Stochastic Assessment of the Renewable–Based Multiple Energy System in the Presence of Thermal Energy Market and Demand Response Program

Document Type : Research paper

Authors

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

Abstract

The impact of different energy storages on power systems has become more important due to the development of energy storage technologies. This paper optimizes the stochastic scheduling of a wind-based multiple energy system (MES) and evaluates the operation of the proposed system in combination with electrical and thermal demand-response programs and the three-mode CAES (TM-CAES) unit. The proposed wind-integrated MES consists of a TM-CAES unit, electrical boiler unit, and thermal storage system which can exchange thermal energy with the local thermal network and exchange electricity with the local grid. The electrical and thermal demands as well as wind farm generation are modeled as a scenario-based stochastic problem using the Monte Carlo simulation method. Afterwards, the computational burden is reduced by applying a proper scenario-reduction algorithm to initial scenarios. Finally, the proposed methodology is implemented to a case study to evaluate the effectiveness and appropriateness of the proposed method.

Keywords

Main Subjects

References

[1]    M. Budt, D. Wolf, R. Span, and J. Yan, “A review on compressed air energy storage: Basic principles, past milestones and recent developments,” Appl. Energy, vol. 170, pp. 250-268, 2016.
[2]    E. Heydarian-Forushani and H. Aalami, “Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power,” J. Oper. Autom. Power Eng., vol. 4, pp. 104-116, 2016.
[3]    K. Afshar and A. Shokri Gazafroudi, “Application of stochastic programming to determine operating reserves with considering wind and load uncertainties,” J. Oper. Autom. Power Eng., vol. 1, pp. 96-109, 2007.
[4]    M. Jadid-Bonab, A. Dolatabadi, B. Mohammadi-Ivatloo, M. Abapour, and S. Asadi, “Risk-constrained Energy Management of PV Integrated Smart Energy Hub in the Presence of Demand Response Program and Compressed Air Energy,” IET Renew. Power Gener., 2019.
[5]    S. Shafiee, H. Zareipour, A. M. Knight, N. Amjady, and B. Mohammadi-Ivatloo, “Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant,” IEEE Trans. Power Syst., vol. 32, pp. 946-957, 2017.
[6]    E. Drury, P. Denholm, and R. Sioshansi, “The value of compressed air energy storage in energy and reserve markets,” Energy, vol. 36, pp. 4959-4973, 2011.
[7]    A. Mohammadi, M. H. Ahmadi, M. Bidi, F. Joda, A. Valero, and S. Uson, “Exergy analysis of a Combined Cooling, Heating and Power system integrated with wind turbine and compressed air energy storage system,” Energy Conv. Manag., vol. 131, pp. 69-78, 2017.
[8]    E. Yao, H. Wang, L. Wang, G. Xi, and F. Maréchal, “Multi-objective optimization and exergoeconomic analysis of a combined cooling, heating and power based compressed air energy storage system,” Energy Conv. Manag., vol. 138, pp. 199-209, 2017.
[9]    X. Liu, Y. Zhang, J. Shen, S. Yao, and Z. Zhang, “Characteristics of air cooling for cold storage and power recovery of compressed air energy storage (CAES) with inter-cooling,” Appl. Therm. Eng., vol. 107, pp. 1-9, 2016.
[10]  M. Saadat, F. A. Shirazi, and P. Y. Li, “Modeling and control of an open accumulator Compressed Air Energy Storage (CAES) system for wind turbines,” Appl. Energy, vol. 137, pp. 603-616, 2015.
[11]  M. Y. Damavandi, S. Bahramara, M. P. Moghaddam, M.-R. Haghifam, M. Shafie-khah, and J. P. Catalão, “Bi-level approach for modeling multi-energy players' behavior in a multi-energy system,” Proce. IEEE Power Tech, Eindhoven, 2015, pp. 1-6.
[12]  M. Yazdani-Damavandi, N. Neyestani, M. Shafie-khah, J. Contreras, and J. P. Catalao, “Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach,” IEEE Trans. Power Syst., vol. 33, pp. 397-411, 2018.
[13]  P. Sheikhahmadi, S. Bahramara, J. Moshtagh, and M. Y. Damavandi, “A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market,” Appl. Energy, vol. 214, pp. 24-38, 2018.
[14]  M. Jadidbonab, M. Vahid-Pakdel, H. Seyedi, and B. Mohammadi-ivatloo, “Stochastic assessment and enhancement of voltage stability in multi carrier energy systems considering wind power,” Int. J. Electr. Power Energy Syst., vol. 106, pp. 572-584, 2019.
[15]  M. T. Hagh, M. Jadidbonab, and M. Jedari, “Control strategy for reactive power and harmonic compensation of three-phase grid-connected photovoltaic system,” CIRED-Open Access Proce. J., vol. 2017, pp. 559-563, 2017.
[16]  M. G. Molina and P. E. Mercado, “Power flow stabilization and control of microgrid with wind generation by superconducting magnetic energy storage,” IEEE Trans. Power Electron., vol. 26, pp. 910-922, 2011.
[17]  M. Abbaspour, M. Satkin, B. Mohammadi-Ivatloo, F. H. Lotfi, and Y. Noorollahi, “Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES),” Renew. Energy, vol. 51, pp. 53-59, 2013.
[18]  A. Dolatabadi, M. Jadidbonab, and B. Mohammadi-ivatloo, “Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach,” IEEE Trans. Sustain. Energy, vol. 10, no. 1, pp. 438-448, 2019.
[19]  M. Jadidbonab, H. Mousavi-Sarabi, and B. Mohammadi-Ivatloo, “Risk-constrained scheduling of solar-based three state compressed air energy storage with waste thermal recovery unit in the thermal energy market environment,” IET Renew. Power Gener., 2018.
[20]  H. Liang and W. Zhuang, “Stochastic modeling and optimization in a microgrid: A survey,” Energies, vol. 7, pp. 2027-2050, 2014.
[21]  A. Hooshmand, M. H. Poursaeidi, J. Mohammadpour, H. A. Malki, and K. Grigoriads, “Stochastic model predictive control method for microgrid management,” Proce. IEEE PES in Innovative Smart Grid Tech. (ISGT), 2012, pp. 1-7.
[22]  A. Parisio, E. Rikos, and L. Glielmo, “Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study,” J. Process Cont., vol. 43, pp. 24-37, 2016.
[23]  W. Su, J. Wang, and J. Roh, “Stochastic energy scheduling in microgrids with intermittent renewable energy resources,” IEEE Trans. Smart Grid, vol. 5, pp. 1876-1883, 2014.
[24]  D. Wang, S. Ge, H. Jia, C. Wang, Y. Zhou, N. Lu, et al., “A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services,” IEEE Trans. Sustain. Energy, vol. 5, pp. 476-486, 2014.
[25]  S. A. Pourmousavi and M. H. Nehrir, “Real-time central demand response for primary frequency regulation in microgrids,” IEEE Trans. Smart Grid, vol. 3, pp. 1988-1996, 2012.
[26]  M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano, “Integrated scheduling of renewable generation and demand response programs in a microgrid,” Energy Conver. Manag., vol. 86, pp. 1118-1127, 2014.
[27]  A. Rabiee, A. Soroudi, B. Mohammadi-Ivatloo, and M. Parniani, “Corrective voltage control scheme considering demand response and stochastic wind power,”, IEEE Trans. Power Syst., vol. 29, pp. 2965-2973, 2014.
[28]  A. Rabiee, A. Soroudi, B. Mohammadi-Ivatloo, and M. Parniani, “Corrective voltage control scheme considering demand response and stochastic wind power,” IEEE Trans. Power Syst., vol. 29, pp. 2965-2973, 2014.
[29]  H. Heitsch and W. Römisch, “Scenario tree reduction for multistage stochastic programs,” Computational Management Science, vol. 6, pp. 117-133, 2009.
[30]  H. Safaei and D. W. Keith, “Compressed air energy storage with waste heat export: An Alberta case study,” Energy Convers. Manag., vol. 78, pp. 114-124, 2014.
[31]  S. Wen, H. Lan, Q. Fu, C. Y. David, and L. Zhang, “Economic allocation for energy storage system considering wind power distribution,” IEEE Trans. Power Syst., vol. 30, pp. 644-652, 2015.
[32]  H. Ren and W. Gao, “A MILP model for integrated plan and evaluation of distributed energy systems,” Appl. Energy, vol. 87, pp. 1001-1014, 2010.
[33]  “GAMS User Guide,” 2008. [Online]. Available: http://www.gams.com/
[34]  A. Dolatabadi, B. Mohammadi-ivatloo, M. Abapour, and S. Tohidi, “Optimal Stochastic Design of Wind Integrated Energy Hub,” IEEE Trans. Ind. Inf., 2017.
Journal of Operation and Automation in Power Engineering
Volume 8, Issue 1
February 2020
Pages 22-31

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History

  • Receive Date: 19 July 2018
  • Revise Date: 02 January 2019
  • Accept Date: 26 March 2019

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