• Energy Research

According to Richard Feynman, it doesn't matter, in which way one obtains Schroedinger equation ??? provided it is Schroedinger equation that is obtained.

In face of the increasing concern on global warming and climate change, the interests in the utilization of solar energy for building operation are also rapidly growing. In this paper, the importance of using renewable energy in building operations is first discussed. The potential use of solar energy is then reviewed. Possible applications of solar energy in building operation are also discussed, including the use of solar energy in the forms of daylighting, hot water heating, space heating and cooling and building-integrated photovoltaics. Finally, the research activities in the utilization of solar energy for space cooling at QUT are highlighted.

How can data and digitalisation bring new insights to operational efficiency in shipping and help capitalise on the opportunity presented? Our new Insight Brief explores this question as part of a series that examines the undervalued opportunity presented by operational efficiency.

We propose an optimal electric and heat energy management for a cooperative multi-microgrid community. The sequentially-coordinated operation for heat energy is proposed in order to distribute the computational burden as an extension of “Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations” and is following the sequentially-coordinated operations for electric energy in it. This sequentially-coordinated operation for heat energy is mathematically modeled and how to obtain the global heat energy optimization solution in the cooperative multi-microgrid community is presented. The global heat energy optimization is achieved for the cooperative community by adjusting the combined electric and heat energy production amounts of combined heat and power (CHP) generators and the heat energy production amount of heat only boilers (HOBs) which satisfy all heat loads, as well as optimize the external electric energy trading in order to minimize the unnecessary cost from the external electric trading, and/or maximize the profit from the external electric trading. To validate the proposed mathematical energy management models, a simulation study is also conducted.

The global energy landscape is going through major shifts triggered by consumer preferences, regulations, and technological development. My dissertation develops optimization models to de-rive insights into strategic decisions in energy operations management. In the first essay, I examine how blockchain-enabled peer-to-peer energy trading shifts electricity consumers’ investment in renewable energy. Using the equilibrium model, I show that electricity consumers are always better off by participating in the virtual network, with their resulting cost savings averaging 9.7%. I also prove that blockchain is able to fully coordinate heterogeneous participants in the network to minimize the total cost in the system. The second essay addresses how a Transmission System Operator (TransCo) can optimally invest in a long-distance transmission line to allow renewable energy development by a Power Generation Company (GenCo) in a geographically remote region. Using a continuous-time, infinite-horizon, Stackelberg game between TransCo and GenCo, I show that transmission and generation act as complements with regard to the value functions for both companies. I derive the value-maximizing transmission fee charged by TransCo to GenCo for each unit of energy exported via transmission lines. I characterize a Pareto-improving cost-sharing contract through which both companies can improve the value of their investment. The third essay focuses on how to better manage a decentralized supply chain of an oil-field service company. To minimize the transportation and inventory holding costs of different members in a cross-docking supply chain, I formulate multi-period, mixed-integer programming models. I use structural properties of optimal solutions to show that different collaborations in the supply chain can generate significant cost savings for individual supply chain members, whereas the quantified cost savings exhibit significant variations depending on product weight and holding cost. I also develop a Stackelberg pricing game ...

The Wave Energy Maintenance Model, as described in the Summary, was produced to allow the effect of variations in WEC design or in maintenance strategy on the total Operation and Maintenance (O & M) costs of wave energy array to be easily and quickly calculated and assessed. Since the model is parametric, factors can be changed and the results of the changes can be seen in the costs of the various operations. Having this facility available, an analysis of the sensitivity of the O & M costs to changes in certain parameters has been possible. The results presented in this report are not device specific. They are based on an array of devices which have the main plant items on-board the WECs. The devices may be bottom sitting or floating. The aim of the results shown here is to indicate trends in O & M costs and to provide some basic rules of thumb that can be applied to O & M costs for any device. The actual costs given are of the right order of magnitude for current devices, but do not represent the actual O & M costs of a specific device. Throughout the report, O & M costs are broken down into the same Cost Centres as used in the Model and described in the Model Summary. This report also contains an Appendix which illustrates the inter-dependancies existing between cost items in different areas of the Model. This allows the full effect of a change in parameters to be seen. It also describes the fundamental assumptions implicit in these results presented in this report.

Algae biodiesel is the sole sustainable and abundant transportation fuel source that can replace petrol diesel use; however, high competition and economic uncertainties exist, influencing independent venture capital decision making. Technology, market, management, and government action uncertainties influence competition and economic uncertainties in the venture capital industry. The purpose of this qualitative case study was to identify the best practice skills at IVC firms to predict uncertainty between early and late funding stages. The basis of the study was real options theory, a framework used to evaluate and understand the economic and competition uncertainties inherent in natural resource investment and energy derived from plant-based oils. Data were collected from interviews of 24 venture capital partners based in the United States who invest in algae and other renewable energy solutions. Data were analyzed by coding and theme development interwoven with the conceptual framework. Eight themes emerged: (a) expected returns model, (b) due diligence, (c) invest in specific sectors, (d) reduced uncertainty-late stage, (e) coopetition, (f) portfolio firm relationships, (g) differentiation strategy, and (h) modeling uncertainty and best practice. The most noteworthy finding was that predicting uncertainty at the early stage was impractical; at the expansion and late funding stages, however, predicting uncertainty was possible. The implications of these findings will affect social change by providing independent venture capitalists with best practice skills to increase successful exits, lessen uncertainty, and encourage increased funding of renewable energy firms, contributing to cleaner and healthier communities throughout the United States.

As the demand for energy grows, there is a substantial push to adopt sustainable and inherently carbon-neutral renewable energy sources. However, the intermittency and non-dispatchability of renewables requires measures to improve grid flexibility. These measures include the frequent cycling of the conventional, fossil-based generating units and energy storage. While conventional plant cycling reduces its efficiency and plant lifetimes, grid-scale energy storage is capital-intensive with only a limited number of suitable candidate technologies. Due to these integration challenges associated with renewables, it is difficult to completely replace the dispatchable fossil generators. CO₂ capture, utilization and storage provides a means to reduce emissions from fossil power plants, but its large-scale deployment is currently limited by high cost and high energy requirement. In this Ph.D. work, the limitations of these decarbonization technologies are addressed through the development of computational frameworks and methodologies. To begin with, a flexible operation strategy is proposed for CO₂ capture in dynamic, uncertain pricing-driven electricity markets to reduce its high energy consumption. This framework is then extended to leverage the operational synergies between renewables and CO₂ capture to address their individual challenges together. A decentralized scheme is further proposed for the integration of energy storage with individual fossil power plants to reduce the costs associated with power plant cycling as well as grid-scale energy storage while accommodating renewable energy. A comprehensive software prototype, THESEUS, is presented to enable the optimal design and operation of the decentralized system, as well as the systematic downselection and comparison of energy storage technologies for different clean energy applications. Using THESEUS, one can determine the best-suited storage technology from a suite of nine technologies at different technology maturity levels, perform detailed techno-economic ...

The detailed derivation processes of Eq. (26)-(31) in the paper "An Energy-efficient Timetabling Approach with Consideration of Varying Train Mass and Real-world Speed profiles for Metro Systems".