Utilising solar energy within conventional coal fired power stations

Although the thermodynamic advantages of using solar energy to replace the bled off steam in the regeneration system of Rankine cycle coal fired power stations has been proven theoretically, the practical techno/economic feasibility of the concept has yet to be confirmed relative to real power station applications. To investigate this concept further, computer modelling software “THERMSOLV” was specifically developed for this project at Deakin University, together with the support of the Victorian power industry and Australian Research Council (ARC). This newly developed software simulates the steam cycle to assess the techno/economic merit of the solar aided concept for various power station structures, locations and local electricity market conditions. Two case studies, one in Victoria Australia and one in Yunnan Province, China, have been carried out with the software. Chapter one of this thesis defines the aims and scope of this study. Chapter two details the literature search in the related areas for this study. The thermodynamic concept of solar aid power generation technology has been described in chapter three. In addition, thermodynamic analysis i.e. exergy/availability has been described in this chapter. The “Thermosolv” software developed in this study is detailed in chapter four with its structure, functions and operation manual included. In chapter five the outcomes of two case studies using the “Thermosolv” software are presented, with discussions and conclusions about the study in chapters 6 and 7 respectfully. The relevant recommendations are then made in chapter eight.

Multi-point and multi-level solar integration into a conventional coal-fired power plant

Solar-aided power generation (SAPG) is capable of integrating solar thermal energy into a conventional thermal power plant, at multi-points and multi-levels, to replace parts of steam extractions in the regenerative Rankine cycle. The integration assists the power plant to reduce coal (gas) consumption and pollution emission or to increase power output. The overall efficiencies of the SAPG plants with different solar replacements of extraction steam have been studied in this paper. The results indicate that the solar thermal to electricity conversion efficiencies of the SAPG system are higher than those of a solar-alone power plant with the same temperature level of solar input. The efficiency with solar input at 330 °C can be as high as 45% theoretically in a SAPG plant. Even the low-temperature solar heat at about 85 °C can be used in the SAPG system to heat the lower temperature feedwater, and the solar to electricity efficiency is nearly 10%. However, the low-temperature heat resource is very hard to be used for power generation in other types of solar power plants. Therefore, the SAPG plant is one of the most efficient ways for solar thermal power generation.

Incorporation of a hybrid cooling system in water cooled power stations to reduce water and energy consumptions

Water shortage is a major problem facing the power industry in many nations around the world. The largest consumer of water in most power plants is the wet cooling tower. To assist water and energy saving for thermal power stations using conventional evaporative wet cooling towers, a hybrid cooling system is proposed in this paper. The hybrid cooling system may consists of all or some of an air pre-cooler, heat pump, heat exchangers, and adsorption chillers together with the existing cooling tower. The hybrid cooling system described in the paper, consisting of a metal hydride heat pump operating in conjunction with the existing wet cooling tower, is capable of achieving water saving by reducing the temperature of warm water entering the cooling tower. Cooler inlet water temperatures effectively reduce the cooling load on existing towers. This will ultimately reduce the amount of water lost to the air by evaporation whilst still achieving the same cooling output. At the same time, the low grade waste energy upgraded by the metal hydride heat pump, in the process of cooling the water, can be used to replace the bleed of steam for the lower stage feed heaters which will increase overall cycle efficiency.

Hybrid cooling system for water conservation in conventional power stations

This thesis proposes the concept of a hybrid cooling system to minimise water consumption in thermal power stations. It identifies that a significant amount of water can be potentially saved by the proposed system. Additional energy input requirements were less than previous approaches considered.

Power plant cooling technologies : hybrid cooling systems can save water

In addition to water saving, the hybrid cooling concept presented in this book also has the potential to improve energy efficiency and possibly reduce CO2 emission by recovering and upgrading the 'waste' energy from the cooling water stream.Yilmaz and Kouzani are at Deakin Uni, Hessami is at Monash Uni, Hu is at Uni of Adelaide.

Static output feedback frequency stabilization of time-delay power systems with coordinated electric vehicles state of charge control

In this paper, for the first time, electric vehicles are used for both the primary and secondary frequency controls to support power plants to rapidly suppress fluctuations in the system frequency due to load disturbances. Via networked control and wide-area communication infrastructures, multiple interval time-varying delays exist in the communication channels between the control center, power plant, and an aggregation of electric vehicles. By coordinating batteries’ state of charge control, the behaviors of the vehicle owners and the uncertainties imposed by the changes of the batteries’ state of charge are taken intoconsideration. A power system model incorporating multiple time-varying delays and uncertainties is first proposed. Then, a robust static output feedback frequency controller is designed to guarantee the resulting closed-loop system stable with an H∞ attenuation level. By utilizing a novel integral inequality, namely refined-Jensen inequality, and an improved reciprocally convex combination, the design conditions are formulated in terms of tractable linear matrix inequalities which can be efficiently solved by various computational tools. The effectiveness of the proposed control scheme is verified by extensive simulations.

Mining small databases by collecting knowledge

Current data mining techniques may not be helpful for mining some companies/organizations such as nuclear power plants and earthquake bureaus, which have only small databases. Apparently, these companies/organizations also expect to apply data mining techniques to extract useful patterns in their databases so as to make their decisions. However, data in these databases such as the accident database of a nuclear power plant and the earthquake database in an earthquake bureau, may not be large enough to form any patterns. To meet the applications, we present a new mining model in this paper, which is based on the collecting knowledge from such as Web, journals, and newspapers.

Collecting quality data for database mining

Data collecting is necessary to some organizations such as nuclear power plants and earthquake bureaus, which have very small databases. Traditional data collecting is to obtain necessary data from internal and external data-sources and join all data together to create a homogeneous huge database. Because collected data may be untrusty, it can disguise really useful patterns in data. In this paper, breaking away traditional data collecting mode that deals with internal and external data equally, we argue that the first step for utilizing external data is to identify quality data in data-sources for given mining tasks. Pre- and post-analysis techniques are thus advocated for generating quality data.

Stabilisation of brown coal fly ash using geopolymers

This research explores geopolymer technology as a means of stabilising fly ash from power stations. By controlling the synthesis process, geopolymeric materials incorporating fly ash were synthesised. Successful stabilisation of strontium and barium was achieved, though attributed to side reactions. The geopolymer did not contribute significantly to stabilisation of fly ash.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Solar aided power generation from coal fired power stations: THERMSOLV software

Although the thermodynamic advantages of using solar energy to replace the bleed off steam in the regeneration system of Rankine cycle coal fired power stations has been proven theoretically, the practical techno/economic feasibility of the concept has yet to be confirmed relative to real power station applications. To investigate this concept further, a computer modelling software “THERMSOLV” was developed by Deakin University researchers, together with the support of the Victorian power industry and Australian Research Council (ARC). This newly developed software simulates the steam cycle to assess the techno/economic merit of the solar aided concept for various power station structures, locations and local electricity market conditions. Two case studies, one in Victoria Australia and one in Yunnan Province, China, have been carried out to show the application of the software. This paper reports the structure and functions of the software, and the results of the two case studies.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.