**Preface to "Multi-Agent Energy Systems Simulation"**

The synergy between artificial intelligence and power and energy systems is providing promising solutions to deal with the increasing complexity of the energy sector. Multi-agent systems, in particular, are widely used to simulate complex problems in the power and energy domain as they enable modeling of dynamic environments and studying the interactions between the involved players. Multi-agent systems are suitable for dealing not only with problems related to the upper levels of the system, such as the transmission grid and wholesale electricity markets, but also to address challenges associated with the management of distributed generation, renewables, large-scale integration of electric vehicles, and consumption flexibility. Agent-based approaches are also being increasingly used for control and to combine simulation and emulation by enabling modeling of the details of buildings' electrical devices, microgrids, and smart grid components.

This book discusses and highlights the latest advances and trends in multi-agent energy systems simulation through a collection of 8 research papers. The addressed application topics include the design, modeling, and simulation of electricity markets operation, the management and scheduling of energy resources, the definition of dynamic energy tariffs for consumption and electrical vehicles charging, the large-scale integration of variable renewable energy sources, and mitigation of the associated power network issues.

"The Application of Ontologies and Multi-Agent Systems in the Energy Sector: A Scoping Review" provides a scoping review of the existing literature on ontology for multi-agent systems in the energy domain, and maps the key concepts underpinning these research areas. Furthermore, this paper provides a recommendation list for the ontology-driven multi-agent systems development.

"Variable Renewable Energy and Market Design: New Market Products and a Real-World Study" addresses the topic of market design to accommodate large-scale integration of variable renewable energy. A new bilateral energy contract type is proposed along with two new marketplaces that can contribute to reducing the imbalances resulting from variable renewable energy producers are introduced.

"Dynamic Tariff for Day-Ahead Congestion Management in Agent-BVased LV Distribution Networks" advances the research made in the area of congestion management in low-voltage networks. The paper tackles these challenges by iterative chances in prices (dynamic tariffs). An agent-based system is able to demonstrate a reduction of 82% in congestion using an IEEE European LV test feeder without loss of power quality in the grid.

"An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm" proposes the design and implementation of a real-time energy management system based on a multi-agent systems approach. The fast converging T-cell algorithm is applied to minimize the operational cost of a microgrid and maximize the real-time response in grid-connected microgrid mode.

"Reactive Power Management Considering Stochastic Optimization under the Portuguese Reactive Power Policy Applied to DER in Distribution Networks" provides a stochastic agent framework to improve the reactive power management by taking advantage of the full capabilities of the distributed energy resources and by reducing the injection of reactive power by the transmission system operator in the distribution network and, therefore, reducing losses. The uncertainty of renewables is considered in the proposed sequential alternative current optimal power flow.

"Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems" takes

further steps in the modeling of customer behavior when it comes to charging electrical vehicles. Previous works usually adopted a stochastic approach with few details since little information is available. This work uses more detailed customer model employing a multi-agent simulation framework in order to investigate how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction in past charging sessions) impacts the essential key performance indicators of the charging system. Results show that the MAS system can produce quantitative and qualitative differences when small changes are tested in the customer behavior.

"Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols" presents an energy model that estimates the energy consumption at each node of a network, taking into account the functions of sensors transmitting data. Therefore, the model considers a given routing protocol allowing the comparison and assessment of different performance metrics from an energy standpoint. The model was validated on a real proof-of-concept implementation using system-on-chip equipment. The proposed model achieved 97% accuracy compared to the actual performance of a network, which reflects its effectiveness in comparing communication protocols in WSNs.

"Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network" objectively defines a set of performance metrics to compare different IoT communication protocols used in wireless sensor networks. A real wireless sensor network is placed on a university campus to compare the performance of some of the most popular protocols, i.e., Zigbee, LoRa, Bluetooth, and WiFi. Since energy consumption is a crucial aspect of the wireless sensor network (due to battery-powered sensors), particular focus is given to the metrics related to energy efficiency. The defined performance metrics and methodology become a suitable tool in contrasting low-consumption wireless technologies applied to IoT that can be used to implement multi-agent systems.

The complementarity and broad scope of this collection of papers offers a relevant perspective of many challenges arising in power and energy systems, and how multi-agent simulation approaches are contributing to overcoming such challenges.

We thank all the authors and reviewers who have significantly contributed to the high quality of the papers included in this collection. We also express our gratitude to the editorial team of MDPI and *Energies* for all the support during the entire project.

> **Tiago Pinto, Jo˜ao Soares, Fernando Lezama** *Editors*

*Review*
