While managing DERs over a communication network offers an appealing modern solution to the control of distributed generation, it poses a number of challenges that must be addressed before DERs can be fully integrated into power networks. These challenges stem from 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 distributed generation units and the loads, and thus degrade the overall control quality if not properly accounted for in the control and communication designs. Despite the availability of fast and reliable industry-standard communication networks, the fact that the distributed power market is primarily driven by the need for super-reliable, high-quality power implies that the impact of even a brief communication disruption (e.g., due to local network congestion or server outage) can be substantial. In sites such as hospitals, police stations, data centers and high-tech plants which cannot afford blackouts, millisecond outages that merely cause lights to flicker will cause costly computer crashes [1]. Such high-stakes risks provide a strong incentive for the development of robust control and communication strategies that achieve the desired levels of power supply and quality from DERs while minimizing reliance on the communication medium, which in turn minimizes the impact of data losses and disruptions on the power supply (see, for example, [2]).
One of the central problems in distributed energy generation is how to optimally dispatch and schedule DERs in real-time. This problem is typically addressed on the basis of economic considerations, such as minimizing the overall energy costs in real-time (see, for example, [3]). When a large number of DERs are deployed and communicate with a central control authority over a shared communication network, however, the intrinsic limitations on the number of DERs that can access the network at any given time become a critical factor in deciding the optimal scheduling policy. In practice, communication networks typically have a limited number of channels so that at any one time only some of the sensors and actuators can exchange information with the supervisor, while others must wait. Under these constraints, the performance of the control loops is dependent not only on the controller design for the individual DERs but also on the selection of the medium access strategy that would assign a transmission schedule to each transmission entity on the network based on a scheduling algorithm (i.e., a set of rules that, at any time, determines the order in which messages are transmitted). Communication-based scheduling also represents an important way of conserving the network's energy resources by selecting and dispatching only a subset of the deployed DERs at any given time.
To address this problem, we consider as a model system for our study a network of fuel cell plants in a power distribution system. The fuel cells are connected to a communication network over which they communicate with the supervisor (typically a computer equipped with suitable communication and control software) responsible for coordinating the levels of power supplied to the grid by each plant. Each fuel cell plant is composed of a Solid Oxide Fuel Cell (SOFC) fueled by biomass gas and interfaced with the electric utility grid through a power conditioning unit, a measuring unit and a control unit. While the utilization of a renewable energy source, such as biomass energy, is desirable to reduce both the consumption of fossil resources and the emission of carbon dioxide, operating a fuel cell on biomass gas can lead to an unstable power output. This instability is due to the fluctuation of gas composition in the fuel caused by the decomposition process of biomass resources (e.g., anaerobic digestion) and necessitates the use of feedback control to allow the fuel cell plant to rapidly follow changes in power demand and to also maintain the power output supplied to the grid at the desired level.
To illustrate the main ideas, we consider a configuration where the the supervisor has un-interrupted access to measurements from each SOFC plant and only the local actuator suites in these plants vie for access to the supervisor. Communication is limited in the sense that only one SOFC plant can receive control commands from the supervisor at any one time to stabilize the power output. The objective is to find a sequence for establishing and terminating communication without performance degradation in the collection. Both static scheduling, where the communication sequence is pre-determined off-line, and dynamic (i.e., feedback-based) scheduling policies are considered. The basic idea is to formulate the closed-loop system for each SOFC plant as a switched system that transitions between two modes of operation depending on whether access to the supervisor is granted or denied. By analyzing the behavior of each subsystem, we obtain an estimate of the maximum allowable rate of communication disruption that each plant can tolerate while maintaining its power output at the desired level. The estimate obtained depends on the time constant for each plant, the controller design parameters as well as the biomass feed composition fluctuations. Based on the various tolerance levels of the different plants, a static transmission schedule is then derived using the rate monotonic algorithm whereby priority is given to plants with faster dynamics and lower communication disruption tolerances. In addition to developing static schedules, we use Multiple Lyapunov Functions (MLF) techniques to devise dynamic (i.e., state-dependent) scheduling policies that are suitable for on-line implementation, are more robust when the power distribution system is subject to unpredictable disturbances, and allow the supervisor to respond quickly to a plant that requires immediate attention. The design, implementation and efficacy of the various scheduling policies are demonstrated using computer simulations of prototype SOFC plant models.
References:
[1] Borbely, A. and J. F. Kreider. Distributed Generation: The Power Paradigm of the New Millennium, CRC Press, Boca Raton, FL, 2001.
[2] Sun, Y., S. Ghantasala and N. H. El-Farra, ``Control of Distributed Energy Generation Over Communication Networks," AIChE Annual Meeting, paper 484g, Salt Lake City, Utah, 2007.
[3] Bae, I., J-O Kim, J-C Kim, and C. Singh, ``Optimal operating strategy for distributed generation considering hourly reliability worth," IEEE Transactions on Power Systems, 19, 2004.