Supervised model predictive control of large‐scale electricity networks via clustering methods

Abstract

This article describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a nontrivial scenario.

Control predictivo de sistemas ciberfísicos

“…En los últimos años el CP predictivo distribuido ha evolucionado hacia métodos que permiten a los controladores agruparse dinámicamente con el fin de cooperar únicamente en los momentos en los que el acoplamiento aumenta, trabajando de forma descentralizada el resto del tiempo (Fele et al, 2017;Jain et al, 2018;La Bella et al, 2021;Chanfreut et al, 2021). Este enfoque es de gran interés dentro del contexto de los SCF ya que aumentan la escala de las aplicaciones en las que el CP puede ser aplicado.…”

Distributed Model Predictive Control for Tracking: A Coalitional Clustering Approach

“…In this context, the classical assumption is to consider a fixed communication network with static neighborhoods. By contrast, some recent works explore dynamic neighbourhoods by considering switching communication topologies [2], [3], time-varying partitioning [4], plug and play schemes [5], [6], and clustering [7]. In this work, we will focus on coalitional control [8], a clustering approach where the use of the communication resources is penalized to deter the control scheme from exchanging unnecessary information, leading to a time-varying degree of coordination.…”

Hierarchical Frequency Control in Multi-Area Power Systems with Prioritized Utilization of Inverter Based Resources

“…Quantification of such prioritization, however, has been addressed very sparsely in the power system control literature so far. Several seminal papers have been written on how IBRs can facilitate frequency regulation using mathematical tools from both linear and nonlinear control theory such as phase cohesiveness [6], passivity [7], [8], synchronization in complex oscillator networks [9], sliding mode control [10], model predictive control [11], and event-triggered ∞ -control [12], to name a few, but not related to prioritization over synchronous generation. The same argument applies to the literature on voltage control [13]- [15].…”