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I received my B.Sc. degree from Razi University and M.Sc. degree from the University of Tabriz in electrical power engineering in 2016 and 2019, respectively. I,m a graduate research associate in the NRI, now. My research interests are modeling, analysis, and design of power systems with a focus on energy storage, renewable power generation, and large-scale power data analysis.
The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads.
Ali Dargahi; Khezr Sanjani; Morteza Nazari-Heris; Behnam Mohammadi-Ivatloo; Sajjad Tohidi; Mousa Marzband. Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs. Sustainability 2020, 12, 7311 .
AMA StyleAli Dargahi, Khezr Sanjani, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Sajjad Tohidi, Mousa Marzband. Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs. Sustainability. 2020; 12 (18):7311.
Chicago/Turabian StyleAli Dargahi; Khezr Sanjani; Morteza Nazari-Heris; Behnam Mohammadi-Ivatloo; Sajjad Tohidi; Mousa Marzband. 2020. "Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs." Sustainability 12, no. 18: 7311.
Nowadays, the non-optimal placement of the shunt capacitors in distributed electricity systems may increase the total active power loss and lead to the voltage instability. Therefore, many researchers have recently focused on optimization of capacitor placement problem in radial and meshed distribution grids aiming to minimize transmission losses and improve the overall efficiency of the power delivery process. This chapter aims to present a backward-forward sweep (BFS) based algorithm for optimal allocation of shunt capacitors in distribution networks. The total real power loss of the whole system is minimized as the objective function. Moreover, the feeder current capacity and the bus voltage magnitude limits are considered as the optimization constraints. In addition, it is assumed that the sizes of capacitors are the known scalars. The 1st capacitor is considered to be located at the 1st bus of the test system. Then, the BFS load flow is run and the objective function is saved as 1st row and 1st column component of a loss matrix. Secondly, the 1st capacitor is assumed to be installed at bus 2 and the BFS load flow is run to obtain objective function as 2nd row and 1st column component of loss matrix. When all buses are assessed for installation of capacitor 1 and losses are calculated in each scenario, similar analyses are carried out for the 2nd capacitor bank and the values of the active power loss are saved as the 2nd column of the loss matrix. The same strategy is applied to other capacitors. Finally, a loss matrix is formed with number of rows and columns equal to the number of buses and shunt capacitors, respectively. The best places for installation of capacitors are determined based on the components of the loss matrix. Simulation of BFS based capacitor placement problem is conducted on the 33-bus distribution network to demonstrate its robustness and effectiveness in comparison with other procedures.
Farkhondeh Jabari; Khezr Sanjani; Somayeh Asadi. Optimal Capacitor Placement in Distribution Systems Using a Backward-Forward Sweep Based Load Flow Method. Developments in Advanced Control and Intelligent Automation for Complex Systems 2020, 63 -74.
AMA StyleFarkhondeh Jabari, Khezr Sanjani, Somayeh Asadi. Optimal Capacitor Placement in Distribution Systems Using a Backward-Forward Sweep Based Load Flow Method. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2020; ():63-74.
Chicago/Turabian StyleFarkhondeh Jabari; Khezr Sanjani; Somayeh Asadi. 2020. "Optimal Capacitor Placement in Distribution Systems Using a Backward-Forward Sweep Based Load Flow Method." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 63-74.
The global enhancement in gas-fired units has increased the rate of interdependency between electricity and natural gas networks. Nowadays, electrical systems heavily depend on reliability of gas suppliers to ramp up/ramp down during the on-peak hours and intermittent renewable generation and contingencies. Because of interdependency between electricity and natural gas system, it is imperative to co-optimize such two systems in an integrated scheme for improving the overall efficiency of the whole system and minimizing total investment and operation costs. This work proposes a hybrid robust-stochastic approach, which focuses on coordinated optimal scheduling of natural gas and electricity co-generation by considering market price contingencies. It should be noted that in proposed work, the methodology only considers purchasing power from market. The proposed model minimizes total costs of these two systems simultaneously, where both electrical and natural gas demand uncertainties are considered. On the other hand, a real-time demand response (DR) program is also considered in order to make load profile smoother to avoid technical and operational issues during on-peak hours in the system. In addition, the proposed method is applied on IEEE 24-bus RTS combined with natural gas network, and the simulation results are reported to evaluate the performance of the proposed model. The obtained results show that the proposed hybrid model has more economic efficiency and takes benefits of gas-electricity coordinated scheduling.
Khezr Sanjani; Neda Vahabzad; Morteza Nazari-Heris; Behnam Mohammadi-Ivatloo. A Robust-Stochastic Approach for Energy Transaction in Energy Hub Under Uncertainty. Robust Optimal Planning and Operation of Electrical Energy Systems 2019, 219 -232.
AMA StyleKhezr Sanjani, Neda Vahabzad, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo. A Robust-Stochastic Approach for Energy Transaction in Energy Hub Under Uncertainty. Robust Optimal Planning and Operation of Electrical Energy Systems. 2019; ():219-232.
Chicago/Turabian StyleKhezr Sanjani; Neda Vahabzad; Morteza Nazari-Heris; Behnam Mohammadi-Ivatloo. 2019. "A Robust-Stochastic Approach for Energy Transaction in Energy Hub Under Uncertainty." Robust Optimal Planning and Operation of Electrical Energy Systems , no. : 219-232.