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Stochastic energy balancing in substation energy management

School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia

Topical Section: Smart Grids and Networks

In the current research, a smart grid is considered as a network of distributed interacting nodes represented by renewable energy sources, storage and loads. The source nodes become active or inactive in a stochastic manner due to the intermittent nature of natural resources such as wind and solar irradiance. Prediction and stochastic modelling of electrical energy flow is a critical task in such a network in order to achieve load levelling and/or peak shaving in order to minimise the fluctuation between off-peak and peak energy demand. An effective approach is proposed to model and administer the behaviour of source nodes in this grid through a scheduling strategy control algorithm using the historical data collected from the system. The stochastic model predicts future power consumption/injection to determine the power required for storage components. The stochastic models developed based on the Box-Jenkins method predict the most efficient state of the electrical energy flow between a distribution network and nodes and minimises the peak demand and off-peak consumption of acquiring electrical energy from the main grid. The performance of the models is validated against the autoregressive moving average (ARIMA) and the Markov chain models used in previous work. The results demonstrate that the proposed method outperforms both the ARIMA and the Markov chain model in terms of forecast accuracy. Results are presented, the strengths and limitations of the approach are discussed, and possible future work is described.
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Keywords Smart grid; renewable energy sources; stochastic modelling; forecasting power flow; energy balancing

Citation: Hassan Shirzeh, Fazel Naghdy, Philip Ciufo, Montserrat Ros. Stochastic energy balancing in substation energy management. AIMS Energy, 2015, 3(4): 810-837. doi: 10.3934/energy.2015.4.810


  • 1. Clean Energy Regulator, 2014. Available from: http://www.cleanenergycouncil. org.au/policy-advocacy/reports/clean-energy-australia-report.html.
  • 2. Liu X (2010) Economic load dispatch constrained by wind power availability: A wait-and- see approach. Smart Grid, IEEE Transactions on 1: 347-355.    
  • 3. Parvania M, Fotuhi-Firuzabad M (2010) Demand response scheduling by stochastic SCUC. Smart Grid, IEEE Transactions on 1: 89-98.    
  • 4. Gong C,Wang X, Xu W, et al. (2013) Distributed real-time energy scheduling in smart grid: Stochastic model and fast optimization. Smart Grid, IEEE Transactions on 4: 1476-1489.    
  • 5. Su S, Lu C, Chang R, et al. (2011) Distributed generation interconnection planning: A wind power case study. Smart Grid, IEEE Transactions on 2: 181-189.    
  • 6. He M, Murugesan S, Zhang J (2013) A multi-timescale scheduling approach for stochastic reliability in smart grids with wind generation and opportunistic demand. Smart Grid, IEEE Transactions on 4: 521-529.    
  • 7. Mohd A, Ortjohann E, Schmelter A, et al. (2008) Challenges in integrating distributed energy storage systems into future smart grid. Industrial Electronics. ISIE 2008. IEEE International Symposium on: 1627-1632.
  • 8. Dantzig G (1998) Linear Programming and Extensions, Princeton university Press.
  • 9. Nfaoui H, Essiarab H, Sayigh A (2004) A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco. Renewable Energy 29: 1407-1418.    
  • 10. Hocaoglu F (2011) Stochastic approach for daily solar radiation modeling. Solar Energy 85: 278-287.    
  • 11. Chen P, Pedersen T, Bak-Jensen B, et al. (2010) ARIMA-based time series model of stochastic wind power generation. Power Systems, IEEE Transactions on 25: 667-676.
  • 12. Box G, Jenkins M, Reinsel G (2008) Time Series Analysis, Forecasting and Control, John Wiley & Sons Inc.
  • 13. Bivona S, Bonanno G, Burlon r, et al. (2011) Stochastic models for wind speed forecasting. Energy Conversion and Management 52: 1157-1165.    
  • 14. A Case Study of Increasing Levels of PV Penetration in an Isolated Electricity Supply Sys- tem, 2014. Available from: http://apvi.org.au/wp-content/uploads/2014/05/Carnarvon- High-PV-Penetration-Case-Study.pdf.
  • 15. Nguyen C, Flueck A (2012) Agent based restoration with distributed energy storage support in smart grids. Smart Grid, IEEE Transactions on 3: 1029-1038.    
  • 16. Kodama J, Hamagami T, Shinji H, et al. (2009) Multi-agent-based autonomous power distribution network restoration using contract net protocol. Electrical Engineering in Japan 166: 56-63.    
  • 17. Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. Power Systems, IEEE Transactions on 20: 1053-1061.    
  • 18. Sortomme E, El-Sharkawi M, (2009) Optimal Power Flow for a System of Microgrids with Controllable Loads and Battery Storage. Power Systems Conference and Exposition, 2009 : 1-5.
  • 19. Zong Y, Kullmann D, Thavlov A, et al. (2012) Application of model predictive control for active load management in a distributed power system with high wind penetration. Smart Grid, IEEE Transactions on 3: 1055-1062.    
  • 20. Zhao Z, Wu L (2013) Impacts of high penetration wind generation and demand response on LMPs in day-ahead market. Smart Grid, IEEE Transactions on 5: 220-229.
  • 21. Li Y, Huang G, Xu Y, et al. (2010) Regional-scale electric power system planning under uncertainty a multistage interval-stochastic integer linear programming approach. Energy Policy 38: 475-490.    
  • 22. Molderink A, Bakker V, Bosman M, et al. (2010) Management and control of domestic smart grid technology. Smart Grid, IEEE Transactions on 1: 109-119.    
  • 23. Yu W, Liu D, Huang Y (2013) Operation Optimization Based on the Power Supply and Storage Capacity of an Active Distribution Network. Energies 6: 6423-6438.    
  • 24. McArthur S, Davidson E, Catterson V, et al. Multi-agent systems for power engineering applications, part ii: Technologies, standards, and tools for building multi-agent systems. Power Systems, IEEE Transactions on 22: 1753-1759.
  • 25. Moslehi K, Kumar R (2010) A reliability perspective of the smart grid. Smart Grid, IEEE Transactions on 1: 57-64.    
  • 26. Garciaa-Ascanio C, Mate C (2010) Electric power demand forecasting using interval time series: A comparison between VAR and iMLP. Energy Policy 38: 715-725.    
  • 27. Shirzeh H, Naghdy F, Ciufo P, et al. (2015) Balancing energy in the smart grid using distributed value function (DVF). Smart Grid, IEEE Transactions on 6: 808-818.    
  • 28. Pawlowski A, Guzman J, Rodriguez F, et al. (2010) Application of time-series methods to disturbance estimation in predictive control problems. in Industrial Electronics (ISIE), 2010 IEEE International Symposium on: 409-414.
  • 29. Taylor J, Menezes L, McSharry P (2006) A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 22: 1-16.    
  • 30. Munoz A, Sanchez-Ubeda E, Cruz A, et al. (2010) Short-term forecasting in power systems: A guided tour, Handbook of Power Systems II, Energy Systems, Springer Berlin Heidelberg, 129-160.
  • 31. Pring M (2002) Technical Analysis Explained, McGraw-Hill, 2002.
  • 32. Murphy J (1999) Technical Analysis of the Financial Markets, New York Institute of Finance.
  • 33. Mooney C (1997) Monte Carlo Simulation, SAGE PUBLICATION.
  • 34. Nguyen M, Nguyen D, Yoon Y (2012) A New Battery Energy Storage Charging/Discharging Scheme for Wind Power Producers in Real-Time Markets. Energies, 5: 5439-5452.    
  • 35. Endeavor Energy, 2013. Available from: www.endeavourenergy.com.au/.
  • 36. Bellifemine F, Caire G, Greenwood D (2007) Developing Multi Agent Systems with JADE, England: Wiley.
  • 37. Department of Agriculture and Food, WA, Australia, 2014. Available from: https://www.agric.wa.gov.au/.
  • 38. University of York, 2009. Available from: http://www.agentcontrol.co.uk/.
  • 39. Robinson C, MendhamP, Clarke T (2010) MACSIMJX: A Tool for Enabling Agent Mod- elling with Simulink Using JADE. JoPha Journal of Pysical Agents 4: 1-7.
  • 40. Shull F, Singer J, Sjoberg D (2008) Guide to advanced empirical software engineering, Springer.


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Copyright Info: 2015, Hassan Shirzeh, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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