid systems, DMPPT interaction etc., are also the focus of this special session.
The ecological transition and climate change, we are facing, and which must be rapid, is also associated with massive electrification. It implies that the issue of sustainability must be fully integrated into the field of electrical engineering.
However, there is a lack in terms of tools available and the existing channels and communities addressing the issue of sustainability. It is then necessary for the electrical engineering community to work on this topic and to be able to federate around them.
This session would therefore focus around the following scientific objectives on sustainability:
This session has a second goal for the electrical engineering community: more and more researchers are working on these questions and it is important to increase this number but also to pool and collaborate around this new and highly multidisciplinary topic.
This special session is devoted to research work covering the fields of new trends in electric power systems contributing to new generations of marine electrical systems. These systems include energy/propulsion system for new generation of electrical or e-hybrid propulsion ships and marine renewable energy systems as these two topics are subject of intensive dual research. This is why ship power and marine renewable energy (MRE) systems are in the scope of this SS.
The session will focus on the application of artificial intelligence techniques to assist or replace the conventional optimization models used for the operational planning of power systems. The assistance the session is targeting is not related to price, load, or renewable generation forecasting. Instead, it pertains to specific tasks required in the search for the system’s optimal operational plan, e.g., defining optimization options, estimating bounds for certain problem parameters, and activating or deactivating some problem constraints..
The session is open to contributions covering both theoretical and practical aspects of the applications described above, as well as reviews of the state-of-the-art. The scope of the systems under study can range from single power plants to entire power systems comprising a variety of renewable and non-renewable generation technologies.
The planning horizon can be diverse, ranging from short-term (a few minutes or hours ahead) to long-term (several weeks or months ahead), provided the planning decisions are operational, such as generation schedules, water values, etc.
Electrical grids, and microgrids in particular, are cyber-physical systems with increasing number of controllable devices, data volumes (from smart-meters, sensors, and controllers), complexity, and flexibility needs. These aspects affect the requirements of energy management systems (EMS) that should be robust and resilient but also able to make timely decisions. For these reasons the technical literature on EMS has followed two avenues, sometimes concurrently, 1) the combination of increasing computing power from the cloud with that of edge devices into hierarchical EMSs and decision-making systems; 2) the development of approximated optimization algorithms like meta-heuristic algorithms and of machine learning algorithms for the solution of energy management problems.
This special session is aimed at presenting the latest advances and developments in algorithms and technologies for energy management systems in smart microgrids of buildings or smart vehicles (e.g., ships or airplanes). Meta-heuristic methods and machine learning based methods for energy management are of interest complemented with applications of cloud computing and edge computing technologies. Furthermore, hierarchical energy management and multilevel decision making that combine edge computing for local decision and cloud computing for higher-level long-term optimization are also of interest. Finally, machine learning applications for renewable sources forecasting, load forecasting and monitoring are fundamental for robust and effective energy management. Therefore, advancement in these topics should also be considered.