Improvement of regional market management considering reserve, information-gap decision theory, and emergency demand response program

Document Type : Article

Authors

1 Department of Electrical Engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran

2 Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

There is a variety of items which should be taken into consideration by a regional market manager (RMM). Participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty and types of the applied demand response program are some instances in this regard. One of the demand response programs is Emergency Demand Response Program (EDRP) which is considered in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM’s profit. Furthermore, determination of the demand level in the EDRP is performed on the basis of a logarithmic model which includes the price elasticity matrix (PEM) is included. The reserve supplied due to Aggregators (AGGs) is also prioritized using the reserve-margin factor (RMF). In addition, information-gap decision theory (IGDT) is applied to model uncertainty in the initial electricity price. The above mentioned items are modeled in a multi-level formulation.

Keywords


References:
1. Bazydo, G., and Werminski, S. "Demand side management through home area network systems", International Journal of Electrical Power & Energy Systems, 97, pp. 174-185 (2018).
2. Akbari-Dibavar, A., Farahmand-Zahed, A., and Mohammadi-Ivatloo, B. "Concept and glossary of demand response programs", Demand Response Application in Smart Grids, pp. 1-20, Springer (2020).
3. FERC, Staff Report. "Assessment of Demand Response and Advanced Metering", [Online] Available: http://www.FERC.gov. (2008).
4. Orans, R. "Phase I results: incentives and rate design for energy efficiency and demand response", LBNL- 60133, Energy and Environmental Economics Inc, Carson City, NV, USA (2006).
5. NYISO Auxiliary Market Operations "Demand response manual New York independent system operator", [Online] Available: http://www.nyso.com. (May, 2019).
6. Wang, Y., Zhang, F., Chi, C., et al. "A marketoriented incentive mechanism for emergency demand response in colocation data centers", Sustainable Computing: Informatics and Systems, 22, pp. 13-25 (2019).
7. Amroune, M., Bouktir, T., and Musirin, I. "Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm", Protection and Control of Modern Power Systems, 4(1), pp. 1-14 (2019).
8. Aghaei, J., Alizadeh, M.-I., Siano, P., et al. "Contribution of emergency demand response programs in power system reliability", Energy, 103, pp. 688-696 (2016).
9. Hajibandeh, N., Shafie-Khah, M., Osorio, G.J., et al. "A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators", Applied Energy, 212, pp. 721-732 (2018).
10. Hajibandeh, N., Ehsan, M., Soleymani, S., et al. "Prioritizing the effectiveness of a comprehensive set of demand response programs on wind power integration", International Journal of Electrical Power & Energy Systems, 107, pp. 149-158 (2019).
11. Asadinejad, A., and Tomsovic, K. "Optimal use of incentive and price based demand response to reduce costs and price volatility", Electric Power Systems Research, 144, pp. 215-223 (2017).
12. Yu, D., Xu, X., Dong, M., et al. "Modeling and prioritizing dynamic demand response programs in the electricity markets", Sustainable Cities and Society, 53, pp. 109-121 (2020).
13. Pourghasem, P., Sohrabi, F., Jabari, F., et al. "Combined heat and power stochastic dynamic economic dispatch using particle swarm optimization considering load and wind power uncertainties", In Optimization of Power System Problems, pp. 143-169, Springer (2020).
14. Imani, M.H., Niknejad, P., and Barzegaran, M. "The impact of customers' participation level and various incentive values on implementing emergency demand response program in microgrid operation", International Journal of Electrical Power & Energy Systems, 96, pp. 114-125 (2018).
15. Kitapbayev, Y., Moriarty, J., and Mancarella, P. "Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems", Applied Energy, 137, pp. 823-831 (2015).
16. Soroudi, A. and Amraee, T. "Decision making under uncertainty in energy systems: State of the art", Renewable and Sustainable Energy Reviews, 28, pp. 376-384 (2013).
17. Alipour, M., Zare, K., Seyedi, H., et al. "Real-time price-based demand response model for combined heat and power systems", Energy, 168, pp. 1119-1127 (2019).
18. Nourollahi, R., Nojavan, S., and Zare, K. "Risk-based purchasing energy for electricity consumers by retailer using information gap decision theory considering demand response exchange", Electricity Markets, pp. 135-168, Springer (2020).
19. Nojavan, S., Majidi, M., and Zare, K. "Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program", International Journal of Hydrogen Energy, 42(16), pp. 11857-11867 (2017).
20. Zhao, C., Wang, J., Watson, J.-P., et al. "Multi-stage robust unit commitment considering wind and demand response uncertainties", IEEE Transactions on Power Systems, 28(3), pp. 2708-2717 (2013).
21. Najafi, M., Ehsan, M., Fotuhi-Firuzabad, M., et al. "Optimal reserve capacity allocation with consideration of customer reliability requirements", Energy, 35(9), pp. 3883-3890 (2010).
22. Ahmadi-Khatir, A., Fotuhi-Firuzabad, M., and Goel, L. "Customer choice of reliability in spinning reserve procurement and cost allocation using well-being analysis", Electric Power Systems Research, 79(10), pp. 1431-1440 (2009).
23. Amirahmadi, M. and Foroud, A.A. "Stochastic multiobjective programming for simultaneous clearing of energy and spinning reserve markets considering reliability preferences of customers", International Journal of Electrical Power & Energy Systems, 53, pp. 691-703 (2013).
24. Ghahary, K., Abdollahi, A., Rashidinejad, M., et al. "Optimal reserve market clearing considering uncertain demand response using information gap decision theory", International Journal of Electrical Power & Energy Systems, 101, pp. 213-222 (2018).
25. Aalami, H., Moghaddam, M.P., and Yousefi, G. "Modeling and prioritizing demand response programs in power markets", Electric Power Systems Research, 80(4), pp. 426-435 (2010).
26. Abdi, H., Dehnavi, E., and Mohammadi, F. "Dynamic economic dispatch problem integrated with demand response (DEDDR) considering non-linear responsive load models", IEEE Transactions on Smart Grid, 7(6), pp. 2586-2595 (2015).
27. Niknam, T., Golestaneh, F., and Sadeghi, M.S. "θ-ultiobjective teaching-learning-based optimization for dynamic economic emission dispatch", IEEE Systems Journal, 6(2), pp. 341-352 (2012).
28. Ben-Haim, Y., Info-Gap Decision Theory: Decisions Under Severe Uncertainty, Elsevier (2006).
29. Mohammadi-Ivatloo, B., Zareipour, H., Amjady, N., et al. "Application of information-gap decision theory to risk-constrained self-scheduling of GenCos", IEEE Transactions on Power Systems, 28(2), pp. 1093-1102 (2012).
30. Liu, W, Huang, Y., Li, Z., et al. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response", Energy, pp. 172-179 (2020).