Resilient Distributed Energy Management for Systems of Interconnected Microgrids

In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

Chebyshev minimax control theory

General, closed-form, analytical solutions are determined for certain classes of C-minimax control problems, several alternative mathematical theories are derived, and a controller design theory is developed to give optimal control in the presence of unmeasureable external disturbances

Time-varying partitioning for predictive control design: density-games approach

The design of distributed optimization-based controllers for large-scale systems (LSSs) implies every time new challenges. The fact that LSSs are generally located throughout large geographical areas makes dicult the recollection of measurements and their transmission. In this regard, the communication network that is required for a centralized control approach might have high associated economic costs. Furthermore, the computation of a large amount of data implies a high computational burden to manage, process and use them in order to make decisions over the system operation. A plausible solution to mitigate the aforementioned issues associated with the control of LSSs consists in dividing this type of systems into smaller sub-systems able to be handled by independent local controllers. This paper studies two fundamental components of the design of distributed optimization-based controllers for LSSs, i.e., the system partitioning and distributed optimization algorithms. The design of distributed model predictive control (DMPC) strategies with a system partitioning and by using density-dependent population games (DDPG) is presented.Peer ReviewedPostprint (author's final draft

Integrating process design and control: An application of optimal control to chemical processes

In this paper, the optimal design of process systems generically used in chemical industries is studied. The closely coupled nature of optimal design specification of the equipment, the determination of the optimal process parameters in steady-state, moreover, some issues of the application of optimal control is shown. The solution of the overall optimization problem including (i) optimal design of the equipment and (ii) specification of its optimal control strategy can be found relying on two different design concepts, namely, on the conventionally used sequential or, on the newly emerged simultaneous design approaches. This paper gives the theoretical background of the ideas and presents a comparative summary of the approaches. The two approaches are contrasted to each other in which the effects of the interaction of optimal process design and optimal control is highlighted. A new simultaneous optimization procedure providing economic and operability benefits over the traditional stand-alone approach is proposed. The applicability of the idea is demonstrated by means of a design study carried out for optimal design of a coaxial heat exchanger and a reactive distillation column for the synthesis of ethyl tert butyl ether (ETBE), relying on the benefits of the utilization of optimal control

Design of Launch Vehicle Flight Control Systems Using Ascent Vehicle Stability Analysis Tool

A launch vehicle represents a complicated flex-body structural environment for flight control system design. The Ascent-vehicle Stability Analysis Tool (ASAT) is developed to address the complicity in design and analysis of a launch vehicle. The design objective for the flight control system of a launch vehicle is to best follow guidance commands while robustly maintaining system stability. A constrained optimization approach takes the advantage of modern computational control techniques to simultaneously design multiple control systems in compliance with required design specs. "Tower Clearance" and "Load Relief" designs have been achieved for liftoff and max dynamic pressure flight regions, respectively, in the presence of large wind disturbances. The robustness of the flight control system designs has been verified in the frequency domain Monte Carlo analysis using ASAT

Robust monetary policy in a small open economy

This paper studies how a central bank’s preference for robustness against model misspecification affects the design of monetary policy in a New-Keynesian model of a small open economy. Due to the simple model structure, we are able to solve analytically solve the optimal robust policy rule, and separately ana-lyze the effects of robustness against misspecification concerning the determination of inflation, output and the exchange rate. We show that an increased central bank preference for robustness makes monetary policy respond more aggressively or more cautiously to shocks, depending on the type of shock and the source of misspecification.Knightian uncertainty; model uncertainty; robust control; min-max policies