(212d) Enhancing the Performance of Networked Distributed Energy Systems through Control and Communication Co-Design

Distributed energy systems, such as distributed generation technologies, renewable energy sources, and hybrid generation systems, offer advantages over conventional grid electricity by offering end users a diversified fuel supply; higher power reliability, quality, and efficiency; lower emissions and greater flexibility to respond to changing energy needs. As the number and diversity of distributed energy systems connected to the grid increases, dispatching these resources at the right time and accounting for the flow of energy correctly become complex problems that require reliable monitoring and telemetering equipment, as well as reliable communication and control technologies. Traditional supervisory control and data acquisition systems with centralized control rooms, dedicated phone lines, and specialized operators, are not cost effective to handle a large number of distributed resources spread over the grid. According to some estimates [3], the market potential for advanced control and communications technologies in managing distributed energy systems (based on 5-10% energy savings achieved) is between $3.75 billion and $7.5 billion domestically, and between $15 billion and $30 billion worldwide.

Over the past decade, several efforts have been made towards the development and implementation of control strategies for distributed energy systems (e.g., [1],[5]). While the focus of these studies has been mainly on demonstrating the feasibility of the developed control algorithms, the explicit characterization and management of communication constraints in the formulation and solution of the control problem have not been investigated. These are important problems given the inherent limitations on the information transmission and processing capabilities of communication networks, such as bandwidth limitations, network-induced delays, data losses, signal quantization and real-time scheduling constraints, which can interrupt the connection between the central control authority (the supervisor), the generation units and the loads, and consequently degrade the overall power supply and quality. These considerations provide an incentive for the development of robust control and communication strategies that ensure the desired levels of power supply and quality while minimizing the susceptibility of the overall system to data losses and communication outages. An effort to address this problem was initiated in [6] where a model-based networked control approach was developed for distributed generation systems that communicate with the central controller over a bandwidth-constrained communication network. The approach is resource-aware in that it guarantees stability of the power outputs while keeping the communication over the network to a minimum. Using hybrid system techniques, an exact characterization of the minimum allowable communication rate between each system and the controller was obtained. To further conserve network resources, this approach was coupled in [2] with a scheduling strategy in which only a subset of the deployed systems was allowed to communicate with the supervisor at any given time.

Beyond handling network resource constraints, another important consideration in the design of networked distributed energy systems is their ability to handle external disturbances which, if unaccounted for, can cause poor performance and may even lead to instability. The presence of disturbances alters not only the stability and performance properties of the constituent subsystems but also changes the optimal rate at which they need to communicate with the central controller to ensure the desired performance level. It is therefore important to assess the performance of these systems under disturbances and to explicitly characterize their impact on the control and communication policies. This analysis is useful in guiding the selection of optimal control and scheduling designs that can ensure minimal influence of the disturbances on the performance of the distributed energy network.

In this paper, we develop an integrated control and communication approach for managing distributed energy systems over resource-constrained communication networks. The objective is to enhance the performance and disturbance-handling capabilities of the distributed systems while keeping the communication requirements with the supervisor to a minimum. To this end, the rate of data transfer from each unit to the supervisor is initially minimized by embedding in the supervisor a model that is used to calculate the necessary control action when measurements are not transmitted over the network, and then updating the model state at discrete time instances. Only one unit is allowed to transmit its data at any given time to provide updates to its target model according to a certain transmission schedule. To analyze the performance properties of the networked scheduled closed-loop system under disturbances, we use the extended H₂-norm [4] of the power outputs as the performance measure. This measure captures the speed at which the power output recovers following a disturbance. The analysis leads to an explicit characterization of the interdependence between the performance of the distributed systems, the communication rate, the transmission schedule and times, and the plant-models mismatch. It is shown that by judicious selection of the control and communication design parameters, it is possible to enhance the performance of the system while simultaneously reducing network utilization. Finally, the results are demonstrated through a simulation case study involving control of a collection of solid oxide fuel cells in a distributed power network.

References:

[1] Barsali, S., M. Ceraolo, P. Pelacchi, and D. Poli, ``Control Techniques of Dispersed Generators to Improve the Continuity of Electricity Supply", Proceedings of IEEE Power Eng. Soc. Win. Mtg., pp. 789-794, 2002.

[2] El-Farra, N. H., Y. Xu, Y. Sun and S. Ghantasala, ``Networked Control and Scheduling of Distributed Energy Resources: Application to Biomass Gas-Fueled Fuel Cell Networks", AIChE Annual Meeting, paper 641c, Philadelphia, PA, 2008.

[3] Lovins, A.B. et al, Small Is Profitable: The Hidden Economic Benefits of Making Electrical Sources the Right Size, The Rocky Mountain Institute, 2002.

[4] Montestruque, L. A. and P. J. Antsaklis, ``Performance Evaluation for Model-based Networked Control Systems", Netw. Emb. Sens. and Cntrl., LNCIS, 331:231-249, P.J. Antsaklis and P. Tabuada (Eds.), Springer-Verlag, Berlin, 2006.

[5] Ro, K. and S. Rahman, ``Control of Grid-Connected Fuel Cell Plants for Enhancement of Power System Stability", Renewable Energy, 28:397-407, 2003.

[6] Sun, Y., S. Ghantasala and N. H. El-Farra, ``Networked Control of Distributed Energy Resources: Application to Solid Oxide Fuel Cells", Proceedings of American Control Conference, to appear, St. Louis, MO, 2009.

.