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Showing 9 results for Salehi
Eng Farhad Samadi Gazijahani, Dr Javad Salehi, Dr Navid Taghizadegan Kalantari, Volume 7, Issue 1 (9-2018)
Abstract
This paper presents a novel approach for optimal planning of the multi microgrids (MMGs) under uncertainties in load and renewable power generation. The proposed approach is applied for optimally determining the size, type, number, and site of renewable and dispatchable distribution generation (DG) with optimal allocation of switch for clustering distribution systems into a number of microgrids to economical and reliable structure. The optimization aim is to minimize the totally microgrid planning cost including investment cost, operation and maintenance cost, power losses cost, the pollutants emission cost and the cost of energy not supply (ENS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is implemented to minimize the objective function as an optimization algorithm. Also, the effect of optimization coefficients on the planning problem and the robustness of the proposed algorithm are investigated using sensitivity analysis. The efficiency of the proposed method are validated on 33-bus distribution system and the obtained results show that the proposed framework can be considered as an efficient tool for planning of multi microgrids under uncertainty.
Eng Amin Namvar, Dr Javad Salehi, Dr Navid Taghizadegan Kalantari, Volume 11, Issue 4 (11-2022)
Abstract
Energy supply is the most important need of human societies because life is impossible without energy. Therefore, the operation of energy resources is a substantial subject in the management of these resources. On the other hand, energy resources are often interdependent, which can help their management. In other words, the integrated operation of energy resources can be useful in energy management. In this regard, “the energy hub” has been introduced as a new concept for the integrated operation of energy resources. Using the concept of an energy hub, this paper tries to reduce the energy supply costs of consumers and manage these resources by integrated and simultaneous operation of electricity, natural gas, and water. In modeling, various pieces of equipment, such as energy storage devices, combined heat and power systems, and renewable sources, are used. Power-to-gas technology is also used to produce hydrogen and natural gas from water and electricity to supply hydrogen loads and inject the natural gas produced into the gas network. Power-to-gas technology uses excess electricity produced by renewable sources to produce hydrogen, which is obtained from the breakdown of water molecules. Given that the technology requires carbon dioxide to produce natural gas, this can reduce air pollution. In addition, a demand response program is implemented to shift a part of the electricity and heat consumption from peak hours to off-peak hours in order to reduce operating costs. Load transfer can be done with different methods, such as incentive plans or load management on the demand side. This modeling is a mixed integer linear programming. As mentioned, the model presented in this article is linear, so it is necessary to linearize the nonlinear equations. In this modeling, a method called "the Cartesian" method is used for linearization. After linearization of the nonlinear equations, this problem has been solved by GAMS software using the CPLEX solver. The results show that the proposed model has a significant impact on reducing operating costs and air pollution. In other words, devices such as electric heaters, combined heat and power, and power-to-gas units could reduce operating costs by 22% by converting energy carriers into different forms of energy, thereby significantly reducing pollutant emissions into the air so that, in the presence of the power-to-gas unit, the amount of pollution in the air can be decreased by 26%.
Mohsen Zangane, Mahmoud Samiei Moghaddam, Azita Azarfar, Mojtaba Vahedi, Nasrin Salehi, Volume 12, Issue 1 (4-2023)
Abstract
This paper presents a model for distribution network optimization considering a high penetration of photovoltaic (PV) sources and electric vehicle charging stations (EVCSs) based on on-load tap changing transformers (OLTC) and step voltage regulator (SVR), shunt capacitor (SC), and shunt reactor (ShR). The purpose is to prevent overvoltage due to power injection by PV sources and voltage drop due to EV charging in distribution networks. The proposed model is solved using a new hybrid algorithm called PSO-GA. Relevant studies show that with the increasing number of PSO replications, particle population variability is easily eliminated and placed in local optimization. The idea of combining GA is based on the PSO introduced in this study. Crossover and mutations of GA are performed on the PSO population, which is useful for improving the overall optimal ability of particles and causing the algorithm to deviate from the local optimal point. Two different IEEE standard test networks are tested under different load scenarios to analyze the proposed model. The results reveal the performance of the proposed model.
Moslem Salehi, Ali Akbar Moti Birjandi, Volume 12, Issue 2 (8-2023)
Abstract
Accurate fault location in a transmission line system is very important for electric utilities in order to quickly diagnose the location of the fault. Using a suitable technique for accurate fault location effectively reduces the time of fault recovery and system operation cost during maintenance. As a result, the reliability and quality of electricity delivery is improved and the economic losses caused by line outages are reduced. In this paper, an accurate two-terminal traveling wave-based fault location method for transmission lines is proposed. In this method, based on the arrival time of the first and second traveling waves caused by the short circuit fault, which are measured separately at both terminals of the transmission line, fault location and fault inception time are determined. The proposed algorithm does not need to synchronize data, transmission line parameters, and traveling wave speed, which are sources of error in traveling wave-based fault location methods. In order to better analyse fault-induced transient signals and detects the arrival time of travelling waves, mathematicl morphology filter (MMF) is used. Several faults on a typical 400 kV, 200 km transmission line were simulated using EMTP and MATLAB programs. The simulation results verified the proposed algorithm is able to accurately locate faults on transmission line. Also, the proposed method is independent of fault conditions shch as fault impedance, fault type, fault inception time and fault location.
Niki Ghanaei, Mahmoud Samiei Moghaddam, Esmaeil Alibeaki, Nasrin Salehi, Reza Davarzani, Volume 12, Issue 3 (10-2023)
Abstract
In this paper, a two-level optimization model of mixed quadratic integer programming (MIQP) is presented in order to optimally operate microgrids under worst-case output conditions of renewable energy sources. This two-level model is divided into two high-level and low-level problems. In the high-level problem, the goal is to reduce energy loss and load shedding in the demand response program, optimal charging and discharging of electric vehicles and energy storage systems. In the low-level problem, the objective is to maximize the power outage of renewable energy sources. In this model, a new method for solving the optimization problem is proposed, which is based on the reformulation of the problem to Karush-Cohen-Tucker (KKT) optimality conditions. For the analysis, a 33 bus microgrid is considered. The simulation results show that the proposed model maintains the flexibility of the network in the worst output conditions of renewable energy sources and no load interruption occurs in the network.
Mohamad Hadi Rostamian, Mohamad Hoseini Abarde, Azita Azarfar, Nasrin Salehi, Volume 12, Issue 4 (12-2023)
Abstract
In the optimization problems of intelligent distribution systems where there are many variables and parameters, providing an efficient algorithm that has the ability to converge and solve in large networks is one of the main challenges of researchers. In this paper, a compound integer quadratic optimization model for improving the performance of large-scale distribution network using demand-side management problem, energy storage system, battery-to-metro system, optimal control of OLTC and SVR tap-trans and distributed generation resources. Fossil and renewable are offered alongside capacitor and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy sources. According to the considered problems and also the model of large networks of 118 and 874 buses, most of the algorithms are not able to converge due to the existence of many variables. In this article, the optimal performance of the proposed hybrid evolutionary algorithm is shown on large distribution networks, which is able to reach global optimal solutions compared to similar algorithms.
Saeid Shakerinia, Abbas Fattahi May Abadi, Mojtaba Vahedi, Nasrin Salehi, Mahmoud Samiei Moghaddam, Volume 12, Issue 4 (12-2023)
Abstract
With the increasing penetration of renewable energy sources such as wind and photovoltaic generation in future microgrids, challenges arise due to variable weather conditions. In this paper, a model is proposed to optimize the performance of microgrids under the worst-case scenario of renewable energy source failures using a bi-level optimization. In the upper-level problem, optimization is carried out in terms of energy loss reduction, load shedding in the load management program, as well as optimal charging and discharging of energy storage systems. The lower-level problem considers maximizing the utilization of renewable energy. A bi-level optimization solution method is proposed, which involves binary variables at both levels and is solved using a non-dominated sorting genetic algorithm (NSGA-II). The proposed model and algorithm are implemented using the Julia programming language. The performance of the model is examined under various conditions using a 33-bus microgrid, and the optimization results demonstrate the optimal performance of the microgrid under the worst-case scenario of renewable energy source failures.
Mahyar Moradi, Mohamad Hoseini Abarde, Mojtaba Vahedi, Nasrin Salehi, Azita Azarfar, Volume 13, Issue 1 (4-2024)
Abstract
The development of microgrids is progressing due to smart loads, renewable energy sources, energy storage systems and also the presence of electric vehicles (EV). The presence of such devices in microgrids may cause inconsistency in the microgrid, which leads to increased losses and changes in the voltage of microgrid buses. In this paper, a mixed integer quadratic programming (MIQP) model is presented for microgrid energy management in the presence of smart loads, renewable energy sources, electric vehicles and energy storage systems. Also, to prevent voltage changes and reduce losses, the Distributed Flexible Alternating Transmission System (D-FACTS) device has been used. A scenario-based multi-objective function is proposed to reduce power losses and voltage deviations, reduce power outages of renewable sources, and reduce environmental pollution caused by distributed generation with fossil fuel (DG) and finally reduce the microgrid load definitively to reduce the vulnerability of the system. In this paper, an innovative evolutionary algorithm called learner performance-based behavior (LPB) algorithm is proposed. The proposed model is implemented on a 33-bus microgrid and the results show that the proposed energy management with demand side management can reduce energy loss by 9% and voltage deviation by 10%.
Alireza Kashki, Azita Azarfar, Mahmoud Samiei Moghaddam, Reza Davarzani, Nasrin Salehi, Volume 13, Issue 2 (7-2024)
Abstract
The proliferation of electric vehicles (EVs) presents both a significant challenge and opportunity for the energy sector. This study proposes a novel approach to optimizing EV charging in smart stations, considering its impact on the distribution network. Using the Particle Swarm Optimization (PSO) algorithm, we address the complex optimization problem of balancing EV charging demands with network constraints. Navigating the complexities of energy management in the distribution network, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model designed for network operations, featuring precise formulations for energy management. This model optimizes battery usage, EV energy management, compensator utilization, and distributed generation distribution. Through extensive simulations, we demonstrate the effectiveness of this approach in minimizing charging costs, reducing network congestion, and enhancing overall system performance. The multi-objective performance minimizes energy losses, electricity purchases, load reduction, distributed generation, and battery/EV costs over 24 hours. Simulations confirm a significant reduction in the operational costs of the distribution network. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring network reliability and efficiency. Incorporating EVs into the system results in significant improvements in performance indices compared to scenarios without electric vehicles. The results indicate a 14% reduction in the objective function value, with a notable 60% reduction in energy purchases and a 40% decrease in energy losses. Additionally, load reduction decreases by approximately 60%, while voltage deviation reduces by around 20%. Importantly, no reduction in PV or WD is observed with EV integration, indicating its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.
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