بهمن بهمنی فیروزی

In this article, the optimal participation of energy hubs (EHs) connected to the grid in the day-to-day and real-time energy markets is presented. The proposed system consists of an electricity distribution network, a private transportation system, and a public transportation system, a water system, a thermal energy system. In addition, reconfiguration, and metro regenerative braking energy have been investigated. The proposed power distribution system is an on-grid hybrid microgrid. Improved gray wolf optimization algorithm for finding the best solution for scheduling the proposed system. Also, the reduced unscented transform scheme is applied to capture the uncertainty in the system. We demonstrate the effectiveness of the proposed method using a 33-bus IEEE test system in Matlab software package. Reductions in costs and pollution of the environment are demonstrated by the results. This study has demonstrated the completeness of the proposed smart network and the efficiency of the desired system by comparing it with the results of other studies and similar systems.

The Vernier PM machine is known as a machine with high torque and low speed, which has several advantages such as higher torque density, lower PM material volume, lower gear torque, improved performance and simpler and more durable structure compared to other structures based on the magnet is permanent. These machines are a good option for use in electric vehicles. One of the proposed structures that is suitable for this purpose is the structure of the Dual-Stator Consequent-Pole Vernier PM machine, which has high torque density, optimal functional characteristics and lower magnet consumption. But for the complete design of this structure, mechanical studies including thermal and vibration studies must be done on the designed electromagnetic structure. This is important because of the different geometry of conventional radial flux machines, unbalanced magnetic forces, and mechanical and thermal constraints. In this paper, the design of heat transfer system and mechanical structure of DS-CP-VPM machine has been done and thermal and vibration studies have been performed. Design variables were selected based on sensitivity analysis using the finite element method. Several design limitations in geometric dimensions, current density and magnetic flux density in different areas and mechanical forces have been considered. The results are confirmed for a 10 kW car with a torque of 2 KN for the application of an electric vehicle using the three-dimensional finite element method. In this paper, the thermal-mechanical analysis of the engine is performed and the simulation results in Comsol software are evaluated.

The issue of unit commitment (UC) is one of the most important issues in the electricity market, and with the smartening and restructuring of networks which its goals and variables have undergone changes. In this paper, solving and modeling the problem of unit commitment by considering smart networks is proposed. As we know, in smart grids, there are new issues such as demand side management and energy storage systems (batteries) as well as new goals such as reducing the environmental pollution of the units that must be considered. In this paper, the issue of unit commitment with demand side management modeling along with optimal charging and discharging of battery storage system with the aim of minimizing the economic costs of units and batteries and environmental pollution is presented. The proposed model is a Mixed Integer Linear Programming (MILP) model that is solved using powerful commercial software such as Gurobi and guarantees optimal global solutions. The proposed model is implemented on two standard IEEE 6 and 118 bus networks, the results of which show the efficiency of the proposed model.

The use of distributed generation based on the simultaneous production of electricity and heat is one of the important steps in the division of distribution networks into micro-grids as building blocks of intelligent systems. Therefore, it is necessary to study and evaluate the performance of distributed generation along with the units of simultaneous production of electricity and heat in micro-grids and operation of micro-grids with regard to electrical energy storage and heat storage. In this paper, modeling the behavior of CHP units and thermal energy storage and formulating the problem of optimal multi-objective utilization of micro-grids by considering heat and energy simultaneous production units with heat storage using an evolutionary training and learning algorithm (TLBO) is provided. The object functions include the costs of operating the micro-grid, the amount of network losses and the amount of deviation of the bus voltage from the nominal value. To solve the optimization problem, the evolutionary algorithm TLBO is used, which is a powerful and effective algorithm in this field. The study network is a 69-bus network that includes a number of distributed generation units and a number of simultaneous sources of electricity and heat. The results show the effective planning of multi-purpose operation of micro-grids in the presence of thermal loads by using MATLAB.

In this paper, optimal participation of the grid-connected energy hubs in the day-ahead energy and reserve markets is presented. The proposed scheme includes objective function that is minimized the difference between flexibility cost of hubs and their revenue in these markets. Also, this problem is constrained to power flow equations in the electrical, gas and heating networks, technical and reserve limits of these networks, and operation model of hubs including different sources and active loads. In the following, the stochastic programming is used to model the uncertainties of load, market price, networks reserve demand, renewable generation power, and energy demand of mobile active loads. Finally, by implement of the proposed scheme on the standard test network, the obtained numerical results confirms the capabilities of this scheme in the improvement of economic and flexibility situation of energy hubs, and improving the operation situation of the energy networks.

In this work, a new control scheme for synchronization of AC microgrids with upstream power grid is presented. The effects of V2Gs (vehicle to grid) dynamics on synchronization process is studied. This new control approach is based on the optimal fractional calculus and has been developed for synchronization of the microgrid. The V2Gs effect on the dynamics of the microgrid is analyzed through small signal stability and simulations. This effect is also considered in synchronization process by considering a PHEV-dominated-microgrid. The proposed control scheme is a coordinated control of distributed resources and provides a soft and reliable synchronization for microgrid. In the proposed control scheme, the fractional order proportional-integral-derivative (FOPID) controllers have optimally been tuned and implemented using the genetics algorithm (GA). The simulation results confirm the effectiveness of the proposed control strategy in soft and swift synchronization of the microgrid.

Optimal energy management in multi area smart grids will increase social welfare, reduce economic costs and environmental pollution. Power management solutions for smart grids include issues such as economical distribution of load, suitable load management, optimized charging and discharging of energy storages, and the availability of renewable resources considering limitation of power exchange in different area, all of which are issues in an intelligent grid, that in this paper has been considered. This paper presents a bi-level mixed integer quadratic programming (MIQP) model for energy management in multi-are smart grids with the aim of reducing economic costs and environmental pollution and increasing social welfare by considering energy storage systems, load management and Renewable resources are presented. In this paper presents a bi-level approach that the upper level is formulated to minimization economic cost and pollution of resource and lower level is presented to maximization social welfare in the form of Karush–Kuhn–Tucker (KKT) conditions. The simulation is implemented in MATLAB with Gurobi solver that the results show that the proposed bi-level model is also an efficient way to optimize energy in multi-area smart grids compared to Pareto front and Weight methods.