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S. Cianchi
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This paper proposes a direct data-driven approach to address decentralized control problems in network systems, i.e., systems formed by the interconnection of multiple subsystems, or agents. Differently from previous work, in this paper we assume that coordination among agents is limited in the data collection phase. Specifically, while we allow for multiple experiments to be performed on the network, these can be asynchronous (meaning that we do not require that all agents take part to each experiment). We focus this study on an open-loop optimal control problem, and propose a strategy to reconstruct the missing experimental data, i.e., data from the agents not participating to a given experiment. Importantly, our data-reconstruction strategy does not compromise the performance or numerical reliability of the approach, as we give conditions under which the missing data can be exactly reconstructed. We complement our findings with numerical simulations, showcasing the effectiveness of our approach in decentralized control scenarios.
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This paper proposes a direct data-driven approach to address decentralized control problems in network systems, i.e., systems formed by the interconnection of multiple subsystems, or agents. Differently from previous work, in this paper we assume that coordination among agents is limited in the data collection phase. Specifically, while we allow for multiple experiments to be performed on the network, these can be asynchronous (meaning that we do not require that all agents take part to each experiment). We focus this study on an open-loop optimal control problem, and propose a strategy to reconstruct the missing experimental data, i.e., data from the agents not participating to a given experiment. Importantly, our data-reconstruction strategy does not compromise the performance or numerical reliability of the approach, as we give conditions under which the missing data can be exactly reconstructed. We complement our findings with numerical simulations, showcasing the effectiveness of our approach in decentralized control scenarios.
The electrical grid is experiencing a significant restructuring to face the challenges posed by the green transition. A key aspect in this restructuring process is the increased engagement of consumers as active players, who, through the participation in demand side management schemes, can deliver services to the grid. In this paper, a novel pricing mechanism composed of two dynamic volumetric parts is introduced which can be employed by a central coordinator (e.g., a DSO) to simultaneously: i) prevent congestions in the grid, and ii) recuperate its investment costs. To capture the interconnected objective functions, and coupling constraints between the central coordinator and the consumers, the problem is formulated as a generalized Nash equilibrium problem, which is solved using a distributed and privacy-friendly hybrid best-response plus dual-ascent algorithm with convergence guarantees. Moreover, the work considers that the consumers' decision-making processes can deviate from full economic rationality and be driven by personal preferences about the tradeoff between their induced comfort and their economic savings. Numerical simulations corroborate the convergence of the proposed algorithm, and show the effectiveness of the proposed two-part tariff in eliminating grid congestions. The results highlight the negative impact of a reduced user price sensitivity on the collective costs of all consumers.
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The electrical grid is experiencing a significant restructuring to face the challenges posed by the green transition. A key aspect in this restructuring process is the increased engagement of consumers as active players, who, through the participation in demand side management schemes, can deliver services to the grid. In this paper, a novel pricing mechanism composed of two dynamic volumetric parts is introduced which can be employed by a central coordinator (e.g., a DSO) to simultaneously: i) prevent congestions in the grid, and ii) recuperate its investment costs. To capture the interconnected objective functions, and coupling constraints between the central coordinator and the consumers, the problem is formulated as a generalized Nash equilibrium problem, which is solved using a distributed and privacy-friendly hybrid best-response plus dual-ascent algorithm with convergence guarantees. Moreover, the work considers that the consumers' decision-making processes can deviate from full economic rationality and be driven by personal preferences about the tradeoff between their induced comfort and their economic savings. Numerical simulations corroborate the convergence of the proposed algorithm, and show the effectiveness of the proposed two-part tariff in eliminating grid congestions. The results highlight the negative impact of a reduced user price sensitivity on the collective costs of all consumers.