Electricity Networks & Energy Markets

Program Leader: Zhao Yang Dong, John Foster & Ariel Liebman

With the introduction of deregulation, the national electricity network has emerged as an excellent example of a complex system in need of an inter-disciplinary approach to modelling and design. This program investigated ways of integrating technical and market aspects of power system with price dynamics to provide key insights into planning expansion of the power transmission network. It also aimed to apply modern computational modelling techniques to the interface between the physical properties of the electricity system and its economic considerations. A particular focus was placed on the impacts of the transmission network and power station operation on electricity price behaviour and its influence on infrastructure investment decisions. It also looked into the importance of customer-load impact on system and market operations.

Projects:

Power system transmission network service pricing using probabilistic analysis techniques

Project Leader: Zhao Yang Dong
Researcher: Benson Gene Heng Li

The power transmission system is an essential part of the overall power grid. Despite the deregulation of the power industry over the past decades, the transmission network remains regulated because of its critical role in system security and reliability. However, with the generation side deregulated into a competitive market, there is increasing pressure to improve the efficiency of the transmission network services. Proper transmission pricing in a market situation is the key element in improving the efficiency and reliability of the transmission network services. Traditionally, transmission pricing methods depend on the power flow and system losses due to power transfer throughout the network. These techniques do not consider the social and economic impact. This project explores the social and economic impacts as well as the power flow information in the transmission network using probabilistic methods to form a framework of transmission pricing which can enhance the efficiency and reliability of the transmission network services. State-of-the-art transmission pricing methods have been studied which include Contract Based Method, Distance Based MW-Mile Method, and Monetary Flow Method. A new probability-based transmission pricing method was developed and tested on bench mark electricity market power grids including an IEEE 30 bus system. Compared with other transmission pricing methods in practice, the new method provides a comprehensive approach for setting the prices for transmission services in an electricity market. (Summer Project 2004/05)

Optimal allocation of embedded renewable electricity sources throughout the distribution network

Project Leader: Ariel Liebman
Researchers: David Abramson, Ngoc Dinh, Zhao Yang Dong, John Foster, Tien Duc Pham, Liam Wagner, Phillip Wild

This study required the simulation of the Australian electricity network down to the level of the distribution sub-station where we assumed that generation resources are connected at the sub-station and the total amount of generation is less than 30MW (the minimum level currently required for registration with the National Electricity Market). The study was formulated as a combinatorial optimisation problem where the total generation at each substation was composed of several (N) units of renewable and low emission generation resources selected from the following set of generation technologies: wind turbines, solar photovoltaic, solar thermal, biomass, micro-turbine (gas-fired), and geo-thermal. Each of these different types of generating technologies have different capital costs, fixed maintenance costs, and variable operation costs. Additionally, the rate at which the capital costs decrease over a long term horizon is different for each technology, with each rate being uncertain and having different degrees of uncertainty. Additional uncertainties exist in the long term forecasts for demand growth and fuel costs (e.g. coal, gas, distillates and bio-fuels such as bio-diesel).

In order to perform the simulation required by this project, the Plexos electricity market simulation software needs to be used. As Plexos is highly computationally intensive it requires the Nimrod grid computing engine. In 2007 a software development effort began to diversify Nimrod for the Windows platform which is currently the only platform on which Plexos operates.

This two part project reached two major milestones in 2008. In the technical part of the project, the Nimrod high-performance computing scheduling software was migrated to the Windows platform to enable integration with the Plexos market simulation software (www.energyexemplar.com), a commercial energy market software tool used in 30 countries around the world. In the economics component of the project, a model of a 30 node micro-grid connected to a main grid was implemented in Plexos to model a test system where the forecast growth in electricity demand cannot be met using the grid’s existing network infrastructure. Once integrated with Nimrod, the user will be able to determine the economically optimal deployment of renewable generation sources inside this grid to meet demand.

  • Cao, G., Dong, Z., Wang, Y., Zhang, P., “A SVC controller design for power systems with FACTS devices”, IEEE Power Engineering Society General Meeting, 2007.

Electricity market price analysis and risk management with advanced data mining techniques

Project Leader: Zhao Yang Dong
Researchers: Jason Ke Meng, Dianhui Wang, Kit Po Wong, Xia Donna Yin, Junhua Zhao, Joe Xun Zhou

This project aimed to develop methodologies and tools for market analysis and system security assessment from a complex system’s point of view using data-mining based methods. The electricity network as a complex system exhibits economical as well as physical characteristics. This research will look into two of the most important aspects of an electricity network in a market environment, namely market price and system security. Specifically, the objectives of this project were to investigate the complex and highly volatile price spikes in an electricity market, and to develop advanced tools to correctly model and predict the price spike.

At the same time, the power system behind the electricity market must maintain a secure state at all times to ensure the functionality of the market. System security/stability is the utmost responsibility of the system operator (usually also the market manager). This project also investigated the features of an electricity network with respect to its stability/security. The aim was to identify the major factors that contribute to possible system failure, and to predict instability events which may lead to system blackouts.

In 2007, a framework for power system transmission and expansion to allow flexibility of planning options was developed. This framework explores the market as well system analysis methods in planning area. It includes techniques developed earlier as well as proposed new research methodologies. An ARC Discovery Project application was lodged in 2008 based on research into this framework. As part of this general framework, previous research in electricity market forecasting, in power system security assessment and in decision making are also included. Four journal papers were published. A paper was published with SIGKDD, the premier conference in data mining area. The research has lead to a comprehensive approach for price spike analysis, and for power system contingency assessment to prevent cascading failure.

2008 achievements included a general methodology for electricity market price modelling and risk management methods particularly for generation company investment/planning. A comprehensive price modelling tool was developed. This tool can handle price series, intervals of electricity price, and price spikes so as to provide useful information for risk management in an electricity market. Methodologies for risk management target various risk factors in the market related to operations and planning, such as emissions trading impacts, uncertainties of generation new entry with respect to transmission planning, and uncertainties in demand forecasting. Publications in this area in 2008 included IEEE Transactions, top conferences and refereed book chapters. A number of PhD students involved with this project also successfully completed their theses.

  • Chen, X., Dong, Z., Wang, D., “Bootstrap prediction intervals for ARMA-GARCH based electricity market price forecasting”, Journal of Electric Power Science and Technology, Vol. 23, No. 3, 2008, 3–11.
  • Chen, X., Dong, Z., Wang, D., “Bootstrap prediction intervals for SVM forecasting model with GARCH errors”, IEEE Transactions on Power Systems, 2009.
  • Fonseka, J., Dong, Z., Saha, T., “Probabilistic market simulation’, IEEE Transactions on Power Systems, 2009.
  • Luo, Y., Xue, Y., Dong, Z., “Composite optimisation of generation capacity adequacy”, Automation of Electric Power Systems, 2007.
  • Meng, K., Dong, Z., “Electricity market clearing price forecasting”, Computational Intelligence in Power Systems, Springer, 2008.
  • Meng, K., Dong, Z., Wong, K., “Self-adaptive RBF neural network for short-term electricity price forecasting”, IET Generation, Transmission & Distribution, 2009.
  • Nizar, A., Dong, Z., Wang, Y., “Power utility nontechnical loss analysis with extreme learning machine method”, IEEE Transactions on Power Systems, Vol. 23, No. 3, August 2008, 946–55.
  • Nizar, A., Dong, Z., Zhao, J., Zhang, P., “Empirical study on NTL analysis references”, IEEE Power Engineering Society General Meeting, 2007.
  • Wang, H., Xue, Y., Dong, Z., “Adaptive optimal restoration control for interconnected grids”, Automation of Electric Power Systems, 2007.
  • Wong, K., Dong, Z., Meng, K., Yin, X., “A hybrid model for electricity spot prices in the Australian NEM”, Proceedings of Electricity Energy Evolution in China and Australia, 2008.
  • Xu, N., Wen, F., Huang, M., Dong, Z., “Optimal parameter setting of performance based regulation with reward and penalty”, Proceedings IEEE Congress on Evolutionary Computing, 2007.
  • Xue, Y., Lu, H., Li, B., Lu, J., and Dong, Z., “On interruptible load participation in the power system in standby”, Automation of Electric Power Systems, 2007.
  • Zhao, J., Dong, Z., Li, X., “An improved Naive Bayesian classifier with advanced discretisation method.”, International Journal Intelligent Systems Technologies, Vol. 3, No. 3/4, 2007, 241-256.
  • Zhao, J., Dong, Z., Li, X., “Electricity market price spike forecasting and decision making”, IET Generation, Transmission & Distribution, Vol. 1, No. 4, July 2007, 647-654.
  • Zhao, J., Dong, Z., Li, X., Wong, K., “A framework for electricity spike analysis with advanced data mining methods”, IEEE Transactions on Power Systems, Vol. 22, No. 1, February 2007, 376-385.
  • Zhao, J., Dong, Z., Xu, Z., Wong, K., “A statistical approach for interval forecasting of the electricity price”, IEEE Transactions on Power Systems, Vol. 23, No. 2, 2008, 267–76.
  • Zhao, J., Dong, Z., Zhang, P., “Mining complex power networks for blackout prevention”, Proceedings of the 13th ACM International Conference on Knowledge Discovery and Data Mining, August 2007, 986-994.
  • Zhao, J., Li, X., Dong, Z., “Online rare events detection”, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’07), 2007, Vol. 4426, 1114-1121.

Vehicle-2-Grid

Project Leader: Ariel Liebman
Researchers: Geoff Walker, Zhao Yang Dong, Rizah Memisevic, Tapan Saha

This project aims to determine the feasibility of the use of hybrid wheeled vehicles (car) connected to electricity grid. These are also known as plug-in-hybrid technologies to enable introduction of large-scale renewable energy and at the same time produce significant improvements in the performance of power systems and markets in a changing global energy environment with a special focus on enabling the growth of renewable generation. Specifically the concept involves the modelling of the effects of the use of batteries installed in electric powered or hybrid cars to supply the grid or offset the local demand. Proof of concept connections of hybrid car (Toyota Prius) already exist (http://www.acpropulsion.com/white_papers.htm).

The motivating aspect of the use of these technologies is the potential benefit of having a large amount of distributed energy storage on the grid at all times in order to be able to respond to various short-term events on the system. In particular events, such as a sudden increase or drop in supply or demand on a timescale of minutes, the possible benefits are economic, environmental and physical.

In 2006, the foundational aspects of the Vehicle-2-Grid technology benefits have been studied. These include a simulation of the ability of the battery technology and the associated power electronics to contribute to system stability control, and the economic impact on the power system and electricity prices. The stability and control aspect was modelled using Matlab and Simulink and demonstrated that even a relatively modest presence of hybrid vehicles on the grid (the equivalent of 5000-10000 Toyota Prius's) would provide benefits to the control of stability. Additionally, an initial study of market impacts over a 10-year horizon was performed using projection of hybrid vehicle penetration based on available information on manufacturers' plans and current vehicles' sales information. This, in essence, assumed all of Toyota's cars, but none of the other car manufacturers' cars, would be grid-connected by 2015. The early results suggest that, at least for our assumptions, the impacts on prices and transmission congestion costs are marginal. However, the assumptions were very conservative and, globally, there is significant momentum for research and development of the plug-in-hybrid vehicle technology. Hence, to complete the study, scenarios with significantly higher penetration of plug-in-hybrid vehicles need to be performed.

In 2007, this project has taken on three final year engineering students in order to assess various aspects of the technology. The students have performed literature reviews of battery technology development as well as impacts on transmission expansion costs and impacts on renewable energy deployment. This enables the research to move on to a stage where an integrated view of the impact of V2G on the electricity system can be thoroughly explored.

Impacts of climate change on the Snowy Hydro system

Project Leader: Liam Wagner
Researchers: Rohan Alexander, John Quiggin, Ariel Liebman, Lukas Skoufa

This project aims to analyse the impacts of climate change on the generation profile of the Snowy Hydro scheme by evaluating natural inflow of water from snow melts and rain fall into the three dams which provide the fuel source for power generation. Furthermore, analysis will also need to be performed on the internal demand of the SNOWY1 region on the National Electricity Market (NEM), which may indicate pumping of water for reuse. Snowy Hydro’s generation performance has been significantly affected during 2007 due to declining inflows into their dam storage network. This project has been performing analysis of electricity production by Snowy Hydros units, its Bid Stacks and use of pump storage during off peak hours of operation. The results from this investigation currently indicate that Snowy’s future viability as a generator of clean renewable electricity for peak demand is under question.

Electricity network expansion planning in a market environment

Project Leader: Zhao Yang Dong
Researchers: Mark Bowden, David Hill, Ariel Liebman, Rizah Memisevic, Jennie Miao Lu, John Zhe Lu, Ke Meng, Yateendra Mishra, Anisah Nizar, GuangYa Yang, Xia Donna Yin, Joe Xun Zhou

This project investigated the integration of technical and market aspects of power system dynamics and price dynamics to provide key insights into planning the expansion of the power transmission network. This project aimed to apply modern computational modelling techniques to the interface between the physical properties of the electricity system and its economic considerations. A particular focus was placed on the impacts of the transmission network and power station operation on electricity price behaviour and its influence on infrastructure investment decisions. It also looked into the importance of customer load impact on system and market operations.

In 2006, the project brought together several researchers and postgraduate students to develop methodologies for three different aspects of the power system: the investment and expansion of the electricity system; the management of an electricity trading portfolio; and the behaviour of electricity demand. The work on the electricity system expansion has produced several new approaches for determining the value of flexibility in the expansion process. The electricity trading work has produced some novel methodologies for pricing hedging contracts and the demand-related work has produced new methodologies for detecting fraud and other non-financial losses of an electricity utility.

Research work in electric power and networks continued to generate many outcomes in 2007. These included new computational methods for electricity market and power system analysis; load modelling and its impact on system dynamic characteristics and planning; and risk management in electricity markets and its impact on power system planning.

Electricity (transmission) network expansion planning requires holistic consideration of transmission, generation and, because of the incremental penetration of distributed generation, distribution as well. In 2008, in addition to the least-cost based planning methodology, a flexible planning framework was developed to cope with the increasing uncertainties in the planning environment. It included system security assessment methodology, protection system planning (especially out of step relay setting), load modelling impact, generation new entry, (long term) demand forecast, and special generation system modelling (such as hydro system modelling and wind modelling). The research team studied the impact of the emissions trading scheme on the Australian National Electricity Market operations and pricing in detail.

  • Ali, M., Dong, Z., Zhang, P., Li, X., “Probabilistic transient stability analysis using grid computing technology”, IEEE Power Engineering Society General Meeting, 2007.
  • Dong, Z., Wong, K., Zhou, X., Ziser, C., “Issues in operation and planning in the Australian national electricity market”, Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2008.
  • Liu, Q., Xue, Y., Dong, Z., “Nondeterministic analysis for transient stability based on transient stability domain and conditional probability”, Automation of Electric Power Systems, 2007.
  • Liu, Q., Xue, Y., Dong, Z., “The abnormal effects of line fault location on the transient stability and its mechanism”, Automation of Electric Power Systems, 2007.
  • Lu, M., Dong, Z., “A probabilistic load flow method considering transmission network contingency”, IEEE Power Engineering Society General Meeting, 2007.
  • Lu, M., Dong, Z., Saha, T., 'A hybrid probabilistic criterion for market-based transmission expansion planning', Proceedings of IEEE PES General Meeting 2006, 2006.
  • Lu, M., Lu, Z., Saha, T., Dong, Z., 'A novel approach to evaluate congestion for composite power system planning in a competitive electricity market', Proceedings of IEEE PES General Meeting 2006, 2006.
  • Lu, Z., Lu, M., Dong, Z., Ngan, H., 'A real options based method for power system planning', Proceedings of IET International Conference Advances in Power Systems Control, Operation and Management, 2006.
  • Lu, Z., Lu, M., Dong, Z., Ngan, H., 'Energy derivative market and derivative pricing via simulation', Proceedings of IET International Conference Advances in Power Systems Control, Operation and Management, 2006.
  • Lu, Z., Liebman, A., Dong, Z., 'Power generation investment opportunities evaluation: A comparison between net present value and real options approach', Proceedings of IEEE PES General Meeting 2006, 2006.
  • Ma, J., Dong, Z., He, R., Hill, D., “Measurement-based load modelling using genetic algorithms”, Proceedings of IEEE Congress on Evolutionary Computing, 2007.
  • Ma, J., Han, D., He, R., Dong, Z., Hill, D., “Research on identifieability of equivalent motor in composite load model”, Proceedings of Power Tech, 2007.
  • Ma, J., Han, D., He, R., Dong, Z., Zhang, P., “Reducing identified parameters of measurement-based composite load model”, IEEE Transactions on Power Systems, Vol. 23, No. 1, February 2008, 76-83, 2008.
  • Ma, J., Hill, D., Dong, Z., He, R., “System energy analysis incorporating comprehensive load characteristics”, IET Generation, Transmission & Distribution, Vol. 1, No. 6, 2007, 885-863.
  • Makarov, Y., Dong, Z., Hill, D., “On convexity of power flow feasibility boundary”, IEEE Transmission on Power Systems (letter), 2007.
  • Makarov, Y., Ma, J., Dong, Z., “Determining static stability boundaries using a non-iterative method”, IEEE Power Engineering Society General Meeting, 2007.
  • Makarov, Y., Ma, J., Dong, Z., “Non-iterative method to determine static stability boundaries”, IEEE Power Engineering Society General Meeting, 2007.
  • Mishra, Y., Dong, Z., Bansal, R.C., Mishra, S., “Rough fuzzy control of SVC for power system stability enhancement”, Computational Intelligence in Power Systems, Springer, 2009.
  • Mishra, Y., Dong, Z., Ma, J., Hill, D., “Induction motor load impact on power system eigen value sensitivity analysis”, IET Generation, Transmission & Distribution, 2009.
  • Mishra, Y., Mishra, S., Dong, Z., “Rough fuzzy control of SVC for power system stability enhancement”, International Journal of Electrical Engineering and Technology, 2009.
  • Nizar, A., Dong, Z., Liebman, A., 'Customer information systems for deregulated ASEAN countries', Institution of Engineers Singapore (IES) Journal, 2006.
  • Nizar, A., Zhao, J., Dong, Z., 'Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market', Proceedings of 2006 International Conference on Power System Technology, 2006.
  • Nizar, A., Dong, Z., Jalaluddin, M., Raffles, M., 'Load profiling method in detecting non-technical loss activities in a power utility', Proceedings of the First International Power and Energy Conference, 2006.
  • Sampepajung, H., Lu, Z., Dong, Z., Zhang, P., “Investigation into the Indonesia power industry for implementation of a competitive generation market”, IEEE Power Engineering Society General Meeting, 2007.
  • Shi, L., Hao, J., Dong, Z., “Theoretical and practical framework for a novel ant colony optimisation variant”, WSEAS Transactions on Information Science & Applications, 2009.
  • Shi, L., Xu, Z., Dong, Z., “Wind turbine model and initialisation analysis with high penetration of grid-connected wind farms of DFIG type”, Hydropower Automation and Dam Monitoring, 2009.
  • Wong, K., Dong, Z., “Differential evolution, an alternative approach to evolutionary algorithm”, Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, Edited by Lee, K., El-Sharkawi, M., Wiley, 2007.
  • Xue, Y., Pan, X., Zhang, G., Dong, Z., Ledwich, G., “Oscillation mode analysis including full nonlinearity of time-varying systems”, Automation of Electric Power Systems, Vol. 18, 2008, 1–7.
  • Xue, Y., Xu, W., Dong, Z., “A review of wide area measurement system and wide area control system”, Automation of Electric Power Systems, 2007.
  • Yang, G., Dong, Z., Wong, K., “A modified differential evolution algorithm with fitness sharing for power system planning”, IEEE Transactions on Power Systems, Vol. 23, No. 2, May 2008, 514–22.
  • Yang, G., Mishra, Y., Dong, Z., Wong, K., “Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution”, Proceedings of the 17th IFAC World Congress, 2008.
  • Yang, G., Hovland, G., Dong, Z., “TCSCs allocation based on line fl ow equations via mixed-integer programming”, IEEE Transactions on Power Systems, Vol. 22, No. 4, December 2007, 2262-2269.
  • Yang, G., Mishra, Y., Dong, Z., Wong, K., “Optimal power system stabiliser tuning in multi-machine system via an improved differential evolution”, IEEE Transactions on Power Systems, November 2007.
  • Zhao, J., Dong, Z., Lindsay, P., Wong, K., “Flexible transmission expansion planning with uncertainties in an electricity market”, IEEE Transactions on Power Systems, Vol. 21, No. 1, 2009, 479–88.

Agent based modelling for electricity price simulation

Project Leader: Ariel Liebman
Researchers: Mark Bowden, Zhao Yang Dong, John Foster

Electricity markets have developed around the world over the past 15 years. However, these markets are mostly still in a maturing phase and data is not reliable or at best not representative of the full range of outcomes possible. This project aimed to develop an agent-based simulation model which can reproduce the sort of price statistics that are seen in a variety of markets. The innovative approach of this project was to incorporate both physical electricity trading and trading in secondary markets (futures and forward markets). The results of the simulation will be electricity prices across both markets and could be used to test the sensitivity of electricity price behaviour to changes in market structure and number of participants.

In 2008 this project was extended into a collaboration between the University of Queensland’s and Victoria University in Melbourne’s schools of economics. Additionally, the University of Queensland’s Professors John Foster and John Quiggin, received ARC Industry Linkage grant funding with Babcock & Brown Power to extend the methods used in this project to investigate the impacts of various forms of carbon permit emission trading schemes. This linkage project has now also extended to include AGL Energy and is expected to make a major contribution to the current ongoing debate about the government’s Carbon Pollution Reduction Scheme.

In 2007, a new software tool was developed to implement the model developed in the previous year. Currently the software is able to simulate an electricity system consisting of a number of electricity generator agents bidding into a gross pool market. The prices emerging from the simulation bear some resemblance to prices seen in Australia’s electricity markets. Future work includes agents responding to profit outcomes and forward market trading through a small-world network of trading partners.

Impacts and risk of emissions trading on electricity generation companies and projects

Project Leader: John Foster
Researchers: Fabio Barelli, Matteo Beltrami, Ariel Liebman, Lukas Skoufa

This project aims to develop and apply a stochastic model to the assessment of the impacts of a CO2 emissions trading scheme on Australian and international power generation entities. These entities could be new investments in power generation plant, or existing power generation companies, or utilities. Particular focus will be placed on the statistical nature of key economic factors affecting the profitability of a power generation entity. These include input fuel costs and electricity outputs. Careful attention will be paid to the correlations between the input costs (particularly coal or gas market prices), the emission permit prices, which need to be postulated since no such scheme exists in Australia, and electricity market prices. The quantities analysed to assess risks and opportunities to the generation entities are the Profit and Loss probability distributions.

In 2007, a comprehensive investment model for a variety of generation technologies was developed with an annual resolution. The technologies the model can assess include, coal fired (from old to next generation ultracritical power stations), gas fired, wind, solar and others. The simulation is on Microsoft Excel spreadsheets but uses monte-carlo simulation engines built by commercial third parties. This model can easily be deployed in a commercial setting. The model currently enables the comparison of the risks of various new ventures and the trade-off between investment costs and running costs can be explored. Preliminary results have been presented at industry forums and have received favourable feedback.

In 2008, we began constructing the input-output econometric model that will be used to assess the impact of carbon trading on different industries and product groups. This is still continuing and involves the collection of relevant data, the econometric estimation of structural equations and linkage calibration of the I/O data with estimated econometric equations. New data was collected concerning electricity generation and distribution systems and high frequency data for the wholesale electricity market was also collected in preparation for simulations of carbon trading impacts, both on existing fossil fuel generators and emerging renewable energy generators. Some preliminary work was also completed in implementing both the PLEXOS and agent-based modelling platforms that will be used to simulate networked behavioural change, as carbon pricing raises average electricity prices.

General NEM publications

  • Dong, Z., Zhang, P., Emerging methods for power system stability and planning, 2009.
  • Memisevic, R., Sanderson, P., Wong, W., Choudhury, S., Li, X., “Investigating human-system interaction with an integrated hydropower”, IEEE Transactions on Power Systems, 2007
  • Tang, H., Weng, L., Dong, Z., Yan, R., “Adaptive and learning control for SI engine model with uncertainties”, IEEE/ASME Transactions on Mechatronics, 2009.
  • Zhou, X., Dong, Z., James, G., Liebman, A., “Australian electricity market power analysis under potential emission trading scheme”, IEEE Transactions on Power Systems, 2008.
  • Zhou, X., Dong, Z., Liebman, A., James, G., “Potential impact of emission trading schemes on the Australian national electricity market”, IEEE Power Engineering Society General Meeting, 2008.
  • Zhou, X., Dong, Z.Y., James, G., Liebman, A., Ziser, C., “Partial carbon permits allocation of potential emission trading scheme in Australian electricity market”, IEEE Transactions on Power Systems, 2008.

 
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