JOURNAL OF ENERGY MANAGEMENT
Year:2019 | Volume:9 | Issue:3 |Papers Count:10

In this paper, the electric vehicle (EV) charging station scheduling process is designed to maximize the profit of EVs owners and the station operator in two steps. First, a complete model is proposed to formulate the problem of charging and discharging EVs at charging stations in one-day-ahead 24-hours. The purpose of the program is to increase the profits of EVs owners charging station operator. In this manner, the charging behaviour of EVs such as arrival time to the station, the initial charge, the departure time from the station and the amount of requested energy are known as inputs of the problem. In the second stage, uncertainty is considered. Monte Carlo and Genetic Algorithm have been used to model the uncertainties in the problem and optimization, respectively. The output of the first stage is the optimal hourly load of the station. Then in the second stage, the optimal location of the charging station is determined by the obtained optimal load on the standard distribution network. So that the losses and voltage deviation index are minimized and the voltage stability index is maximized.

In this paper, a new control strategy for energy management in Plug-in Hybrid Electric Vehicles (PHEVs) using Fuzzy Cognitive Map (FCM) is presented. In this strategy, FCM is used as a supervisory control such that the State of Charge (SoC) of the battery is kept in the acceptable range and fuel consumption per kilometer is reduced, in addition to providing the request power. Since this method does not need to have an accurate model of the system, it has lower computational complexity in determining the control law and its implementation is easier than the classical methods. In this paper, the PHEV is series-parallel. For analysis the performance of the proposed control strategy, three actual and standard driving cycles and a typical urban driving cycle based on the actual traffic condition in Tehran city, have been used. Simulation results show the proposed control strategy provides the request power, reduceing the fuel consumption and keeps the SoC in high level, in comparison to conventional Fuzzy Controller.

In this paper, parking lots with bidirectional power flow capability, is used as an achievements of smart power systems. Based on operating conditions, electric vehicles can be considered as a load or generator. For optimal operation of power systems, allocation of these novel units is also necessary same as other distributed generation. In this paper, an optimization problem is proposed for allocation of electric vehicles parking lots. Loss reduction and reliability improvement are considered as two main components of this allocation problem cost function. Because of uncertain in electric vehicle grid connection, modeling of these probabilistic units is necessary to determine optimal size and location of parking lots. There-fore, in this paper electric vehicle’ s generated power is modeled by Markove Chain. Markove Chain analysis is based on passengers’-trip data such as trip start and end time. Simulation results show that installation of electric vehicle parking lots depends on many factors such as availability of electric vehicles as well as the electricity price. Also, it is shown that by taking enough incentive for electric vehicles owners, optimal size and location of parking lots has a considerable benefit for distribution system companies.

Train braking energy regeneration in urban electrical rail transportation systems can reduce energy consumption and operational cost of the system. In this paper, optimal design of an onboard energy storage system (OESS) with volume constraint is presented for urban electrical rail transportation systems (ERTS). Onboard super-capacitors are considered as the storage system. The objective functions is the total cost including the energy consumption and super-capacitor depreciation costs. Optimal design is executed considering five different traffic scenarios for daily metro headway and passenger. Determining the optimum capacity of the energy storage system minimizes the total cost of energy storage installation and energy cost while improving the voltage profile of the system. Simulation results confirm effectiveness of the proposed optimization method.

In recent years, hybrid energy storage (HES) to increase lifetime and reliability extremely are used in renewable systems and microgrids. In this paper, a new method for determining battery and supercapacitor capacity in an isolated microgrid is presented. Difference between Generation and load power using discrete fourier transform is transferred to the frequency domain and by determining the cut-off frequency, the optimized cost function is obtained; so that high frequencies are provided by supercapacitor and low frequencies by the battery. In the presented method, determining the capacity of the storages is done in such a way that in addition to the good response to loads, the lifetime of storages is maximized. The use of the proposed method due to the hybridization of storages improve the dynamic performance of the system by reducing the number of battery charging and discharging, decreases storage cost. Due to necessity and importance of storages in the isolated microgrid, the considered microgrid is isolated from the main grid that is consisted of different distributed generations. The proposed algorithm is tested on an isolated microgrid and the results confirm method effectiveness.

Usually, the conventional droop methods is utilized for control of autonomous micro-grids. In islanded micro-grids (MG), due to the effects of mismatch in line impedance, the reactive power can't share by using the conventional droop method. In this paper, a method is proposed to improve reactive power sharing. In this method, the micro-sources are acted with P-w & Q-E droop characteristics, and then the reactive power sharing error is reduced to modify Q-E characteristic on the periodic time distances. The feasibility and effectiveness of the proposed method are validated using simulation on SIMULINK/MATLAB software.

The increasing penetration of renewable energy results in changing the traditional power system planning and operation tools. As the generated power by the renewable energy resources are probabilistically changed, the certain power system analysis tolls cannot be applied in this case. Probabilistic optimal power flow is one of the most useful tools regarding the power system analysis in presence the uncertainties. In this paper, Monte Carlo simulation and point estimation methods are used to solve the POPF in the presence of wind and solar sources uncertainties. These methods are simulated on the PEGASE 89– bus European system. The most important novelty of this paper is arising from the comparison detailed studies of point estimation methods with the Monte Carlo simulation method. As the obtained results confirm, the point estimation methods lead to increasing the computing time efficiency in compare to the Monte Carlo simulation method. Also, increasing the number of sampling points in PEMs will be resulted in increasing the accuracy of the obtained results, while the computing time is still lower than the Monte Carlo simulation method.

The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, active power losses network and system loadability index, so artificial neural network used to compare two propagation algorithms of this type of network and define the model OPF analysis is carried out. The performances of the two algorithms are analyzed and compared using the model assessment index and MGN tests. Statistical method of Bootstrap has been used on achieve the best performance to improve OPF estimates. In order to reduce steps with less than 1%, for evaluate and improve the OPF estimation with single-objective optimization functions of the Bayesian and Perceptron neural networks have been studied in IEEE 30 bus test system. The results show the effective role of bootstrapped Bayesian neural network in terms of performance, using MATLAB software.

The solar radiation energy is one of the clean, essential and renewable resources for future. Iran, having 300 annual sunny days, has such intrinsic potential and thermosyphon solar-electrical water heaters (TSEWH) are the simplest solar-thermal convertors. Despite their unique merits, they can not supply hot water for domestic applications during cold period, so, an auxiliary backup system such as electrical resistance heater (electrical element) is required. The reduction of consumed electrical energy by electrical element not only leads to decrease of electrical network load, but also enhances the solar water heater efficiency and speeds up their application in the buildings sector. Careful scheduling the electrical element circuit via a timer key (timing mode operating) leads to reduction of auxiliary portion compared to the case that its circuit to be activated permanently (continues mode). Field results imply that, implementation the timer key to TSEWH has been considerable during cold period and reduced the consumed electrical energy as much as700kWh yearly and promoted its annual efficiency from 46% up to 67%.

The factors that can have a significant effect on the amount of solar energy received by the building are the material used in the external view and the lightning. The general objective of this research is to consider the existing climate conditions (Mashhad) in selecting and applying materials as well as the dimensions of the openings relative to the facade, taking into account the energy consumption criteria, in order to achieve the optimal pattern in design using modeling of a building sample Residential interiors are in the Design Builder software. The method of research in the present study is a composite type. In the first stage, the sections of the walls with different materials such as clay block, leka and AAC and intervals of 10-50% of the facade are introduced into the software and analyzed. In the next step, through parametric optimization, all modes in the sunblinds were simultaneously investigated and, finally, the AAC block with extruded thermal insulation of extruded polystyrene as the optimal materials in this climate and the proposed range for Exposures are rated at 25-30%, in addition to minimizing light access to the energy loss of buildings.