Locating Hybrid Fuel Cell-Turbine Power Generation Units under Uncertainty

Hybrid gas turbine-solid oxide fuel cell power generation has the potential to create a positive economic and environmental impact. Annually, the U.S. spends over $235 billion on electricity, and electric utilities emit 550 million metric tons of carbon. The integration of distributed hybrid generation can reduce these emissions and costs through increased efficiencies. In this paper, a model is presented that minimizes the costs of distributed hybrid generation while optimally locating the units within the existing electric infrastructure. The model utilizes data from hybrid generation modules, and includes uncertainty in customer demand, weather, and fuel costs. Introduction In recent years, distributed generation has received increasing attention from both the engineering and business communities. Distributed generation is defined as the placement of power generating modules of 30 megawatts (MW) or less near the end user [1]. These modules can be used to entirely replace larger central power plants, or can be used for peak shaving and standby power. Distributed generation power modules may either be connected to the power grid or operated in isolated conditions. Many of the technologies being considered for distributed generation applications are attractive both economically and environmentally. Some common distributed generation technologies are reciprocating internal combustion engines, for applications of less than 10 MW; combustion turbines, for applications larger than 5 MW; microturbines, which can produce between 30 and 200 kilowatts (kW); and fuel cells, which have the potential to generate power in the MW range [2]. When solid oxide fuel cells (SOFCs) and turbines are combined into hybrid power generating systems, unprecedented levels of efficiency can be achieved. Within the past year, Siemens Westinghouse has produced a 220-kW hybrid power system that is capable of generating electric power at 55% efficiency. Additionally, by 2002, the EPA’s Environmental Science Center at Fort Meade will be powered by a 1-MW hybrid fuel cell-turbine plant. These prototypes already show great promise in increasing efficiency and lowering global warming gas emissions. It has been predicted that hybrid systems will be able to achieve efficiencies of 70-80%, and that hybrid plants will produce 50 times less nitrous oxide than current conventional gas turbines and 75% less carbon dioxide than coal-fired power plants [3]. While it is likely that hybrid systems will indeed greatly reduce greenhouse gas emissions while producing economical power generation, the estimated savings that are commonly published do not account for the total economic impact of implementing distributed hybrid generation systems. The research outlined in this paper will focus on the analyses that must be performed in order to examine the economic feasibility and consequences of implementing hybrid fuel cell-turbine systems as distributed generation units. First, the optimal placement of the hybrid power modules must be considered so that they can be effectively interconnected with the existing electric infrastructure. Through data collection and mathematical modeling techniques, we have replicated a city, with its various industrial, commercial, and residential power needs, and its surrounding region for an urban area similar to Pittsburgh, Pennsylvania. Pittsburgh is a medium-sized American city that traditionally generates much of its power using coal, but that has a well-established natural gas infrastructure. Additionally, many Pittsburgh electric customers are already exploring alternative power generation sources, including local generation and low-impact wind farms. The power needs of the various sectors (which are realistically distributed geographically) have been evaluated, and plants have been assigned a variety of generation potentials, and then placed so as to minimize costs (and, incidentally, transmission losses). Since the operation of power generation systems involves a significant amount of uncertainty, any model for optimally designing these systems must address this issue. Uncertainty can include varying supply and demand as well as factors such as the regulatory environment. The demand for different types of energy is also uncertain, and depends on factors such as economic growth, availability, and price relative to other types of energy. Other uncertain aspects include environmental impact, technological change, and available raw materials. Clearly, uncertainty is pervasive in the design and operation of power systems. Stochastic programming is an appropriate tool for many problems arising in the optimal design of power systems, including facility location and network design. Additionally, scenarios can represent various combinations of the uncertain aspects of energy problems. The model outlined above includes a variety of uncertain factors, such as power demand, raw fuel costs, and weather patterns. Energy security and network reliability issues have also been considered. As the demand for electricity grows, and as homeland security becomes a higher priority, the issue of energy security will become increasingly important. When distributed generation units fail due to either natural causes or deliberate actions, the integrity of the power grid must be maintained. It is possible that the use of distributed generation will be able to increase energy security without increasing electricity costs. Although the current model focuses on minimizing the economic impact, it is possible to adapt the model to also incorporate environmental factors. This adaptation could be implemented through the incorporation of a life-cycle assessment of a hybrid generation unit. This assessment must focus not only on the fuel cell and the gas turbine, but also on the complete system. During the operating life of the hybrid plant, fuel resource usage costs and waste disposal costs should also be incorporated. Whether evaluating hybrid generation solely on an economic or also on an environmental basis, the results from the model can be used to not only compare hybrid distributed generation with conventional power generation techniques, but also with “cutting-edge” technologies, such as photovoltaics and wind farms. Distributed Generation and the Electric Industry Deregulation The recent energy crisis in California highlights some of the problems faced by America’s existing electric infrastructure. As the electric industry has undergone deregulation and restructuring, blackouts, capacity shortages, and high prices have become more common, as have fears about power reliability. Figure 1 shows the current status of deregulation in the United States [4]. Restructuring Legislation Enacted Comprehensive Regulatory Order Issued Legislative Investigation Ongoing No Activity Figure 1: Electricity Deregulation By State Under deregulation, the transmission, generation, and distribution functions of the utilities are divided into separate and distinct businesses. This has the potential to create an active power trading market, and increase the attractiveness of distributed power generation [5]. Distributed Generation When implemented in a partially or fully deregulated environment, distributed generation can be used to provide an alternative power source, reliable back-up power, or peak shaving. As an alternative power source, it is assumed that distributed hybrid generation would take the place of conventional high-emission central power plants that would otherwise be built. Distributed hybrid generation is also attractive as a source of stand-by power for companies that would incur large costs from the losses of productivity and profits that occur during blackouts. Utilizing distributed hybrid generation for peak shaving is attractive both economically and environmentally. As outlined in the previous subsection, not only do the highest electrical rates occur at peak times, but peak usage can also determine fixed charges for an entire year. Additionally, it is often the oldest and least efficient generators that must be utilized in order to meet peak power demands, thereby increasing