Chiara Piacentini

An extension of metric temporal planning with application to AC voltage control

In this paper we explore the deployment of planning techniques to solve a new class of metric temporal planning problems, characterised by the need to manage both plan trajectory constraints and uncontrollable numeric events. This combination gives rise to challenges not previously solved in state-of-the-art planners. We introduce new planning methods to handle these challenges, and demonstrate our approach using a real application domain: voltage control in Alternating Current (AC) electrical networks. Embedding electricity networks in a domain description presents important modelling challenges. We introduce an encapsulated type, Network, the implementation of which is hidden from the planner. The effects of actions trigger complex updates to the state of the network. We distinguish between the direct effects of planned actions, and the indirect effects triggered by them, and we propose a method for integrating a specialised external AC power equation solver with a planner. We consider a new heuristic function that takes into account the next uncontrollable event, and its interaction with active trajectory constraints, when determining the actions that are helpful in a state. This lookahead heuristic also exploits an abstraction of the encapsulated Network type to obtain more informative distance estimates. We conduct experiments to evaluate the benefits of the lookahead heuristic, showing that our approach scales very well with the size of the network and the number of controllable components of the network.

Zoning reconfiguration for coordinated voltage regulation in future transmission grids

Hierarchically structured Automatic Voltage Control (AVC) architecture enables wide-area closed-loop Coordinated Voltage Regulation (CVR). Owing to the inherent complexity of the task, CVR relies on reduced control models, i.e. simplified models of the system suitable for voltage control. It is a fact however that a single reduced control model (static RCM) cannot be optimal for all network configurations and operating conditions. In pursuit of advanced online voltage control for a future smart transmission grid, this paper presents adaptive control model reduction (adaptive RCM) for CVR. The proposed formulation is based on a complex network representation of the transmission grid. Additionally, the formulation employs spectral clustering complemented by perturbation theory to deliver the adaptive RCM. Indicative results are presented on the New England 39-bus network and provide adequate justification of the proposed approach. The implications of an adaptive RCM scheme to control algorithm design and selection are also discussed.