NEMSIM a New, Long-term Simulation Tool for Australia s National Electricity Market

Transcription

1 NEMSIM a New, Long-term Simulation Tool for Australia s National Electricity Market George Grozev, Marcus Thatcher, Per da Silva, Chi-hsiang Wang, Pramesh Chand, Geoff Lewis George.Grozev@csiro.au CSIRO Sustainable Ecosystems, Graham Road, PO Box 56, Highett, VIC 3190, Australia CSIRO Marine and Atmospheric Research, Station Street, Private Bag1, Aspendale VIC 3195, Australia SETE 2007 Sydney 1

2 Topics Overview and background The story about NEMSIM Analysis Lessons learned Summary Conclusion References List of acronyms Questions and comments SETE 2007 Sydney 2

3 Simulation of Electricity and Gas Markets Why How Major contributor to greenhouse gas (GHG) emissions, Significant cost of investment, Innovative sustainable solutions for pressing energy needs, Complexity of electricity and gas markets, Decision making under uncertainty, Models and simulates the electricity market up to 30 years in the future, Applies agent-based simulation to companies decision making, Constructs electricity demand based on climate data and usage patterns, Calculates electricity generation, prices, GHG emissions and fuel usage, Evaluates new investment in generation, Commercial version known as GENERSYS is under development in partnership with two companies, GENERSYS aims to integrate all major elements of the energy system, including gas, coal, hydro, wind and other renewables. SETE 2007 Sydney 3

4 The NEM Model 500 Capacity in MW QLD 500 QNI 180 Directlink SA 200 Murraylink 1000 NSW VIC-SA SNO VIC SNO-VIC Basslink SNO-NSW NSW 1150 TAS SETE 2007 Sydney 4

5 Brief history of the project January 2003: Initial funding from CSIRO s Centre for Complex Systems Science; October 2003: Commenced as a project in CSIRO s Energy Transformed Flagship Program; April 2004: Contract with Swinburne University of Technology to codevelop the NEMSIM prototype; April 2005: NEMSIM Industry Focus Group Meeting (Mercure Hotel, Melbourne); July 2006: Development and License agreements signed with Core Collaborative Pty Ltd. to commercialise NEMSIM under the name GENERSYS; February 2007: Development agreement signed between Core Collaborative and AGL for providing AGL with a license to use GENERSYS and AGL to participate in the development. SETE 2007 Sydney 5

6 Overview of NEMSIM Time Horizons Historical Data Demand Bidding Prices Dispatch 30 min Dispatch Technical Infrastructure Agents & Markets Environment Generating Plants Investment Agents Electricity Supplied Generating Clusters of Transmission Units DG Lines Companies Spot Market Contract Market Fuel GHG Power Losses Resources Emissions Simulated Scenario Daily Decisions Weekly Decisions Monthly Decisions Yearly Decisions Longer Term Decisions Input User Data Input Simulation Engine Demand Evolution Daily Bidding Supply Evolution Spot Prices Contract Prices Investment Decisions GHG Emissions Reports Graphs Tables Simulation Log Scenario Evaluation SETE 2007 Sydney 6

7 Specific Modelling Features Electricity Demand Generating unit failure and maintenance Historical Data Demand Bidding Prices Dispatch Technical Infrastructure Generating Plants Generating Units Cluster of DG Transmission Lines Agents & Markets Market Operator Companies Contract Market Spot Market GHG Calculator Generation investment Environment Electricity Supplied Fuel Resources Power Losses GHG Emissions SETE 2007 Sydney 7

8 Scalable (SVG) User Interface SETE 2007 Sydney 8

9 Inter-regional Coupling between Climate and Electricity Demand Daily climate NSW ELECTRICITY DEMAND MODEL 24 hr demand (30 min steps) NSW GLOBAL CLIMATE MODEL (DOWN- SCALLED) VIC QLD SA ELECTRICITY DEMAND MODEL ELECTRICITY DEMAND MODEL ELECTRICITY DEMAND MODEL VIC QLD SA NEMSIM TAS ELECTRICITY DEMAND MODEL TAS SETE 2007 Sydney 9

10 Examples of Constructed Electricity Demand Datasets The model robustly describes the qualitative behavioural changes in electricity consumption between seasons. This give us confidence in anticipating future changes to demand behaviour as a consequence of global warming Summer cooling Winter heating :00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00 Demand (MW) Observed Model Demand (MW) Observed Model :00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00 Time Time Examples of demand reconstruction for a typical summer day (left) and winter day (right) in NSW. Note how the model s qualitative behaviour changes due to different input temperatures. SETE 2007 Sydney 10

11 Modelling of Decision Making and Agent-based Simulation Bidding (daily) Contracting (monthly) Time Investment (yearly) SETE 2007 Sydney 11

12 Bidding Models Static: Simple, Lookup, Select, File, Historic Dynamic: based on rules price, demand, availability. 10 high prices 8 P 10 P 9 P 8 Q 10,1 Q 9,1 Q 8,1 Q 10,2 Q 9,2 Q 8,2 Q 10,48 Q 9,48 Q 8,48 Y Axis Title 6 4 P 7 Q 7,1 Q 7,2 Q 7,48 2 P 6 Q 6,1 Q 6,2 Q 6, X Axis Title P 5 Q 5,1 Q 5,2 Q 5,48 P 4 Q 4,1 Q 4,2 Q 4,48 P 3 Q 3,1 Q 3,2 Q 3,48 P 2 Q 2,1 Q 2,2 Q 2,48 P 1 Q 1,1 Q 1,2 Q 1,48 low prices Offer price vector 4:30 am 5:00 4:00 am Offer quantities matrix SETE 2007 Sydney 12

13 NEMSIM total and specific regional GHG emissions per year SETE 2007 Sydney 13

14 Generator Emissions SETE 2007 Sydney 14

15 Definitions by NERC: Types of outages Unplanned forced outage: An outage due to unexpected events requiring immediate removal from service Planned outage: An outage scheduled in advance and of a predetermined duration Maintenance outage: An outage for which repair can be deferred beyond the end of the next weekend, but requires that the unit be removed from service SETE 2007 Sydney 15

16 States of a generating unit: Up state Down state Basic assumptions: Modelling approach Constant failure rate over service lifetime. A forced outage can occur randomly at any time. Occurrences of failure are independent of past events. Outage occurrence model: Two model parameters: (1) Failure rate, (2) Time to repair. Events of unplanned & maintenance outage can be modelled as a Poisson process. Planned outages are scheduled during periods of high reserve capacity/low regional demand. SETE 2007 Sydney 16

17 Example of generating unit outages SETE 2007 Sydney 17

18 Generation Investment Based on a generic cost model, Long term decision (evaluated for 30 years), A hybrid NPV & Real options approach; net present value approach may not be good enough to capture the price volatility and not good for peaking plants for example, Differentiate between companies based on performance, ability to anticipate the demand growth, risk appetite and learning capabilities, Takes into accounts normal prices that reflect the SRMC and jump prices that are associated with supply scarcity. SETE 2007 Sydney 18

19 Analysis NEMSIM allows to build comprehensive energy scenarios for electricity and gas in Australia. The tool allows to evaluate possible future scenarios from sustainability point of view, estimating GHG emissions and required fuel resources. Efficient algorithms and modelling essential components allow good performance for simulation periods up to 30 years in the future. SETE 2007 Sydney 19

20 Analysis - continued Dependencies between events and decision making at different time scales are essential. Modelling typical behaviour is not enough. A simulation method developed for constructing regional electricity data sets at 30 minutes intervals, which are consistent with climate change scenarios. Argonne National Laboratory developed EMCAS also based on agent-based simulation for electricity markets. Agent-based simulation presents its challenges in abstracting behaviour and adapting learning algorithms. SETE 2007 Sydney 20

21 Lessons Learned Continuous support from CSIRO s Energy Transformed Flagship Program is central for the achievements over the lifetime of the NEMSIM project. Efficient joint (CSIRO & Core Collaborative) development of the simulation tool, aiming commercial applications, is a critical growth and support factor. Building domain knowledge about operations of electricity and gas markets is essential. Integrating open source and free Java libraries is working well to achieve superior functionality for reasonable time and effort. Validation, testing, documenting and developing realistic simulation scenarios are all important for establishing this simulation tool. SETE 2007 Sydney 21

22 Summary NEMSIM, a simulation system developed by CSIRO s Energy Transformed Flagship project to analyse the operation of Australia s National electricity and gas markets. NEMSIM represents the electricity and gas markets as an evolving system of complex interactions between company behaviour in markets, technical infrastructures and the natural environment. Users of NEMSIM/GENERSYS are able to explore various evolutionary pathways and future development scenarios of energy markets (gas and electricity). SETE 2007 Sydney 22

23 Conclusions Appling agent-based simulation methods to study organisational behaviour in gas and electricity markets is a constructive and promising approach. The ABM methodology allows to explore the interplay at macro (emerging aggregated behaviour) and micro (interactions between components) levels of dynamic systems. Complex adaptive systems framework is establishing itself as a mature methodology to study sustainability. Software models and programs are medium for the storage of knowledge ( Communications of ACM, Vol. 43, 10). Other storage media: DNA, brains, hardware, books. SETE 2007 Sydney 23

24 Acknowledgements NEMSIM & GENERSYS Research and Development Group CSE/CMIT CMAR Core Collaborative Dr. George Grozev Mr. Per da Silva Dr. Pramesh Chand Dr. Chi-hsiang Wang Dr. Xiaoming Wang Dr. John Mo Mr. Shanon McQuay Mr. Miles Anderson (CMIS) Mr. Mario Sammut (CMIS) Mr. Geoff Lewis - contractor Mr. Scott Maves - contractor UNSW Dr. Xinmin Hu Dr. Marcus Thatcher Dr. David Batten Dr. Jack Katzfey ET Flagship Mr. Paul Graham Mr. Terry Jones Swinburne University of Technology Prof. Myles Harding Mr. Neale Taylor Mr. Paul Taliangis Mr. Tennyson Wickham AGL Energy SETE 2007 Sydney 24

25 References Batten, D.F., Grozev, G.V. (2007). Managing energy futures and greenhouse gas emissions with the help of agent-based simulation, chapter 8 in Changing Stocks, Flows and Behaviors in Industrial Ecosystems, Editors Ruth, M. and Davidsdottir, B., Edward Elgar Publishing Ltd (in press). Thatcher, M.J., (2007) Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia, Energy, Vol. 32, 9, Batten, D., Grozev, G. (2006). NEMSIM: Finding Ways to Reduce Greenhouse Gas Emissions using Multi-Agent Electricity Modelling, Chapter 11 in Complex Science for a Complex World: Exploring Human Ecosystems with Agents, Editors Pascal, P. and Batten, B., ANU Press. Hu, X., Grozev, G., Batten, D. (2005). Empirical observations of bidding patterns in Australia s National Electricity Market, Energy Policy, Vol. 33, 16, Grozev, G., Batten, D., Anderson, M., Lewis, G., Mo, J., and Katzfey, J. (2005). NEMSIM: Agent-based Simulator for Australia's National Electricity Market, SimTecT 2005 Conference Proceedings, Sydney, Australia. SETE 2007 Sydney 25

26 Acronyms ABM Agent-Based Modelling CAS Complex Adaptive Systems CSIRO Commonwealth Scientific and Industrial Research Organisation CSS Complex Systems Science DG Distributed Generation EMCAS Electricity Market Complex Adaptive System, developed by Argonne National Laboratory, USA GHG Greenhouse gases NEM Australia s National Electricity Market NEMMCO National Electricity Market Management Company NEMSIM National Electricity Market Simulator NERC North American Electric Reliability Council NPV Net Present Value SRMC Short Run Marginal Cost SVG Scalable Vector Graphics SETE 2007 Sydney 26

27 Questions and discussion In the Shadow of Saturn SETE 2007 Sydney 27