A Two-Step Stochastic Linear Programming Approach for Microgrid Resources and Energy Storage Management with Real-Time Pricing Program Using Salp Swarm Optimization Algorithm
Subject Areas : electrical and computer engineering
Mohsen
Sarami
1
مجيد
معظمي
2
غضنفر
شاهقلیان
3
(دانشگاه آزاد اسلامي واحد نجفآباد)
Keywords: Optimal operation, microgrid, renewable energies, energy storage, Salp swarm algorithm, power market, demand response,
Abstract :
Integrating renewable resources to provide local load has created a concept called microgrid. With the widespread introduction of microgrids, energy management and system utilization and resources in the electricity market are important tasks of microgrid management. In this paper, the problem of microgrid utilization is modeled taking into account economic, technical and uncertainties related to power consumption, wind speed and solar radiation in electricity market conditions. One of the most important issues in the electricity market is the discussion of the participation of units in real price conditions. In this paper, a framework for the exploitation of electricity and the consumption of controllable loads through integrated utilization of distributed energy sources of uncertainty is presented from a consumer perspective. The optimization problem is a two-step stochastic linear programming that minimized the cost of microgrid operation and expected cost of consumers considering the consumer’s requirement for controllable loads in the desire time interval and distribution company constraints that solved by using Salp swarm optimization algorithm. RBT and IBR tariffs are employed for modeling retail power market for better reflection of wholesale price volatility and avoid of the concurrent use of consumers. In this method price announced to the consumers by retailers only is limited specific later hours instead of the entire operation period. In this condition any timing of controllable loads need to price forecasting, while this forecasting have some uncertainties. These uncertainties are modeled using Monte Carlo method for stochastic price variable scenario generation. MATLAB software is employed for simulation and verification of the proposed method.
[1] M. B. Mollah, et al., "Blockchain for future smart grid: a comprehensive survey," IEEE Internet of Things J., vol. 8, no. 1, pp. 18-43, Jan. 2021.
[2] J. L. Gallardo, M. A. Ahmed, and N. Jara, "Clustering algorithm-based network planning for advanced metering infrastructure in smart grid," IEEE Access, vol. 9, pp. 48992-49006, 2021.
[3] W. Mendieta and C. A. Canizares, "Primary frequency control in isolated microgrids using thermostatically controllable loads," IEEE Trans. on Smart Grid, vol. 12, no. 1, pp. 93-105, Jan. 2021.
[4] S. J. A. D. Hosseini, M. Moradian, H. Shahinzadeh, and S. Ahmadi, "Optimal placement of distributed generators with regard to reliability assessment using virus colony search algorithm," International J. of Renewable Energy Research, vol. 8, no. 2, pp. 714-723, Jun. 2018.
[5] M. Daneshvar, B. Mohammadi-Ivatloo, and K. Zare, Integration of distributed energy resources under the transactive energy structure in the future smart distribution networks, Ch. 14, pp. 349-379, Academic Press, 2018.
[6] M. Nazari-Heris, B. Mohammadi-Ivatloo, G. B. Gharehpetian, and M. Shahidehpour, "Robust short-term scheduling of integrated heat and power microgrids," IEEE Systems J., vol. 13, no. 3, pp. 3295-3303, Sept. 2019.
[7] G. G. Talapur, H. M. Suryawanshi, L. Xu, and A. B. Shitole, "A reliable microgrid with seamless transition between grid connected and islanded mode for residential community with enhanced power quality," IEEE Trans. on Industry Applications, vol. 54, no. 5, pp. 5246-5255, Sep./Oct. 2018.
[8] J. Llanos, D. E. Olivares, J. W. Simpson-Porco, M. Kazerani, and D. Saez, "A novel distributed control strategy for optimal dispatch of isolated microgrids considering congestion," IEEE Trans. on Smart Grid, vol. 10, no. 6, pp. 6595-6606, Nov. 2019.
[9] H. Shahinzadeh, M. Moazzami, M. Abbasi, H. Masoudi, and V. Sheigani, "Smart design and management of hybrid energy structures for isolated systems using biogeography-based optimization algorithm," in Proc. IEEE Smart Grids Conf., 7 pp., Kerman, Iran, 20-21 Dec. 2016.
[10] T. Kerdphol, K. Fuji, Y. Mitani, M. Watanabe, and Y. Qudaih, "Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids," International J. of Electrical Power and Energy Systems, vol. 81, pp. 32-39, Oct. 2016.
[11] Y. Yang, S. Bremner, C. Menictas, and M. Kay, "Battery energy storage system size determination in renewable energy systems: a review," Renewable and Sustainable Energy Reviews, vol. 91, pp. 109-125, Aug. 2018.
[12] H. Shahinzadeh, M. Moazzami, S. H. Fathi, and G. B. Gharehpetian, "Optimal sizing and energy management of a grid-connected microgrid using HOMER software," in Proc. IEEE Smart Grids Conf., 6 pp., Kerman, Iran, 20-21 Dec. 2016.
[13] S. Karellas and N. Tzouganatos, "Comparison of the performance of compressed-air and hydrogen energy storage systems: Karpathos island case study," Renewable and Sustainable Energy Reviews, vol. 29, pp. 865-882, Jan. 2014.
[14] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, "Evaluation of nonlinear models for time-based rates demand response programs," International J. of Electrical Power and Energy Systems, vol. 65, pp. 282-290, Feb. 2015.
[15] S. Upadhyay and M. P. Sharma, "Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India," Energy, vol. 94, pp. 352-366, Jan. 2016.
[16] S. Shojaabadi, S. Abapour, M. Abapour, and A. Nahavandi, "Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties," IET Generation, Transmission and Distribution, vol. 10, no. 13, pp. 3330-3340, Oct. 2016.
[17] P. Kayal and C. K. Chanda, "Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network," Renewable Energy, vol. 75, pp. 173-186, Mar. 2015.
[18] S. Hadayeghparast, A. S. Farsangi, H. Shayanfar, and H. Karimipour, "Stochastic multi-objective economic/emission energy management of a microgrid in presence of combined heat and power systems," in Proc. IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conf., 9 pp., Calgary, Canada, 5-8 May 2019.
[19] M. Shepero, J. Munkhammar, J. Widen, J. D. Bishop, and T. Bostrom, "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: a review," Renewable and Sustainable Energy Reviews, vol. 89, pp. 61-71, Jun. 2018.
[20] J. Boland and A. Grantham, "Nonparametric conditional heteroscedastic hourly probabilistic forecasting of solar radiation," Multidisciplinary Scientific J., vol. 1, no. 1, pp. 174-191, Dec. 2018.
[21] H. Shayeghi and E. Shahryari, "Optimal operation management of grid-connected microgrid using multi-objective group search optimization algorithm," J. of Operation and Automation in Power Engineering, vol. 5, no. 2, pp. 227-239, Autumn 2017.
[22] J. Munkhammar, J. Widen, and J. Ryden, "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, vol. 142, pp. 135-143, Mar. 2015.
[23] F. Kalavani, B. Mohammadi-Ivatloo, A. Karimi, and F. Kalavani, "Stochastic optimal sizing of integrated cryogenic energy storage and air liquefaction unit in microgrid," Renewable Energy, vol. 136, pp. 15-22, Jun. 2019.
[24] M. Majidi, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam, "Optimal robust operation of combined heat and power systems with demand response programs," Applied Thermal Engineering, vol. 149, pp. 1359-1369, Feb. 2019.
[25] S. Mirjalili, et al., "Salp swarm algorithm: a bio-inspired optimizer for engineering design problems," Advances in Engineering Software, vol. 114, pp. 163-191, Dec. 2017.
[26] S. H. Dolatabadi, M. Ghorbanian, P. Siano, and N. D. Hatziargyriou, "An enhanced IEEE 33 bus benchmark test system for distribution system studies," IEEE Trans. on Power Systems, vol. 36, no. 3, pp. 2565-2572, May 2021.