Urban drainage systems (UDS) may be considered large–scale systems given their large number of associated states and decision actions, making challenging their real–time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.Peer ReviewedPostprint (author's final draft

Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.

In the path to the future implementation of the smart grid, microgrids are presented as a cornerstone. An efficient and optimal microgrid operation is paramount. In this paper, we present a hierarchical microgrid management system using task sharing and an evolutionary game theory based dispatch strategy as a coordination algorithm to integrate the three main control levels needed for microgrid operation, which can be considered as a distributed intelligent system. The proposed algorithm shares the total load demanded into the distributed generators efficiently, while the microgrid utility is maximized dynamically, increasing the system reliability. Some simulation results are presented to illustrate the effectiveness of the dynamic performance of the proposed approach compared with a traditional hierarchical algorithm.

Power distribution networks that include distributed generation need new control strategies based on distributed and hierarchical structures. For these systems, a replicator dynamics strategy for dynamic resource allocation in the dispatch of distributed generators in a microgrid is presented. This approach uses the characteristics of a defined subsystem in order to offer a simple algorithm with optimal and feasible solutions even in cases where other methods do not satisfy the problem's constraints. To compare the performance of the replicator dynamics strategy and to analyze the optimality of the obtained solutions, a market multiagent-based scheme is adapted. The results are implemented in a simulation model for different scenarios to show the applicability of the proposed strategy.

Recent work has studied the impact caused by attackers that compromise pricing signals used in the emerging retail electricity market and send false prices to a subset of consumers. In this paper, we extend previous work by considering a more realistic adversary model that is not arbitrarily tied to scaling and delay attacks, but that can generate any arbitrary pricing signal and show how to keep the problem tractable with a new analysis based on sensitivity functions. In addition, we extend previous work by proposing countermeasures to mitigate the negative impact of these attacks. Countermeasures include selecting parameters of the controller, designing robust control algorithms, and by detecting anomalies in the behavior of the system.

Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers.