(468g) Networked Predictive Control of Distributed Energy Systems with Adaptive Communication

The convergence of competition in the power industry with the arrival of environmentally friendly micro-turbines, fuel cells, photovoltaics, small wind turbines, and other advanced distributed power technologies has sparked strong interest in distributed power generation. This convergence of policy and technology promises to transform the electric power system from one that relies primarily on central generation to one in which small Distributed Energy Resources (DERs) provide most of the power needed. The resulting major improvement in power reliability, quality and efficiency; and the greater flexibility to respond to changing energy needs could save billions of dollars now lost each year because of power disruptions. An important challenge with this evolution is the development of integrated control and communication systems that can handle the large number and diversity of DERs spread over the grid, dispatch these resources at the right time and account for the flow of energy correctly. This is significant given the fact that the distributed power market is primarily driven by the need for super-reliable, high-quality power and the fact that the impact of of even a brief communication disruption (e.g., due to local network congestion or server outage) can be substantial, particularly in sites where millisecond outages can cause costly computer crashes.

An effort to address this problem was initiated in [1] where a model-based networked control approach was developed for DERs that communicate with a supervisor over a shared resource-constrained communication network. The main idea was to reduce the susceptibility to communication outages by reducing the rate at which the data are exchanged between each DER and the supervisor as much as possible without sacrificing the desired stability and performance properties. A dynamic model that supplies the supervisor with the needed DER state information when communication is suspended over the network was embedded in the supervisor, and updated periodically using the actual state provided by the DER sensors at discrete time instances.

In this contribution, we extend this model-based networked control structure in two key directions. The first is to incorporate optimality considerations, as well as state and control constraints, in the design of the local control systems. The second is to design the networked control system in a way such that the necessary communication rate can be determined and adjusted dynamically based on the state of the DERs. Dynamic feedback-based communication policies have the advantage that they are more robust to unpredictable disturbances and allow the supervisor to respond quickly in an adaptive fashion to a DER that requires immediate attention to minimize power disruption. To address both problems, we initially synthesize for each DER a Lyapunov-based model predictive controller that enforces closed-loop stability and achieves the desired power output in the absence of communication suspensions. The controller minimizes a finite-horizon cost functional that imposes penalties on the deviation from the desired power reference and on the control action, subject to state, control and Lyapunov-based stability constraints. An adaptive communication policy in which the Lyapunov stability constraint is used as the basis for switching on and off the communication between the local sensor suite of each DER and the supervisor is then devised. The idea is to locally monitor the evolution of the Lyapunov function of each DER plant such that if it begins to breach a pre-specified performance threshold at any time, the local sensor suite is prompted to send its data over the network to update the corresponding model in the supervisor and provide corrective control action. The results are illustrated through an application to a multi-source hybrid DER micro-grid that includes a photo-voltaic panel, a PEM fuel cell stack, and a battery.

References:

[1] Sun, Y., S. Ghantasala and N. H. El-Farra, "Networked Control of Distributed Energy Resources: Application to Solid Oxide Fuel Cells," Industrial and Engineering Chemistry Research, 48, 9590-9602, 2009.