Stochastic Simultaneous Planning of Interruptible Loads, Renewable ‎Generations and Capacitors in Distribution Network ‎

Document Type : Research paper


Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran‎


Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.


[1]    M  Sahebi, A Soroudi, M Ehsan, “Simultaneous emergency demand response  programming and unit commitment programming in comparison with interruptible load contracts”, IET Gener. Transm. Distrib., vol. 6, pp. 605-11,  2012.
[2]    W. Niu, Y. Li, “Uncertain optimization decision of interruptible load in demand response program”, IEEE Innovative Smart Grid Technologies-Asia, 2014.
[3]    F. Gazijahani, J. Salehi. “Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach”, Energy, vol. 161, pp. 999-1015, 2018.
[4]    E. Shahryari et al., “Optimal energy management of microgrid in day-ahead and intra-day markets using a copula-based uncertainty modeling method”, J. Oper. Autom. Power Eng., vol. 8, pp. 86-96, 2020.
[5]    Y. Zhang, W. Chen, Q. Gao, “Model of interruptible load contract for minimum compensation cost”, Zhejiang University of Technology and Zhejiang University, 2008.
[6]    S. Ghaderi, H. Shayeghi, Y. Hashemi. “Impact of demand response technique on hybrid transmission expansion planning and reactive power planning”, J. Oper. Autom. Power Eng., vol. 9, pp. 1-10, 2021.
[7]    G. Aghajani, I. Heydari, “Energy management in microgrids containing electric vehicles and renewable‎ energy sources considering demand response”, J. Oper. Autom. Power Eng., vol. 9, pp. 34-48, 2021.
[8]    M. Azimi, M., Salami. “Optimal operation of integrated energy systems considering demand response program”, J. Oper. Autom. Power Eng., vol. 9, pp. 60-7, 2019.
[9]    H. Li, Y. Li, Z. Li, “A multi period energy acquisition model for a distribution company with distributed generation and interruptible load”, IEEE Trans. Power Syst., vol. 22, pp. 588-96, 2007.
[10]  A. Dizaji et al., “Resilient operation scheduling of microgrid using stochastic programming considering demand response and electric vehicles”, J. Oper. Autom. Power Eng., vol. 7, pp. 157-67, 2019.
[11]  H. Song, R. Diolata, Y. Hoon Joo, “Photovoltaic system allocation using discrete particle swarm optimization with multi-level quantization”, J. Electr. Technol., vol. 4, pp. 185-93, 2009.
[12]  F. Gazijahani, J. Salehi. “Game theory based profit maximization model for microgrid aggregators with presence of EDRP using information gap decision theory”, IEEE Syst. J., vol. 13, pp. 1767-75, 2018.
[13]  A. Dukpa, B. Venkatesh, “Fuzzy stochastic programming method: capacitor planning in distribution systems with wind generations”, IEEE Trans. Power Syst., vol. 26, 2011.
[14]  F. Gazijahani, J. Salehi, “IGDT-based complementarity approach for dealing with strategic decision making of price-maker VPP considering demand flexibility”, IEEE Trans. Ind. Inform., vol. 16, pp. 2212-20, 2019.
[15]  F. Gazijahani et al., “Joint energy and reserve scheduling of renewable powered microgrids accommodating price responsive demand by scenario: a risk-based augmented epsilon-constraint approach”, J. Cleaner Prod., vol. 262, pp. 121365, 2020.
[16]  R. Swief, N. El-Amary, “Optimal probabilistic reliable hybrid allocation for system reconfiguration applying WT/PV and reclosures”, Ain Shams Eng. J.,vol. 11, pp. 109-18, 2020.
[17]  Z. Lu, C. Shen, Y. Chen. “Coordinated allocation of distributed generation, capacitor banks and soft open points in active distribution networks considering dispatching results”, Applied Energy, vol. 231, pp. 1122-31, 2018.
[18]  M. Jafarian, A. Ranjbar, “Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine”, Renew. Energy, vol. 35, pp.2008-14, 2010.
[19]  P. Salyani, P. Salehi, “A customer oriented approach for distribution system reliability improvement using optimal distributed generation and switch placement”, J. Oper. Autom. Power Eng., vol. 7, pp. 246-60, 2019.
[20]  S. Pineda and A. Conejo, “Scenario reduction for risk-averse electricity trading”, IET Gener. Transm. Distrib., vol. 4, pp. 694-705, 2010.
[21]  M. Basu, “Dynamic economic emission dispatch using evolutionary programming and fuzzy satisfying method”, Int. J. Emerg. Electr. Power Syst., vol. 8, pp. 1, 2007.
[22]  F. Gazijahani et al., “Spatiotemporal splitting of distribution networks into self-healing resilient microgrids using an adjustable interval optimization”, IEEE Trans. Ind. Inf., vol. 17, pp. 5218-29, 2021.
[23]  A. wazir, N. Arbab, “Analysis and optimization of IEEE 33 Bus Radial Distribution System Using Optimization Algorithm”, J. Emerg. Trends Appl. Eng., vol. 1, pp. 121-34, 2016.