With progressing utility deregulation and the need to eliminate unnecessary transmission and distribution costs, reduce greenhouse gas emissions, and improve the availability and reliability of electrical networks, a transformation in energy production is emerging from traditional, Central Generation to new, Distributed Generation. Distributed Generation (DG) is defined as the integrated or stand-alone use of small, modular electric generation deployed close to the point of consumption. Examples of power sources that fit the definition of DG include internal combustion engines coupled with generators, micro-turbines, fuel cells, and renewable systems (photo-voltaic arrays, wind turbines). As the number and diversity of distributed resources on 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. Another challenge is to ensure reliable communication and control between distributed resources and loads. Traditional supervisory control and data acquisition systems with centralized control rooms and dedicated communication lines, are not cost effective to handle a large number of DG resources spread over the grid.

New communication technology and software have become available to transform traditional centralized generation plants into networked control systems whereby the supervisor (a computer equipped with suitable communication and control software) can efficiently monitor the operational health of a large number of distributed generation resources. While managing distributed resources over a communication medium offers an appealing solution to the control of Distributed Generation, it poses a number of challenges due to the inherent limitations of communication networks which can temporarily interrupt the connection between the supervisor and the generation units. Therefore, any control strategy must be designed with communication constraints in mind.

In this study, we analyze on the problem of controlling distributed energy generation across communication networks and develop an integrated control and communication strategy that coordinates efficiently between the distributed sources. As a model system, we consider a network of fuel cell plants in a power distribution system. Each fuel cell plant is composed of a Solid Oxide Fuel Cell (SOFC) interfaced with the electric utility grid through a power conditioning unit, a measuring unit and a control unit. Real power supplied to the grid is measured and the measurement is fed back to the fuel stack controller which controls the real power by adjusting the volumes of input fuel and air. Currents and voltages to the grid are also measured and fed back to the inverter controller to track the real and reactive power set points and adjust the power factor. A customer (e.g., a house-owner) is represented by a load whose power consumption is registered by smart metering devices. The controllers of the fuel cells are connected to a communication network so they are able to communicate with one other and with the supervisory controller responsible for coordination between the fuel cell plants. Normally, each fuel cell unit only transfers the power produced to the utility grid, without having to exchange information with other units or metering devices. When a unit detects a distorted current, however, it may start compensating it autonomously by supplying a certain amount of nonactive current based on local measurements. The amount of nonactive current that each unit is able to provide should be traceable in order to receive the correct revenue from the customer. Therefore, information needs to be exchanged via the communication network in order to efficiently coordinate the compensation of nonactive currents. Using appropriate dynamic models of prototype SOFC fuel cell plants, we design a set of local controllers, one for each plant, and a supervisory controller that coordinates between them over the network to determine at any given time the appropriate levels of active power supplied to the grid by each unit as well as the non-active current that can be used to compensate for distorted currents. The control and communication logics account explicitly for the presence of communication constraints due to intermittent network failures and medium access constraints.