Modern environmental improvement pathways for the coal power generation industry in Australia

In this work we assess the pathways for environmental improvement by the coal utilization industry for power generation in Australia. In terms of resources, our findings show that coal is a long term resource of concern as coal reserves are likely to last for the next 500 years or more. However, our analysis indicates that evaporation losses of water in power generation will approach 1000 Gl (gigalitres) per year, equivalent to a consumption of half of the Australian residential population. As Australia is the second driest continent on earth, water consumption by power generators is a resource of immediate concern with regards to sustainability. We also show that coal will continue to play a major role in energy generation in Australia and, hence, there is a need to employ new technologies that can minimize environmental impacts. The major technologies to reduce impacts to air, water and soils are addressed. Of major interest, there is a major potential for developing sequestration processes in Australia, in particular by enhanced coal bed methane (ECBM) recovery at the Bowen Basin, South Sydney Basin and Gunnedah Basin. Having said that, CO2 capture technologies require further development to support any sequestration processes in order to comply with the Kyoto Protocol. Current power generation cycles are thermodynamic limited, with 35-40% efficiencies. To move to a high efficiency cycle, it is required to change technologies of which integrated gasification combined cycle plus fuel cell is the most promising, with efficiencies expected to reach 60-65%. However, risks of moving towards an unproven technology means that power generators are likely to continue to use pulverized fuel technologies, aiming at incremental efficiency improvements (business as usual). As a big picture pathway, power generators are likely to play an increasing role in regional development; in particular EcoParks and reclaiming saline water for treatment as pressures to access fresh water supplies will significantly increase.

Sustainability metrics for coal power generation in Australia

The basis of this work was to investigate the relative environmental impacts of various power generators knowing that all plants are located in totally different environments and that different receptors will experience different impacts. Based on IChemE sustainability metrics paradigm, we calculated potential environmental indicators (P-EI) that represent the environmental burden of masses of potential pollutants discharged into different receiving media. However, a P-EI may not be of significance, as it may not be expressed at all in different conditions, so to try and include some receiver significance we developed a methodology to take into account some specific environmental indicators (S-EI) that refer to the environmental attributes of a specific site. In this context, we acquired site specific environmental data related to the airsheds and water catchment areas in different locations for a limited number of environmental indicators such as human health (carcinogenic) effects, atmospheric acidification, photochemical (ozone) smog and eutrophication. The S-EI results from this particular analysis show that atmospheric acidification has highest impact value while health risks due to fly ash emissions are considered not to be as significant. This is due to the fact that many coal power plants in Australia are located in low population density air sheds. The contribution of coal power plants to photochemical (ozone) smog and eutrophication were not significant. In this study, we have considered emission related data trends to reflect technology performance (e.g., P-EI indicators) while a real sustainability metric can be associated only with the specific environmental conditions of the relevant sites (e.g., S-EI indicators).

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.

A real options based method for power system planning

A deregulated electricity market is characterized with uncertainties, with both long and short terms. As one of the major long term planning issues, the transmission expansion planning (TEP) is aiming at implementing reliable and secure network support to the market participants. The TEP covers two major issues: technical assessment and financial evaluations. Traditionally, the net present value (NPV) method is the most accepted for financial evaluations, it is simple to conduct and easy to understand. Nevertheless, TEP in a deregulated market needs a more dynamic approach to incorporate a project's management flexibility, or the managerial ability to adapt in response to unpredictable market developments. The real options approach (ROA) is introduced here, which has clear advantage on counting the future course of actions that investors may take, with understandable results in monetary terms. In the case study, a Nordic test system has been testified and several scenarios are given for network expansion planning. Both the technical assessment and financial evaluation have been conducted in the case study.

Power generation investment opportunities evaluation: A comparison between net present value and real options approach

To evaluate an investment project in the competitive electricity market, there are several key factors that affects the project's value: the present value that the project could bring to investor, the possible future course of actions that investor has and the project's management flexibility. The traditional net present value (NPV) criteria has the ability to capture the present value of the project's future cash flow, but it fails to assess the value brought by market uncertainty and management flexibility. By contrast with NPV, the real options approach (ROA) method has the advantage to combining the uncertainty and flexibility in evaluation process. In this paper, a framework for using ROA to evaluate the generation investment opportunity has been proposed. By given a detailed case study, the proposed framework is compared with NPV and showing a different results

A cognitive genetic algorithm for power distribution system planning

Power systems are large scale nonlinear systems with high complexity. Various optimization techniques and expert systems have been used in power system planning. However, there are always some factors that cannot be quantified, modeled, or even expressed by expert systems. Moreover, such planning problems are often large scale optimization problems. Although computational algorithms that are capable of handling large dimensional problems can be used, the computational costs are still very high. To solve these problems, in this paper, investigation is made to explore the efficiency and effectiveness of combining mathematic algorithms with human intelligence. It had been discovered that humans can join the decision making progresses by cognitive feedback. Based on cognitive feedback and genetic algorithm, a new algorithm called cognitive genetic algorithm is presented. This algorithm can clarify and extract human's cognition. As an important application of this cognitive genetic algorithm, a practical decision method for power distribution system planning is proposed. By using this decision method, the optimal results that satisfy human expertise can be obtained and the limitations of human experts can be minimized in the mean time.

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Classical and quantum signatures of competing chi((2)) nonlinearities

We report the observation of the quantum effects of competing chi((2)) nonlinearities. We also report classical signatures of competition, namely, clamping of the second-harmonic power and production of nondegenerate frequencies in the visible. Theory is presented that describes the observations as resulting from competition between various chi((2)) up-conversion and down-conversion processes. We show that competition imposes hitherto unsuspected limits to both power generation and squeezing. The observed signatures are expected to be significant effects in practical systems.

Optimization of pipeline transport for CO2 sequestration

Coal fired power generation will continue to provide energy to the world for the foreseeable future. However, this energy use is a significant contributor to increased atmospheric CO2 concentration and, hence, global warming. Capture and disposal Of CO2 has received increased R&D attention in the last decade as the technology promises to be the most cost effective for large scale reductions in CO2 emissions. This paper addresses CO2 transport via pipeline from capture site to disposal site, in terms of system optimization, energy efficiency and overall economics. Technically, CO2 can be transported through pipelines in the form of a gas, a supercritical. fluid or in the subcooled liquid state. Operationally, most CO2 pipelines used for enhanced oil recovery transport CO2 as a supercritical fluid. In this paper, supercritical fluid and subcooled liquid transport are examined and compared, including their impacts on energy efficiency and cost. Using a commercially available process simulator, ASPEN PLUS 10.1, the results show that subcooled liquid transport maximizes the energy efficiency and minimizes the Cost Of CO2 transport over long distances under both isothermal and adiabatic conditions. Pipeline transport of subcooled liquid CO2 can be ideally used in areas of cold climate or by burying and insulating the pipeline. In very warm climates, periodic refrigeration to cool the CO2 below its critical point of 31.1 degrees C, may prove economical. Simulations have been used to determine the maximum safe pipeline distances to subsequent booster stations as a function of inlet pressure, environmental temperature and ground level heat flux conditions. (c) 2005 Published by Elsevier Ltd.