The advance on smart grid calls for the development and application of distributed intelligent control strategies based on open platforms that provides interoperability. The distributed control must be able to ensure reliable and secure operation of the power systems and enhance supply quality to consumers. The development of a multi‐agent system for automatic restoration system (MARS) applied to a real power distribution network is presented. The agents of the MARS are embedded in external hardware to the intelligent electronic devices (IEDs) and communicate with each other via standard Foundation for Intelligent Physical Agents open protocols. To evaluate the MARS performance, a computer simulator was developed in Java to represent the distribution system. Experimental tests were performed in laboratory with the simulator integrated to the MARS via TCP/IP. The tests results have shown that the MARS was able to locate and isolate branches under permanent fault condition and restore healthy branches efficiently through a given switching sequence taking into account the system operational constraints. The proposed MARS has proved the effectiveness of distributed control scheme based on multi‐agent system that runs independently of the current technologies of the IEDs of control and protection.

This work proposes a smart protection system for microgrids, which relies on an adaptive metaheuristic for the automatic calculation of optimal settings for directional overcurrent relays (DOCRs). The adaptive fuzzy directional bat algorithm (AFDBA) associated with a fuzzy inference system (FIS) is used for this purpose. A prominent advantage of this solution is that there is no need for an initial tuning of the parameters associated with the algorithm, unlike many traditional approaches reported in the literature. Such a metaheuristic is used in the conception of an adaptive protection system (APS) in the context of a microgrid while taking into account the connection status of distributed generation (DG) units under distinct scenarios. A performance comparison with a protection system with fixed optimal settings (PSFOS) is also presented. The results demonstrate that the proposed APS outperforms the PSFOS while providing faster response, higher reliability and less susceptibility to miscoordination. In other words, it presents a shorter trip time when compared with the PSFOS, with a reduction of 6.83% and 26.58% when considering the DG penetration and the islanded microgrid, respectively.

This paper is concerned with the two‐stage hierarchical control of microgrids (MG) that can operate in both grid‐connected and off‐grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control‐based method is used in the primary level, while a distributed multi‐agent coordination scheme is applied in the secondary one. The multi‐agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA‐compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of non‐dispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi‐agent system. Co‐simulations using PSCAD^TM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies.

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals.