A new MPC scheme for damping wide-area electromechanical oscillations in power systems

Abstract

This paper introduces a new Model Predictive Control (MPC) scheme to damp wide-area electromechanical oscillations. The proposed MPC controller, based on a linearized discrete-time state space model, calculates the optimal input sequence for local damping controllers over a chosen time horizon by solving a quadratic programming problem. Local controllers considered include: Power Systems Stabilizers (PSSs), Thyristor Controlled Series Compensators (TCSCs) and Static Var Compensators (SVCs). The MPC scheme is realized and tested first in ideal conditions (complete state observability and controllability, neglecting communication and computing delays). Next, the effects of state-estimation errors, computation and communication delays, and of the number and type of available local damping controllers are studied in order to assess the versatility of this scheme. Realistic simulations are carried out using a 16 generators, 70 bus test system.

Distributed MPC of wide-area electromechanical oscillations of large-scale power systems

“…To overcome the limitations of these systems, a promising option would be to automatically adjust their control strategy to the changing behavior of the underlying system (caused, e.g., by outages of control devices, maintenance operations, connectivity changes, incorporation of new generation, modifications of load dynamics implied by smart-grid technologies, etc.). Since power system dynamics can be quite accurately modeled, and given the recent progresses in large-scale optimization, a natural idea is to apply Model Predictive Control (MPC) to design such adaptive control strategies [4].…”

“…More recently MPC was proposed for emergency alleviation of thermal overloads [11]- [13], voltage control [14]- [16], transient stability and oscillations control [4], [17], [18], automatic generation control [19], [20]. In [12], agents are placed at each generator and load to control injections, and they use detailed models of their surrounding area and simplified models of remote ones; they share their objectives and exchange solutions and measurements.…”

“…The paper is organized as follows. Section II recalls the centralized MPC problem formulation from [4], while Section III proposes a distributed MPC scheme and discusses its decomposition and coordination issues. In Section IV, three distributed schemes, two of them involving weak coordination by Wide Area Measurement Systems (WAMS), are proposed and assessed.…”

“…II. CENTRALIZED MPC SCHEME FOR WIDE-AREA ELECTROMECHANICAL OSCILLATIONS DAMPING [4] For the sake of making this paper self-contained and to introduce the notations and concepts used subsequently, we briefly recall in this section the centralized MPC scheme proposed in [4]. This scheme is based on a linearized discrete-time model of the power system dynamics combined with a linearly constrained quadratic optimal control problem formulation.…”

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

“…The work presented in Glavic et al (2005a) suggested fusion of RL and the concept of control Lyapunov functions (this approach is further elaborated in Glavic et al (2006)). Other works suggesting fusion of RL with known control techniques include Ernst et al (2009);Wang et al (2014) where RL was considered together with model predictive control, and Li and Wu (1999) where RL is combined with fuzzy logic control.…”