تعداد نشریات | 26 |

تعداد شمارهها | 296 |

تعداد مقالات | 2,556 |

تعداد مشاهده مقاله | 3,813,572 |

تعداد دریافت فایل اصل مقاله | 2,501,231 |

## Generation Scheduling of Active Distribution Network with Renewable Energy Resources Considering Demand Response Management | ||

Journal of Operation and Automation in Power Engineering | ||

دوره 9، شماره 2، آبان 2021، صفحه 132-143 اصل مقاله (868.96 K) | ||

نوع مقاله: Research paper | ||

شناسه دیجیتال (DOI): 10.22098/joape.2021.6706.1501 | ||

نویسندگان | ||

A. Afraz^{1}؛ B. Rezaeealam^{*} ^{1}؛ S.J. SeyedShenava^{2}؛ M. Doostizadeh^{1}
| ||

^{1}Department of Electrical Engineering, Lorestan University, Khorramabad, Iran | ||

^{2}Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||

چکیده | ||

The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system. | ||

کلیدواژهها | ||

Active distribution network؛ demand response؛ energy storage system؛ renewable energy resource؛ demand management | ||

مراجع | ||

[1] O. Babacan, W. Torre and J. Kleissl, “Siting and sizing of distributed energy storage to mitigate voltage impact by solar PV in distribution systems”, [2] M. Khan, A. Yadav and L. Mathew, “Techno economic feasibility analysis of different combinations of PV-Wind-Diesel-Battery hybrid system for telecommunication applications in different cities of Punjab, India”, [3] C. Das, O. Bass, G. Kothapalli and TS. Mahmoud, “Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality”, [4] A. Jalali and M. Aldeen, “Risk-based stochastic allocation of ESS to ensure voltage stability margin for distribution systems”, [5] C. Das et al., “Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm”, [6] A. Tsikalakis and N. Hatziargyriou, “Centralized control for optimizing microgrids operation”, [7] A. Hoke et al., “Lookahead economic dispatch of microgrids with energy storage, using linear programming”, [8] M. Lively, “Creating a microgrid market: using a frequency driven pricing curve to dispatch load and embedded distributed generation and to charge and pay for participation”, [9] A. Ehsan and Q. Yang, “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review”, [10] M. Jannesar, A. Sedighi, M. Savaghebi and J. Guerrero, “Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration”, [11] H. Fallahzadeh-Abarghouei, S. Hasanvand, A. Nikoobakht and M. Doostizadeh, “Decentralized and hierarchical voltage management of renewable energy resources in distribution smart grid”, [12] N. Hatziargyriou et al., “Management of microgrids in market environment”, [13] G. Kryonidis, C. Demoulias and G. Papagiannis, “A new voltage control scheme for active medium-voltage (MV) network”, [14] A. Yasmeen, N. Javaid, S. Zahoor and H. Iftikhar, “Optimal energy management in microgrids using meta-heuristic technique”, [15] J. Iria, M. Heleno and G. Cardoso, “Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks”, [16] L. Wong et al., “Review on the optimal placement, sizing and control of an energy storage system in the distribution network”, [17] S. Mahdavi, R. Hemmati and M. Jirdehi, “Two-level planning for coordination of energy storage systems and wind-solar-diesel units in active distribution networks”, [18] S. Wang, F. Luo, Z. Dong and G. Ranz, “Joint planning of active distribution networks considering renewable power uncertainty”, [19] M. Plecas, H. Xu and I. Kockar, “Integration of energy storage to improve utilization of distribution networks with active network management shemes”, [20] Y. Zhang et al., “Optimal placement of battery energy storage in distribution networks considering conservation voltage reduction and stochastic load composition”', [21] D. Fioriti and D. Poli, “A novel stochastic method to dispatch microgrids using Monte Carlo scenarios”, [22] M. Mohsin and K. Rao, “Estimation of weibull distribution parameters and wind power density for wind farm site at Akal at Jaisalmer in Rajasthan”, [23] A. Chauhan and RP Saini, “Statistical analysis of wind speed data using Weibull distribution parameters”, [24] A. Dolatabadi, R. Ebadi, and B. Mohammadi-Ivatloo, “A two-stage stochastic programming model for the optimal sizing of hybrid PV/diesel/battery in hybrid electric ship system”, [25] A. Maleki and A. Askarzadeh, “Optimal sizing of a PV/wind/diesel system with battery storage for electrification to an off-grid remote region: A case study of Rafsanjan, Iran”, [26] J. Lian et al., “A review on recent sizing methodologies of hybrid renewable energy systems”, [27] A. Nateghi and H. Shahsavari, “Optimal design of FPI^ λ D^ μ based stabilizers in hybrid multi-machine power system using GWO algorithm”, [28] W. Sheng et al., “Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II”, [29] S. Abbasi and H. Abdi, “Return on investment in transmission network expansion planning considering wind generation uncertainties applying non-dominated sorting genetic algorithm”, [30] M. Kumar, P. Nallagownden and I. Elamvazuth, “Optimal placement and sizing of renewable distributed generations and capacitor banks into radial distribution system”, [31] Australian energy Market Operator (AEMO). Available: http://www.aemo.com.au/. | ||

آمار تعداد مشاهده مقاله: 775 تعداد دریافت فایل اصل مقاله: 597 |