An incentive-based policy on minimization of GHG emissions and loss using adaptive group search multi-objective optimization algorithm

Document Type : Article


1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Electrical Engineering, Golpaygan University, Golpaygan, Iran


A transactive strategy to purposeful pricing distributed energy resources (DERs) in distribution networks is proposed in this paper. This strategy is presented as a novel heuristic optimization approach. The total network loss and released greenhouse gases (GHGs) emissions are considered as objective functions. In addition, the locational marginal prices (LMPs) and power factors of DERs are considered as decision variables. Each DER, which is more participated in the mitigation of afore-mentioned objectives, will contribute a larger excitement form benefits consequently. Therefore, more contribution consequent to more generation leads to a higher price for DERs bus in comparison to substation market price. Also, the earned benefits from loss/emission mitigations are allocated to DERs directly. The fairness of this pricing process is supervised by the Independent Distribution System Operator (IDSO). Because the problem has two contradictory objective functions, a reliable Multi-Objective method called Chaotic search and Covariance matrix (MGSOACC) is proposed to solve the problem. To evaluate the proposed method, the pricing procedure is applied on modified IEEE-33 and IEEE-69 bus test networks. Furthermore, in order to the validation of the proposed optimization method, the result-oriented comparisons between four conventional Multi-Objective optimization methods and proposed optimization method are presented.


1. Zhang, D., Li, M., Ji, X., Wu, J., and Dong, Y. "Revealing potential of energy-saving behind emission reduction", Management of Environmental Quality: An International Journal, 30(4), pp. 714-730 (2018).
2. Karasoy, A. and Akcay, S. "Effects of renewable energy consumption and trade on environmental pollution", Management of Environmental Quality: An International Journal, 30(2), pp. 437-455 (2019).
3. Pavani, P. and Singh, S.N. "Placement of DG for reliability improvement and loss minimization with reconfiguration of radial distribution systems", International Journal of Energy Sector Management, 8(3), pp. 312-329 (2014).
4. Lorestani, A., Gharehpetian, G.B., and Nazari, M.H. "Optimal sizing and techno-economic analysis of energy- and cost-efficient standalone multi-carrier microgrid", Energy, 178, pp. 751-764 (2019).
5. Lorestani, A., Mohammadian, M., Aghaee, S.S., et al. "A novel analytical-heuristic approach for placement of multiple distributed generator in distribution network", 2016 Smart Grids Conference (SGC), IEEE, pp. 1-7 (2016).
6. Ahmadi, P., Nazari, M.H., and Hosseinian, S.H. "Optimal resources planning of residential complex energy system in a day-ahead market based on invasive weed optimization algorithm", Engineering, Technology & Applied Science Research, 7(5), pp. 1934-1939 (2017).
7. Nazari, M.H., Hosseinian, S.H., and Azad-farsani, E."A multi-objective LMP pricing strategy in distribution networks based on MOGA algorithm", Journal of Intelligent & Fuzzy Systems, 36(6), pp. 6143-6154 (2019).
8. Azad-Farsani, E. "Loss minimization in distribution systems based on LMP calculation using honey bee mating optimization and point estimate method", Energy, 140, pp. 1-9 (2017).
9. Alkaabi, S.S., Zeineldin, H.H., and Khadkikar, V. "Short-term reactive power planning to minimize cost of energy losses considering PV systems", IEEE Transactions on Smart Grid, 10(3), pp. 2923-2935 (2019).
10. Savelli, I., Giannitrapani, A., Paoletti, S., and Vicino, A. "An optimization model for the electricity market clearing problem with uniform purchase price and zonal selling prices", IEEE Transactions on Power Systems, 33(3), pp. 2864-2873 (2018).
11. Ding, F. and Fuller, J.D. "Nodal, uniform, or zonal pricing: distribution of economic surplus", IEEE Transactions on Power Systems, 20(2), pp. 875-882 (2005).
12. Liu, L. and Zobian, A. "The importance of marginal loss pricing in an RTO environment", The Electricity Journal, 15(8), pp. 40-45 (2002).
13. Huang, S., Wu, Q., Oren, S.S., Li, R., and Liu, Z. "Distribution locational marginal pricing through quadratic programming for congestion management in distribution networks", IEEE Transactions on Power Systems, 30(4), pp. 2170-2178 (2015).
14. ISO New England Inc., ISO New England Manual for Market Operations, ISO-NE PUBLIC, Revision 57 (2019).
15. Yang, Z., Bose, A., Zhong, H., Zhang, N., Lin, J., Xia, Q., and Kang, C. "LMP revisited: A linear model for the loss-embedded LMP", IEEE Transactions on Power Systems, 32(5), pp. 4080-4090 (2017).
16. Hosseinian, S.H., Askarian-Abyaneh, H., Azad-Farsani, E., and Abedi, M. "Stochastic locational marginal price calculation in distribution systems using game theory and point estimate method", IET Generation, Transmission & Distribution, 9(14), pp.
1811-1818 (2015).
17. Sotkiewicz, P.M. and Vignolo, J.M. "Nodal pricing for distribution networks: efficient pricing for efficiency enhancing DG", IEEE Transactions on Power Systems, 21(2), pp. 1013-1014 (2006).
18. Morais, H., Faria, P., and Vale, Z. "Demand response design and use based on network locational marginal prices", International Journal of Electrical Power & Energy Systems, 61, pp. 180-191 (2014).
19. Farsani, E.A., Abyaneh, H.A., Abedi, M., and Hosseinian, S.H. "A novel policy for LMP calculation in distribution networks based on loss and emission reduction allocation using nucleolus theory", IEEE Transactions on Power Systems, 31(1), pp. 143-152 (2016).
20. Wei, Y., and Yanli, W. "Advanced Studies on Locational Marginal Pricing", Doctoral Dissertations, Tennessee Research, and Creative Exchange, University of Tennessee (2013).
21. Sarafraz, F., Ghasemi, H., and Monsef, H. "Locational marginal price forecasting by locally linear neuro-fuzzy model", 10th International Conference on Environment and Electrical Engineering, IEEE, pp. 1-4 (2011).
22. Khodadadi, A., Hasanpor Divshali, P., Nazari, M.H., and Hosseinian, S.H. "Small-signal stability improvement of an islanded microgrid with electronicallyinterfaced distributed energy resources in the presence of parametric uncertainties", Electric Power Systems Research, 160, pp. 151-162 (2018).
23. Wang, L., Zhong, X., and Liu, M. "A novel group search optimizer for multi-objective optimization", Expert Systems with Applications, 39(3), pp. 2939- 2946 (2012).
24. Ozceylan, E., Kabak, M., and Dagdeviren, M. "A fuzzy-based decision making procedure for machine selection problem", Journal of Intelligent & Fuzzy Systems, 30(3), pp. 1841-1856 (2016).
25. Galiana, F.D. and Khatib, S.E. "Emission allowances auction for an oligopolistic electricity market operating under cap-and-trade", IET Generation, Transmission & Distribution, 4(2), p. 191 (2010).
26. Farsani, E.A., Abyaneh, H.A., Abedi, M., and Hosseinian, S.H. "A novel policy for LMP calculation in distribution networks based on loss and emission reduction allocation using Nucleolus theory", IEEE Transactions on Power Systems, 31(1), pp. 143-152 (2016).
27. Wu, Q.H., Lu, Z., Li, M.S., and Ji, T.Y. "Optimal placement of FACTS devices by a group search optimizer with multiple rpoducer", 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), IEEE, pp. 1033-1039 (2008).
28. Hansen, N., Muller, S.D., and Koumoutsakos, P. "Reducing the time complexity of the derandomized evolution tsrategy with covariance matrix adaptation (CMA-ES)", Evolutionary Computation, 11(1), pp. 1- 18 (2003).
29. Peng, J. and Jiang, H. "Fair and analytical allocating of transmission losses using two-step coalitional game", IEEE Power Engineering Society General Meeting, Denver (2004).
30. Peng, J.-C., Jiang, H., and Song, Y.-H. "A weakly conditioned imputation of an impedance-branch dissipation power", IEEE Transactions on Power Systems, 22(4), pp. 2124-2133 (2007).
31. Sharma, S. and Abhyankar, A. "Loss allocation for weakly meshed distribution system using analytical formulation of shapley value", IEEE Transactions on Power Systems, 32(2), pp. 1369-1377 (2016).
32. Wei, F., Wu, Q.H., Jing, Z.X., Chen, J.J., and Zhou, X.X. "Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach", Energy, 111, pp. 933-946 (2016).
33. Hosseini,, Moradian, M., Shahinzadeh, H., and Ahmadi, S. "Optimal placement of distributed generators with regard to reliability -assessment using virus colony search algorithm", International Journal of Renewable Energy Research (IJRER), 8(2), pp. 714- 723 (2018).
34. Nazari, M.H., Khodadadi, A., Lorestani, A., Hosseinian, S.H., and Gharehpetian, G.B. "Optimal multiobjective D-STATCOM placement using MOGA for THD mitigation and cost minimization", Journal of Intelligent & Fuzzy Systems, 35(2), pp. 2339-2348 (2018).
35. Coello Coello, C.A., Veldhuizen, D.A., and Lamont, G.B., Evolutionary Algorithms for Solving Multi- Objective Problems, Springer US (2002).
36. Abdolahi, A., Salehi, J., Samadi Gazijahani, F., and Safari, A. "Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system", Journal of Renewable and Sustainable Energy, 10(4), p. 045502 (2018).
37. Zheng, J.H., Chen, J.J., Wu, Q.H., and Jing, Z.X. "Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded", Applied Energy, 154, pp. 369-379 (2015).
38. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197 (2002).