1. M. Carrion, A. J. Conejo, and J. M. Arroyo, “Forward contracting and
selling price determination for a retailer,” IEEE Transactions on Power
Systems, vol. 22, no. 4, pp. 2105–2114, 2007.
2. S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Optimal stochastic
energy management of retailer based on selling price determination
under smart grid environment in the presence of demand response
program,” Applied energy, vol. 187, pp. 449–464, 2017.
3. J. Torriti, M. G. Hassan, and M. Leach, “Demand response experience
Fig. 6. Demand for any customer group without uncertainty.
in europe: Policies, programmes and implementation,” Energy, vol. 35,
no. 4, pp. 1575–1583, 2010.
4. R. Sharifi, S. Fathi, and V. Vahidinasab, “A review on demand-side
tools in electricity market,” Renewable and Sustainable Energy Reviews, vol. 72, pp. 565–572, 2017.
5. O. Erdinc, “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response
strategies for smart households,” Applied Energy, vol. 126, pp. 142–
6. R. H. Boroumand and G. Zachmann, “Retailers’ risk management and
vertical arrangements in electricity markets,” Energy Policy, vol. 40,
pp. 465–472, 2012.
7. W. Lee, B. O. Kang, and J. Jung, “Development of energy storage system scheduling algorithm for simultaneous self-consumption
and demand response program participation in south korea,” Energy,
vol. 161, pp. 963–973, 2018.
8. T. Khalili, A. Jafari, M. Abapour, and B. Mohammadi-Ivatloo, “Optimal
battery technology selection and incentive-based demand response
program utilization for reliability improvement of an insular microgrid,”
Energy, vol. 169, pp. 92–104, 2019.
9. J. Xie, T. Hong, and J. Stroud, “Long-term retail energy forecasting
with consideration of residential customer attrition,” IEEE transactions
Fig. 7. Demand for any customer group without uncertainty.
on smart grid, vol. 6, no. 5, pp. 2245–2252, 2015.
10. S. A. Gabriel, M. F. Genc, and S. Balakrishnan, “A simulation approach
to balancing annual risk and reward in retail electrical power markets,”
IEEE Transactions on Power Systems, vol. 17, no. 4, pp. 1050–1057,
11. S. Nojavan, M. Mehdinejad, K. Zare, and B. Mohammadi-Ivatloo, “Energy procurement management for electricity retailer using new hybrid approach based on combined bica–bpso,” International Journal
of Electrical Power & Energy Systems, vol. 73, pp. 411–419, 2015.
12. S. Feuerriegel and D. Neumann, “Measuring the financial impact of demand response for electricity retailers,” Energy Policy, vol. 65, pp. 359–
13. M. Charwand, A. Ahmadi, P. Siano, V. Dargahi, and D. Sarno, “Exploring the trade-off between competing objectives for electricity energy
retailers through a novel multi-objective framework,” Energy Conversion and Management, vol. 91, pp. 12–18, 2015.
14. M. Charwand, A. Ahmadi, A. R. Heidari, and A. E. Nezhad, “Benders
decomposition and normal boundary intersection method for multiobjective decision making framework for an electricity retailer in energy
markets,” IEEE Systems Journal, vol. 9, no. 4, pp. 1475–1484, 2014.
15. A. Hatami, H. Seifi, and M. Sheikh-El-Eslami, “Optimal selling price
and energy procurement strategies for a retailer in an electricity market,” Electric Power Systems Research, vol. 79, no. 1, pp. 246–254,
16. S. Nojavan and K. Zare, “Optimal energy pricing for consumers by
electricity retailer,” International Journal of Electrical Power & Energy
Systems, vol. 102, pp. 401–412, 2018.
17. Á. Gomes, C. H. Antunes, and E. Oliveira, “Direct load control in
the perspective of an electricity retailer–a multi-objective evolutionary
approach,” in Soft Computing in Industrial Applications, pp. 13–26,
18. X. Huang, X. Lei, and Y. Jiang, “Comparison of three multi-objective
optimization algorithms for hydrological model,” in International Symposium on Intelligence Computation and Applications, pp. 209–216,
19. K. Deb, “Multi-objective optimisation using evolutionary algorithms: an
introduction,” in Multi-objective evolutionary optimisation for product
design and manufacturing, pp. 3–34, Springer, 2011.
20. A. Hojjati, M. Monadi, A. Faridhosseini, and M. Mohammadi, “Application and comparison of nsga-ii and mopso in multi-objective optimization of water resources systems,” Journal of Hydrology and Hydromechanics, vol. 66, no. 3, pp. 323–329, 2018.
21. M. A. Panduro, C. A. Brizuela, J. Garza, S. Hinojosa, and A. Reyna,
“A comparison of nsga-ii, demo, and em-mopso for the multi-objective
design of concentric rings antenna arrays,” Journal of Electromagnetic
Waves and Applications, vol. 27, no. 9, pp. 1100–1113, 2013.
22. R. T. Marler and J. S. Arora, “Survey of multi-objective optimization
methods for engineering,” Structural and multidisciplinary optimization,
vol. 26, no. 6, pp. 369–395, 2004.
23. J. S. Vardakas, N. Zorba, and C. V. Verikoukis, “A survey on demand
response programs in smart grids: Pricing methods and optimization
algorithms,” IEEE Communications Surveys & Tutorials, vol. 17, no. 1,
pp. 152–178, 2014.
24. M. Daghi, M. Sedghi, A. Ahmadian, and M. Aliakbar-Golkar, “Factor
analysis based optimal storage planning in active distribution network
considering different battery technologies,” Applied energy, vol. 183,
pp. 456–469, 2016.
25. C. Kahraman and S. Ç. Onar, Intelligent techniques in engineering
management, vol. 87. Springer, 2015.
26. A. T. Saric and R. M. Ciric, “Integrated fuzzy state estimation and load
flow analysis in distribution networks,” IEEE Transactions on Power
Delivery, vol. 18, no. 2, pp. 571–578, 2003.
27. M.-R. Haghifam and O. Malik, “Genetic algorithm-based approach for
fixed and switchable capacitors placement in distribution systems with
uncertainty and time varying loads,” IET generation, transmission &
distribution, vol. 1, no. 2, pp. 244–252, 2007.
28. S. Ganguly, N. Sahoo, and D. Das, “Multi-objective particle swarm optimization based on fuzzy-pareto-dominance for possibilistic planning
of electrical distribution systems incorporating distributed generation,”
Fuzzy Sets and Systems, vol. 213, pp. 47–73, 2013.
29. B. Zakeri and S. Syri, “Corrigendum to electrical energy storage systems: A comparative life cycle cost analysis[renew. sustain. energy rev.
42 (2015) 569–596],” Renewable and Sustainable Energy Reviews,
vol. 100, no. 53, pp. 1634–1635, 2016.
30. C. Zeljkovi ˇ c and N. Rajakovi ´ c, “Integrated cost-benefit assessment ´
of customer-driven distributed generation,” Electronics, vol. 18, no. 1,
pp. 54–61, 2014.
31. W. Yu, D. Liu, and Y. Huang, “Operation optimization based on the
power supply and storage capacity of an active distribution network,”
Energies, vol. 6, no. 12, pp. 6423–6438, 2013.
32. E. Litvinov, “Design and operation of the locational marginal pricesbased electricity markets,” IET generation, transmission & distribution,
vol. 4, no. 2, pp. 315–323, 2010.
33. M. A. F. Ghazvini, J. Soares, N. Horta, R. Neves, R. Castro, and
Z. Vale, “A multi-objective model for scheduling of short-term incentivebased demand response programs offered by electricity retailers,” Applied energy, vol. 151, pp. 102–118, 2015.
34. Y. Fu and Z. Li, “Different models and properties on lmp calculations,”
in 2006 IEEE Power Engineering Society General Meeting, pp. 11–pp,
35. G. Derakhshan, H. A. Shayanfar, and A. Kazemi, “Optimal design
of solar pv-wt-sb based smart microgrid using nshcso,” International
Journal of Hydrogen Energy, vol. 41, no. 44, pp. 19947–19956, 2016.