Distributed optimization algorithms are computational techniques used to solve optimization problems by distributing tasks across multiple agents or nodes, allowing them to work collaboratively. This approach is particularly effective in systems like smart grids, where decentralized control is crucial for managing resources and responding to dynamic conditions. By leveraging the strengths of local computation and communication, these algorithms enhance efficiency, scalability, and resilience in energy management applications.
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