More Like this

1. Decentralized control of distributed energy resources in radial distribution systems

   https://doi.org/10.1109/SmartGridComm.2016.7778777  

   Lin, Weixuan; Bitar, Eilyan (November 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm))

   We consider the decentralized control of radial distribution systems with controllable photovoltaic inverters and storage devices. For such systems, we consider the problem of designing controllers that minimize the expected cost of meeting demand, while respecting distribution system and resource constraints. Employing a linear approximation of the branch flow model, we formulate this problem as the design of a decentralized disturbance-feedback controller that minimizes the expected value of a convex quadratic cost function, subject to convex quadratic constraints on the state and input. As such problems are, in general, computationally intractable, we derive an inner approximation to this decentralized control problem, which enables the efficient computation of an affine control policy via the solution of a conic program. As affine policies are, in general, suboptimal for the systems considered, we provide an efficient method to bound their suboptimality via the solution of another conic program. A case study of a 12 kV radial distribution feeder demonstrates that decentralized affine controllers can perform close to optimal. 

   more » « less
2. Communication- and Control-aware Optimal Quantizer Selection for Multi-agent Control

   https://doi.org/10.1109/LCSYS.2024.3416858  

   Afshari, M; Maity, D; Tsiotras, P (June 2024, IEEE control systems letters)

   We consider a multi-agent linear quadratic optimal control problem. Due to communication constraints, the agents are required to quantize their local state measurements before communicating them to the rest of the team, thus resulting in a decentralized information structure. The optimal controllers are to be synthesized under this decentralized and quantized information structure. The agents are given a set of quantizers with varying quantization resolutions—higher resolution incurs higher communication cost and vice versa. The team must optimally select the quantizer to prioritize agents with ‘highquality’ information for optimizing the control performance under communication constraints. We show that there exist a sepatation between the optimal solution to the control problem and the choice of the optimal quantizer. We show that the optimal controllers are linear and the optimal selection of the quantizers can be determined by solving a linear program. 

   more » « less
3. A 𝑃 ₁ Finite Element Method for a Distributed Elliptic Optimal Control Problem with a General State Equation and Pointwise State Constraints

   https://doi.org/10.1515/cmam-2021-0106  

   Brenner, Susanne C.; Liu, Sijing; Sung, Li-Yeng (February 2021, Computational Methods in Applied Mathematics)

   Abstract We investigate a P 1 P_{1} finite element method for an elliptic distributed optimal control problem with pointwise state constraints and a state equation that includes advective/convective and reactive terms.The convergence of this method can be established for general polygonal/polyhedral domains that are not necessarily convex.The discrete problem is a strictly convex quadratic program with box constraints that can be solved efficiently by a primal-dual active set algorithm. 

   more » « less
4. Finite-Horizon Separation-Based Covariance Control for Discrete-Time Stochastic Linear Systems

   https://doi.org/10.1109/CDC.2018.8619542  

   Bakolas, Efstathios (December 2018, 2018 IEEE Conference on Decision and Control (CDC))

   In this paper, we address a finite-horizon stochastic optimal control problem with covariance assignment and input energy constraints for discrete-time stochastic linear systems with partial state information. In our approach, we consider separation-based control policies that correspond to sequences of control laws that are affine functions of either the complete history of the output estimation errors, that is, the differences between the actual output measurements and their corresponding estimated outputs produced by a discrete-time Kalman filter, or a truncation of the same history. This particular feedback parametrization allows us to associate the stochastic optimal control problem with a tractable semidefinite (convex) program. We argue that the proposed procedure for the reduction of the stochastic optimal control problem to a convex program has significant advantages in terms of improved scalability and tractability over the approaches proposed in the relevant literature. 

   more » « less
5. A global algorithm for AC optimal power flow based on successive linear conic optimization

   https://doi.org/10.1109/PESGM.2017.8273957  

   Barati, Masoud; Kargarian, Amin (July 2017, 2017 IEEE Power & Energy Society General Meeting)

   Newly, there has been significant research interest in the exact solution of the AC optimal power flow (AC-OPF) problem. A semideflnite relaxation solves many OPF problems globally. However, the real problem exists in which the semidefinite relaxation fails to yield the global solution. The appropriation of relaxation for AC-OPF depends on the success or unfulflllment of the SDP relaxation. This paper demonstrates a quadratic AC-OPF problem with a single negative eigenvalue in objective function subject to linear and conic constraints. The proposed solution method for AC-OPF model covers the classical AC economic dispatch problem that is known to be NP-hard. In this paper, by combining successive linear conic optimization (SLCO), convex relaxation and line search technique, we present a global algorithm for AC-OPF which can locate a globally optimal solution to the underlying AC-OPF within given tolerance of global optimum solution via solving linear conic optimization problems. The proposed algorithm is examined on modified IEEE 6-bus test system. The promising numerical results are described. 

   more » « less