Conceptual Microgrid Management Framework Based on Adaptive and Autonomous Multi-Agent Systems

Abstract

The Smart Grids paradigm emerged as a response to the need to modernize the electric grid and address problems related to the demand for better quality energy. However, there are no fully developed and implemented smart grids, but only some minor scale tests to prove the concepts. Centralized systems are still common, with a low granularity of control and reduced monitoring capacity, especially in low-voltage networks. In this work, we propose a framework for Microgrid Management, addressing problems such as determining how to control the energy demand and peak loads, the effect of the energy consumption in the network, and the amount of energy required. We proposed a solution based on autonomous and distributed systems for the following problems: Peak Load addressed with AIN-DSM distributed algorithm, transformer lifespan estimation using a thermal model adjusted by Genetic Algorithms, and Short-Term Load Forecasting based on Artificial Neural Networks and Genetic Algorithms. The distributed paradigm of the Organization Centered Multi-Agent Systems methodology was applied for the framework's modeling and development. The results obtained by using these solutions in the Tucumán province, Argentina, show the system's capabilities and the relevance of the information produced from the framework.

Component degradation and system deterioration: An overview of early termination of PV-DG microgrid system

“…Overloading and corrosion were found in previous research [83] [87], which led to a deteriorated transformer. This condition shall later reduce the transformer lifespan and affect the other section of the microgrid system.…”

“…Unmanaged activities will lead to an accumulated loss of power and result in an unpredicted blackout. Without proper maintenance, the line and the network shall fail and require an unnecessary re-design [86] [87].…”

“…Lastly, peak load shaving is giving a significant effect on preventing blackouts. Without proper peak shaving management, an unpredicted high peak load can trigger an unpredicted blackout and lead to a total loss of power [87].…”