The Economics of Water: Taking Full Account of First Use, Reuse and Return to the Environment.A Report for the Australian Water Conservation and Reuse Research Program (AWCRRP).

There has been a lot of discussion about water being undervalued in Australia. The nation's attention is presently focussed on water issues with the drought of 2002 - 2003, the deepening crisis with the River Murray and water rationing in a number of urban areas. Over 2003-2005, the States and Territories will be required to demonstrate progress against the water reform goals in the area of urban and rural water pricing. This is an opportune time as part of the Australian Water Conservation and Reuse Research Program to develop a framework for the introduction of mechanisms that reveal the full cost of using water. This paper emphasises the importance of looking at potable water, reuse and disposal and return of water to the environment together as part of a comprehensive framework.catchment; Australia; water; water reuse

Predicting Strategic Energy Storage Behaviors

Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitrage behaviors for market power monitoring and capacity adequacy planning. This paper proposes a novel data-driven approach that incorporates prior model knowledge for predicting the strategic behaviors of price-taker energy storage systems. We propose a gradient-descent method to find the storage model parameters given the historical price signals and observations. We prove that the identified model parameters will converge to the true user parameters under a class of quadratic objective and linear equality-constrained storage models. We demonstrate the effectiveness of our approach through numerical experiments with synthetic and real-world storage behavior data. The proposed approach significantly improves the accuracy of storage model identification and behavior forecasting compared to previous blackbox data-driven approaches.Comment: accepted by IEEE Transactions on Smart Grid, 202

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

The Industrial Village Energy Approach: A Cost-Effective Approach To Balance Interests and Collaboratively Harness Onsite Solar Energy

While the residential sector has seen a strong and rapid uptake of photovoltaic panels on rooftops in the last decade, especially in Australia, the uptake has been much slower on commercial and industrial roofs. This research focuses on how commercial and industrial precincts can transition to cost-effective long-term solar energy generation in a manner that creates multiple benefits. The outcome is a new approach that benefits the energy customers, the embedded utility and the environment

Stationary, Second Use Battery Energy Storage Systems and Their Applications: A Research Review

publishedVersio

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science