Smart Grids Seminar Series
The Smart Grids Seminar Series is a joint effort of the Centre for Future Energy Networks and the Centre of Excellence in Telecommunications at the School of Electrical Engineering and Information Technologies. A seminar is presented every four weeks, alternating between power and telecoms topics.
Harnessing Demand Flexibility to Match Renewable Production and Provide Ancillary Services
Professor Ross Baldick, The University of Texas at Austin
Thursday, 2 August 2012, 1:00pm
Abstract:Intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory.
In this work we focus on harnessing demand flexibility as a key to enabling more renewable integration. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing “grid friendly” charging modes of plug-in electric vehicles.
We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of ancillary services to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads.
Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner.
Smart gird electricity demand forecasting based on real-time data mining model
Mr Wenzhu Huang
Thursday, 15 May 2012, 1:00pm
Abstract:Electricity load forecasting is a crucial point for the effective operation and planning of smart grid power systems. The forecasting accuracy has significant impact on electric utilities and regulators. Over estimation of electricity demand will cause a conservative operation, which lead to the startup of too many units supplying an unnecessary level of reserve or excessive energy purchase, as well as substantial wasted investment in the construction of excess power facilities. The purpose of this work is to develop a real-time data mining model. While traditional data mining model are base on hard-disk database analysis, this model are built with memorized stream data mining. It is more effective and efficient handling of huge amounts of real-time data to support ‘smart’ grid. This decision support data analysis model are based on artificial intelligence, statistics model such as decision tree, neural network and some machine learning methods. Currently load prediction model are in term of historical electricity data, population, GDP and weather factors in order to improve electricity demand prediction accuracy. Series forecasting models will be developed for renewable energy forecasting, such as wind power and solar power. Combined with demand responds prediction result, an optimization algorithm will designed for generation, distribution and access grid. Real time fault detection data mining model is used to analyze electricity voltages, frequencies and phase. It can real time monitoring the grid health state and detecting potential fault in smart grid.
Scalable optimisation methods for decision making and control in distributed systems
Dr George Mathews, NICTA
Thursday, 16 February 2012, 1:00pm
Abstract:Over the past decades model predictive control techniques have become standard for the control of linear and nonlinear systems. However as the systems become larger, physically distributed and more complex, centralised approaches become limited by the speed at which information can be communicated to/from a central location and the size of the optimisation problem that must be repeatedly solve. This talk will consider this distributed optimisation problem and present two methods that exploit the local computational and communication resources that may exist is the system. Examples will be given for the techniques applied to the control of distributed sensor networks undertaking search and surveillance tasks and directions of current and future work in the areas of state estimation and control of smart grids will be highlighted.
Interference and Spectrum Management for Wireless Smart Grid Cellular Networks
Dr Wibowo Hardjawana
Thursday, 10 November 2011, 1:00pm
Abstract:Spectrum scarcity and inter-cell interference (ICI) limit the maximum number of users that can be supported by wireless smart grid networks.
The standard wireless network uses Soft frequency reuse (SFR) scheme where transmit power and subcarriers are allocated in each cell prior to system deployment to reduce ICI and manage spectrum usage. This limits the potential performance of the SFR scheme. We propose an adaptive SFR (ASFR) iterative algorithm that jointly optimizes subcarrier and power allocation for multi-cell wireless networks to improve the system capacity of SFR scheme. The proposed scheme dynamically adjusts the number of major and minor subcarriers and their transmit powers in each cell according to its traffic loads. The algorithm first finds the optimal resource allocation in each single cell and then repeated iteratively among cells until a predefined convergence criterion is satisfied. Simulation results show that the proposed algorithm achieve higher system throughput and than traditional frequency reuse schemes.
Smart grid for researchers
Prof David Hill
Thursday, 15 September 2011, 1:00pm
Abstract: The likely development of smart grids can be viewed in several stages starting with establishing observability on the whole network. The Centre of Excellence in Intelligent Electricity Networks (CIEN) carries out advanced research in power electronics and systems, telecommunication, control, networking, software and computing all towards the development of smart grids for electricity distribution. It is supported by Ausgrid and the ARC. This seminar will review the research agenda which is emerging in current planning, with some emphasis on systems and control aspects, and consider some deep and meaningful questions about where we might be headed long-term. As in other areas of technology, as the control capability changes so does the basic system. Even putting aside the economic, political and social forces at play, there are enough major questions to answer to keep researchers busy for years to come.
Cooperative Cognitive Radio for Smart Grids
Dr Raymond Louie
Thursday, 18 August 2011, 4:00pm
Abstract:Two-way communications network for smart grids will require the capability to provide varying levels of reliability and delay QoS constraints, as well as support for a large number of smart metering nodes. Cognitive radio techniques, combined with cooperative relaying, offer a promising solution to support these requirements. We consider the use of relaying techniques to provide high reliability for applications having high QoS requirements, and the use of cognitive radio techniques to simultaneously allow for transmission from applications having lower QoS requirements. We present results demonstrating proof of concept for these techniques.
Smart Meter and HAN Communications
Dr Van Dong Pham
Thursday, 30 June 2011, 1:00pm
Abstract:This presentation highlights our developments in hardware, software, monitoring and control applications in Smart Meter and Home Area Network project. We present our work in network deployment with different network technologies for communications between wireless sensor networks and control systems. We also outline the interoperability between various technologies within the network in order to support real-time energy communications. A brief demonstration of the network deployment is also presented.
Towards a Smart House Energy Management System – A Dynamic Programming Approach
Mr Henning Tischer
Thursday, 2 June 2011
Abstract: In this presentation, we present an energy management system for a smart home. The smart home is equipped with a PV system, a fuel cell, an electric vehicle and a battery. The entire energy system is modelled as Markov and our management system is computed using Dynamic Programming. The consumption of electrical energy within the household, the usage and consumption of the electric vehicle and the electrical energy generated by the PV system are modelled as random variables with known stochastic properties. The availability of the electric vehicle according to the driverâ€™s preferences and habits is considered by the algorithm. We provide simulations of the performance of our described energy management system and a simpler management system that aims at generating as much electrical energy as possible within the household. They show that our approach enables the response of demand and generation in the household to the grid.