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Abstract
In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events.
Highlights
With the frequent occurrence of extreme events and their severe impact on the existing grid architecture, the need for improving the resilience of the grid has grown
Power system resilience is defined as the ability to withstand the High Impact Low Probability (HILP) events, such as wildfires and hurricanes, and continue to energize at least the critical loads and quickly recover from any interruptions caused by the extreme events [1]
Traditional power systems are more prone to failure during extreme events due to their centralized and interconnected structure, and enormous size
Summary
With the frequent occurrence of extreme events and their severe impact on the existing grid architecture, the need for improving the resilience of the grid has grown. The agent-based MG power management problem has been explored and solved using a variety of optimization frameworks. In [8], the energy trading between multiple MGs is formulated as a distributed optimization problem and solved using an iterative subgradient-based algorithm to minimize operation cost. Energies 2022, 15, 1774 management strategy during extreme events and contingency focusing on home a3popfl1i-3 ances and available electric vehicles Different indices such as consumer convenience and demand rebound are considered in this study. General framework of the proposed agent-based active power management scheme. If the MG does not have adequate power supply to cover all its loads during the extreme event, the ESS agent creates a discharge scheme that tries to supply critical loads at all hours of the extreme event. The following constraints, (6)–(9), are considered for the ESS agent to ensure it operates within its power limits and energy capacity
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