Distributed Model Predictive Control for the Mitigation of Cascading Failures

Abstract

Most large blackouts are caused by cascading failures-sequences of equipment outages, one set of outages precipitating another. We study the application of distributed, autonomous agents for shortening such sequences. Each agent controls a single variable-the consumption of a load or the output of a generator. Each agent uses model predictive control and cooperates with its neighbors in making its decisions. Experiments using the IEEE 118 bus test case illustrate the effectiveness of this method.

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

“…They could negotiate/exchange useful information in order to improve global performance. E.g., local agents of [12] are designed to inform their neighbors about what they intend to do and pass along measurements that other agents may not be able to sense directly. In [20], local agents exchange Lagrange multipliers and values of shared variables after each computation and computations are carried on until absolute changes in the Lagrange multipliers are smaller than a pre-defined small positive constant, before actual controls are applied to the system.…”

“…Since distributed MPC controllers act in the same system, coordination is needed in order to achieve satisfactory performances in damping low-frequency oscillations throughout the system [8], [12], [19]. They could negotiate/exchange useful information in order to improve global performance.…”

“…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. By cloning the boundary nodes of neighboring areas, reference [15] breaks the whole power grid into relatively independent subsystems that only interact through consistency constraints on shared variables; each local MPC controller calculates optimal inputs for automatic voltage regulators and static Var compensators in its area, and coordinates with neighboring MPC controllers by exchanging Lagrange multipliers.…”

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

“…In [26], [41]- [43] several DMPC approaches are discussed. Moreover, in [1], [5], [22]- [28], [30], [43], [44] some specific DMPC schemes are presented. Almost all of them have been applied to systems with sampling times in the range between seconds and minutes.…”

Optimal integration of intermittent energy sources using distributed multi-step optimization

“…Distributed MPC is applied to power systems in [10] for the mitigation of cascading failures, and uses models of neighboring areas. The control agents may anticipate that neighboring areas will make different control decisions than those calculated by the other agents themselves according to the accuracy of these models.…”