Optimal stochastic scheduling in microgrids under managed risk

Document Type : Original Article

Authors

1 Electrical Engineering Department, Razi University, Kermanshah, Iren

2 Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran

Abstract

This paper addresses the stochastic optimal day-ahead microgrid (MG) energy resources scheduling, considering the uncertain load, price of electricity, and generated electrical power by wind and solar units. Moreover, the vehicle-to-grid (V2G) implementation, load curtailment cost, and spinning reserve requirements are modeled to make the results more practical and applicable. Furthermore, the price elasticity of supply is considered to explore the relation between V2G capability and the optimization process. The stochastic energy resources scheduling problem is formulated in a two-level optimization framework. The unit commitment of dispatchable resources is analyzed in the upper level, and the lower level is formulated as a scenario-based two-stage stochastic programming problem that minimizes the operation cost of MG considering all the constraints. The risk of attaining unfavorable high costs of MG scheduling is considered using the variance approach. The generated scenarios are reduced by using the backward reduction method for each uncertain variable at each hour, independently. The artificial intelligent-based methods, including differential evolution algorithm (DEA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES) are applied to solve the problem. The effectiveness of the proposed approaches is confirmed by simulations on a modified 13-bus IEEE test system, in two cases of neglecting the risk and including the managed risk by applying the real-world data. The results confirmed the better performance of CMAES for solving such optimization problems. 

Keywords

Main Subjects


1. A. Cagnano, E. De Tuglie, and P. Mancarella, “Microgrids: Overview and guidelines for practical implementations and operation,” Applied Energy, vol. 258, p. 114039, 2020.
2. M. Shahbazitabar, H. Abdi, H. Nourianfar, A. Anvari-Moghaddam, B. Mohammadi-Ivatloo, and N. Hatziargyriou, “An introduction to micrgrids, concepts, definition, and classifications,” Microgrids: Advances in Operation, Control, and Protection, pp. 3–16, 2021.
3. F. Khavari, A. Badri, and A. Zangeneh, “Energy management in multimicrogrids considering point of common coupling constraint,” International Journal of Electrical Power & Energy Systems, vol. 115, p. 105465, 2020.
4. A. Nazari and R. Keypour, “A two-stage stochastic model for energy storage planning in a microgrid incorporating bilateral contracts and demand response program,” Journal of Energy Storage, vol. 21, pp. 281–294, 2019.
5. D. Wang, J. Qiu, L. Reedman, K. Meng, and L. L. Lai, “Two-stage energy management for networked microgrids with high renewable penetration,” Applied Energy, vol. 226, pp. 39–48, 2018.
6. M.-A. Rostami, A. Kavousi-Fard, and T. Niknam, “Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration,” IEEE Transactions on Industrial Informatics, vol. 11, no. 2, pp. 388–397, 2015.
7. S. M. B. Sadati, J. Moshtagh, M. Shafie-khah, and J. P. Catalão, “Smart distribution system operational scheduling considering electric vehicle parking lot and demand response programs,” Electric Power Systems Research, vol. 160, pp. 404–418, 2018.
8. R. Mkahl, A. Nait-Sidi-Moh, J. Gaber, and M. Wack, “An optimal solution for charging management of electric vehicles fleets,” Electric Power Systems Research, vol. 146, pp. 177–188, 2017.
9. H. Farzin, M. Moeini-Aghtaie, and M. Fotuhi-Firuzabad, “Reliability studies of distribution systems integrated with electric vehicles under battery-exchange mode,” IEEE Transactions on Power Delivery, vol. 31, no. 6, pp. 2473–2482, 2015.
10. M. Shamshirband, J. Salehi, and F. S. Gazijahani, “Look-ahead risk averse power scheduling of heterogeneous electric vehicles aggregations enabling v2g and g2v systems based on information gap decision theory,” Electric Power Systems Research, vol. 173, pp. 56–70, 2019.
11. H. Lund and W. Kempton, “Integration of renewable energy into the transport and electricity sectors through v2g,” Energy policy, vol. 36, no. 9, pp. 3578–3587, 2008.
12. E. Mortaz, A. Vinel, and Y. Dvorkin, “An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid,” Applied energy, vol. 242, pp. 1649–1660, 2019.
13. Q. Huang, Q.-S. Jia, Z. Qiu, X. Guan, and G. Deconinck, “Matching ev charging load with uncertain wind: A simulation-based policy improvement approach,” IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1425–1433, 2015.
14. S. S. Reddy, “Multi-objective based congestion management using generation rescheduling and load shedding,” IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 852–863, 2016.
15. M. Rahmani-andebili, “Spinning reserve supply with presence of electric vehicles aggregator considering compromise between cost and reliability,” IET Generation, Transmission & Distribution, vol. 7, no. 12, pp. 1442–1452, 2013.
16. Z. Wu, J. Ding, Q. Wu, Z. Jing, and X. Zhou, “Two-phase mixed integer programming for non-convex economic dispatch problem with spinning reserve constraints,” Electric Power Systems Research, vol. 140, pp. 653–662, 2016.
17. L. Guo, W. Liu, B. Jiao, B. Hong, and C. Wang, “Multi-objective stochastic optimal planning method for stand-alone microgrid system,” IET Generation, Transmission & Distribution, vol. 8, no. 7, pp. 1263–1273, 2014.
18. S. Mohammadi, S. Soleymani, and B. Mozafari, “Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 525–535, 2014.
19. P. Li, D. Xu, Z. Zhou, W.-J. Lee, and B. Zhao, “Stochastic optimal operation of microgrid based on chaotic binary particle swarm optimization,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 66–73, 2015.
20. M. Bornapour, R.-A. Hooshmand, and M. Parastegari, “An efficient scenario-based stochastic programming method for optimal scheduling of chp-pemfc, wt, pv and hydrogen storage units in micro grids,” Renewable energy, vol. 130, pp. 1049–1066, 2019.
21. P. Firouzmakan, R.-A. Hooshmand, M. Bornapour, and A. Khodabakhshian, “A comprehensive stochastic energy management system of micro-chp units, renewable energy sources and storage systems in microgrids considering demand response programs,” Renewable and Sustainable Energy Reviews, vol. 108, pp. 355–368, 2019.
22. D. Fioriti and D. Poli, “A novel stochastic method to dispatch microgrids using monte carlo scenarios,” Electric Power Systems Research, vol. 175, p. 105896, 2019.
23. H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, “A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids,” IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 117–127, 2016.
24. W. Su, J. Wang, and J. Roh, “Stochastic energy scheduling in microgrids with intermittent renewable energy resources,” IEEE Transactions on Smart grid, vol. 5, no. 4, pp. 1876–1883, 2013.
25. G. Liu, Y. Xu, and K. Tomsovic, “Bidding strategy for microgrid in dayahead market based on hybrid stochastic/robust optimization,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 227–237, 2015.
26. K. Masoudi and H. Abdi, “Multi-objective stochastic programming in microgrids considering environmental emissions,” Journal of Operation and Automation in Power Engineering, vol. 8, no. 2, pp. 141–151, 2020.
27. K. Masoudi and H. Abdi, “Multi-objective stochastic programming in microgrids considering environmental emissions,” Journal of Operation and Automation in Power Engineering, vol. 8, no. 2, pp. 141–151, 2020.
28. K. Masoudi and H. Abdi, “Scenario-based two-stage stochastic scheduling of microgrid considered as the responsible load,” Electric Power Components and Systems, vol. 48, no. 14-15, pp. 1614–1631, 2020.
29. A. J. Conejo, M. Carrión, J. M. Morales, et al., Decision making under uncertainty in electricity markets, vol. 1. Springer, 2010.
30. P. Artzner, F. Delbaen, J.-M. Eber, and D. Heath, “Coherent measures of risk,” Mathematical finance, vol. 9, no. 3, pp. 203–228, 1999.
31. J. Shen, C. Jiang, Y. Liu, and X. Wang, “A microgrid energy management system and risk management under an electricity market environment,” IEEE Access, vol. 4, pp. 2349–2356, 2016.
32. R. Khodabakhsh and S. Sirouspour, “Optimal control of energy storage in a microgrid by minimizing conditional value-at-risk,” IEEE Transactions on Sustainable Energy, vol. 7, no. 3, pp. 1264–1273, 2016.
33. Z. Shu and P. Jirutitijaroen, “Latin hypercube sampling techniques for power systems reliability analysis with renewable energy sources,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2066–2073, 2011.
34. D. S. Kirschen and G. Strbac, Fundamentals of power system economics. John Wiley & Sons, 2018.
35. H. Brand, E. Thorin, and C. Weber, “Scenario reduction algorithm and creation of multi-stage scenario trees,” Optimization of Cogeneration Systems in a Competitive Market Environment, Discussion Paper, vol. 7, 2002.
36. S. A. Siddiqui, Solving two-level optimization problems with applications to robust design and energy markets. University of Maryland, College Park, 2011.
37. H. Hejazi, H. Mohabati, S. Hosseinian, and M. Abedi, “Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1145–1155, 2010.
38. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948, IEEE, 1995.
39. N. Hansen, “The covariance matrix adaptation evolution strategy,” 2008.
40. R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, vol. 11, no. 4, pp. 341–359, 1997.
41. R. Sakipour and H. Abdi, “Optimizing battery energy storage system data in the presence of wind power plants: A comparative study on evolutionary algorithms,” Sustainability, vol. 12, no. 24, p. 10257, 2020.
42. H. Abdi, M. Ranjbaran, P. Nazari, and H. Akbari, “A review on pso models in power system operation,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 7, 2013.
43. Y. Wang, Z. Cai, and Q. Zhang, “Enhancing the search ability of differential evolution through orthogonal crossover,” Information Sciences, vol. 185, no. 1, pp. 153–177, 2012.
45. EPEX SPOT; Available from: https://www.epexspot.com/en/market-data The ELIA, Belgiums electricity transmission system operator website, [Online], Available: http://elia.be/en/grid-data