Distributed strategy for constrained resource allocation problems of autonomous second-order nonlinear agents and its application to smart grids

This paper studies the constrained distributed resource allocation problems (DRAPs) of autonomous multi-agent systems (MASs). Unlike well-defined DRAPs, the disturbed second-order agents are taken into account. Moreover, all agents' decisions are subject to both coupling and local inequalities. Because of the coexistence of disturbed second-order nonlinear dynamics and inequality constraints, existing strategies are not applicable to tackle our problem. Also, they create challenges for the strategy design, because the inequality constraints must hold at the optimal allocation (OA), while these agents are not able to control their decisions directly. By gradient descent, state feedback and internal model (IM), we exploit a distributed strategy to control all agents to carry out the distributed resource allocation tasks (DRATs). Furthermore, the strategy is rigorously analyzed. Finally, our strategy is applied to the economic dispatch problems (EDPs). Under our approach, turbine-generators can autonomously achieve the optimal generation allocation by regulating their powers, in accordance with the load demand of smart grids.