New Scenario-based Stochastic Programming Problem for Long-term Allocation of Renewable Distributed Generations

Ikki Tanaka, Hiromitsu Ohmori

Abstract

Large installation of distributed generations (DGs) of renewable energy sources (RESs) on distribution network has been one of the challenging tasks in the last decade. According to the installation strategy of Japan, long-term visions for high penetration of RESs have been announced. However, specific installation plans have not been discussed and determined. In this paper, for supporting the decision-making of the investors, a new scenario-based two-stage stochastic programming problem for long-term allocation of DGs is proposed. This problem minimizes the total system cost under the power system constraints in consideration of incentives to promote DG installation. At the first stage, before realizations (scenarios) of the random variables are known, DGs’ investment variables are determined. At the second stage, after scenarios become known, operation and maintenance variables that depend on scenarios are solved. Furthermore, a new scenario generation procedure with clustering algorithm is developed. This method generates many scenarios by using historical data. The uncertainties of demand, wind power, and photovoltaic (PV) are represented as scenarios, which are used in the stochastic problem. The proposed model is tested on a 34 bus radial distribution network. The results provide the optimal long-term investment of DGs and substantiate the effectiveness of DGs.

References

  1. Abdelaziz, A., Hegazy, Y., El-Khattam, W., and Othman, M. (2015). Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks. Electric Power Systems Research, 119:34-44.
  2. Asensio, M., de Quevedo, P. M., Munoz-Delgado, G., and Contreras, J. (2016a). Joint distribution network and renewable energy expansion planning considering demand response and energy storage part i: Stochastic programming model. IEEE Transactions on Smart Grid, PP(99):1-1.
  3. Asensio, M., de Quevedo, P. M., Munoz-Delgado, G., and Contreras, J. (2016b). Joint distribution network and renewable energy expansion planning considering demand response and energy storage part ii: Numerical results and considered metrics. IEEE Transactions on Smart Grid, PP(99):1-1.
  4. Atwa, Y., El-Saadany, E., Salama, M., and Seethapathy, R. (2010). Optimal renewable resources mix for distribution system energy loss minimization. IEEE Transactions on Power Systems, 25(1):360-370.
  5. Baringo, L. and Conejo, A. (2011). Wind power investment within a market environment. Applied Energy, 88(9):3239-3247.
  6. Baringo, L. and Conejo, A. (2013a). Correlated wind-power production and electric load scenarios for investment decisions. Applied energy, 101:475-482.
  7. Baringo, L. and Conejo, A. J. (2013b). Risk-constrained multi-stage wind power investment. IEEE Transactions on Power Systems, 28(1):401-411.
  8. Carvalho, P. M., Ferreira, L. A., Lobo, F. G., and Barruncho, L. M. (1997). Distribution network expansion planning under uncertainty: a hedging algorithm in an evolutionary approach. In Power Industry Computer Applications., 1997. 20th International Conference on, pages 10-15. IEEE.
  9. Chis, M., Salama, M., and Jayaram, S. (1997). Capacitor placement in distribution systems using heuristic search strategies. IEE Proceedings-Generation, Transmission and Distribution, 144(3):225-230.
  10. Dupac?ová, J., Consigli, G., and Wallace, S. W. (2000). Scenarios for multistage stochastic programs. Annals of operations research, 100(1-4):25-53.
  11. Eduardo, L. (1994). Solar electricity: Engineering of photovoltaic systems. Progensa, Sevilla. ISBN, pages 84- 86505.
  12. Eftekharnejad, S., Vittal, V., Heydt, G. T., Keel, B., and Loehr, J. (2013). Impact of increased penetration of photovoltaic generation on power systems. IEEE transactions on power systems, 28(2):893-901.
  13. Fu, X., Chen, H., Cai, R., and Yang, P. (2015). Optimal allocation and adaptive var control of pv-dg in distribution networks. Applied Energy, 137:173-182.
  14. Gurobi 6.5.0, Gurobi Optimization, I. (2016). User's manual. http://gams.com/help/topic/gams.doc/solvers/ gurobi/index.html.
  15. Huang, K. and Ahmed, S. (2009). The value of multistage stochastic programming in capacity planning under uncertainty. Operations Research, 57(4):893-904.
  16. Jordehi, A. R. (2016). Allocation of distributed generation units in electric power systems: A review. Renewable and Sustainable Energy Reviews, 56:893-905.
  17. Krukanont, P. and Tezuka, T. (2007). Implications of capacity expansion under uncertainty and value of information: the near-term energy planning of japan. Energy, 32(10):1809-1824.
  18. Mavrotas, G., Demertzis, H., Meintani, A., and Diakoulaki, D. (2003). Energy planning in buildings under uncertainty in fuel costs: The case of a hotel unit in greece. Energy Conversion and management, 44(8):1303-1321.
  19. Mazidi, M., Zakariazadeh, A., Jadid, S., and Siano, P. (2014). Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 86:1118-1127.
  20. Montoya-Bueno, S., Mun˜oz-Hernández, J., and Contreras, J. (2016). Uncertainty management of renewable distributed generation. Journal of Cleaner Production.
  21. Montoya-Bueno, S., Muoz, J. I., and Contreras, J. (2015). A stochastic investment model for renewable generation in distribution systems. IEEE Transactions on Sustainable Energy, 6(4):1466-1474.
  22. Munoz, F. D., Hobbs, B. F., and Watson, J.-P. (2016). New bounding and decomposition approaches for milp investment problems: Multi-area transmission and generation planning under policy constraints. European Journal of Operational Research, 248(3):888-898.
  23. Nick, M., Cherkaoui, R., and Paolone, M. (2014). Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support. IEEE Transactions on Power Systems, 29(5):2300-2310.
  24. Nick, M., Cherkaoui, R., and Paolone, M. (2015). Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers. International Journal of Electrical Power & Energy Systems, 72:33-39.
  25. Nojavan, S. and allah Aalami, H. (2015). Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program. Energy Conversion and Management, 103:1008-1018.
  26. Payasi, R. P., Singh, A. K., and Singh, D. (2011). Review of distributed generation planning: objectives, constraints, and algorithms. International journal of engineering, science and technology, 3(3).
  27. Pereira, B. R., da Costa, G. R. M., Contreras, J., and Mantovani, J. R. S. (2016). Optimal distributed generation and reactive power allocation in electrical distribution systems. IEEE Transactions on Sustainable Energy, 7(3):975-984.
  28. Sadeghi, M. and Kalantar, M. (2014). Multi types dg expansion dynamic planning in distribution system under stochastic conditions using covariance matrix adaptation evolutionary strategy and monte-carlo simulation. Energy Conversion and Management, 87:455-471.
  29. Saif, A., Pandi, V. R., Zeineldin, H., and Kennedy, S. (2013). Optimal allocation of distributed energy resources through simulation-based optimization. Electric Power Systems Research, 104:1-8.
  30. Seljom, P. and Tomasgard, A. (2015). Short-term uncertainty in long-term energy system modelsa case study of wind power in denmark. Energy Economics, 49:157-167.
  31. Verderame, P. M., Elia, J. A., Li, J., and Floudas, C. A. (2010). Planning and scheduling under uncertainty: a review across multiple sectors. Industrial & engineering chemistry research, 49(9):3993-4017.
  32. Wang, Z., Chen, B., Wang, J., Kim, J., and Begovic, M. M. (2014). Robust optimization based optimal dg placement in microgrids. IEEE Transactions on Smart Grid, 5(5):2173-2182.
  33. Zou, K., Agalgaonkar, A., Muttaqi, K., and Perera, S. (2010). Multi-objective optimisation for distribution system planning with renewable energy resources. In Energy Conference and Exhibition (EnergyCon), 2010 IEEE International, pages 670-675. IEEE.
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Paper Citation


in Harvard Style

Tanaka I. and Ohmori H. (2017). New Scenario-based Stochastic Programming Problem for Long-term Allocation of Renewable Distributed Generations . In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 96-107. DOI: 10.5220/0006189900960107


in Bibtex Style

@conference{icores17,
author={Ikki Tanaka and Hiromitsu Ohmori},
title={New Scenario-based Stochastic Programming Problem for Long-term Allocation of Renewable Distributed Generations},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2017},
pages={96-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006189900960107},
isbn={978-989-758-218-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - New Scenario-based Stochastic Programming Problem for Long-term Allocation of Renewable Distributed Generations
SN - 978-989-758-218-9
AU - Tanaka I.
AU - Ohmori H.
PY - 2017
SP - 96
EP - 107
DO - 10.5220/0006189900960107