جستجوی مقالات مرتبط با کلیدواژه « پیک سایی » در نشریات گروه « برق »

Nowadays, increasing the cost of electrical energy, especially at the peak load duration, besides the environmental caused by fossil fuels, have made the peak shaving problem by clean and renewable energies, one of the important as well as essential subjects in electric power industry. Hence, in this paper, combination of the photovoltaic power plant and the battery storage system is used to solve the peak shaving problem. In doing so, a new formulation is proposed for determining the optimal capacity of the photovoltaic power plant as well as battery storage. In the considered constraints in the mentioned formulation, technical issues and economic problems are combined together in an appropriate manner. Moreover, solving the optimization problem is performed regarding the consumption load profile as well as the hourly cost of the electrical energy of under-consideration costumer, and also the expected value of the inflation and the electrical energy cost in the future years. After that, the obtained solution is evaluated in terms of the economic feasibility and its interaction with the upstream electric power distribution network. Another considerable point in this paper is the use of the real data in an actual network for simulations. The simulation results, confirm the convenient performance as well as good efficiency of the proposed method.

The use of electric vehicles (EVs), to reduce greenhouse gases and air pollution caused by the fossil fuel consumption, seems to be an inevitable solution. The increased penetration of EVs imposes a large variable load to the grid. However, the battery of EVs in an aggregator provides a large source of energy storage. Therefore, EVs depending on the charging or discharging modes can act as flexible loads or as flexible energy sources. Then, the proper coordination and control charging and discharging of EVs, using vehicle-to-grid (V2G) technology, not only can minimize the undesirable effects resulting from the increased penetration of EVs, but also can improve the voltage profile. In this paper, a new algorithm with variable-objective function is proposed to control variable quantities of generation and consump tion. In the proposed method, the control of the point of common connection (PCC) voltage in a specific value of a determined permissible range, which depends on different operating conditions, can be considered as a variable-objective function. Moreover, there are constraints to state of charge (SOC) of electric vehicles (EVs) batteries and charging/discharging time. Other advantages of using the proposed method are the reduction of network losses in peak load hours and the establishment of an appropriate coordination between charging and discharging EVs. The simulations of the IEEE 14-Bus distribution system with V2G capabilities based on the proposed variable-objective function (VOF) algorithm are implemented for both charging and discharging modes using MATLAB/PSAT software and tested for the various scenarios. Finally, the results of the proposed method are compared with the traditional method and the merit of that is proved.

In many studies related to electric vehicle charging, the charging station rate is either assumed to be a constant or a continuous value. While the mechanism of charging stations is not like this. The most common methods of charging lithium-ion batteries are the constant voltage-constant current, multi-stage, pulse or adaptive method. As far as the researchers of this article are concerned, little research has been done on the coordinated charging of electric vehicles, considering the technical details of the battery charging method and the nonlinear behavior of the vehicle battery at charge levels and charge rates. Although this idealistic view simplifies synchronized charge optimization, in practice it leads to increased internal battery losses, reduced battery life, damage to the charger, and a large discrepancy between simulation results and practical implementation. With the increasing penetration of electric vehicles, the need for practical charging methods has increased. In this paper, first, the adaptive current charging method of a lithium-ion battery is modeled, in which the charge current is changed inversely to the battery internal resistance in each state of charge interval to obtain the lowest battery losses. This model is then used for coordinated charging of electric vehicles, taking into account the interests of customers (reducing total charging costs) and the distribution network operation company (peak-shaving) in a real 37-bus network. The results show the efficiency of the proposed model in internal losses minimization of vehicle batteries, the total charging cost reduction and peak shaving of the network.

Peak-shaving and load leveling have been one of the most important issues of power system.  There are some ways to reduce and smooth out peak loads. With the development of microgrid and smart grid concepts, installation of distributed generation (DG) and renewable energy resources as well as distributed battery storages, which are rapidly increasing on modern distribution grid, have been introduced as one of the solution for this issue. This paper aims to assess the effects of residential battery storage deployment on the peak demand shaving. The problem is formulated as a mixed-integer linear programming (MILP) and implemented in GAMS. The results show that the optimal charge/discharge scheduling of residential battery storages could reduce peak demand and make the load profile smoother.

In this paper, a novel procedure is proposed to identify the most efficient group of customers for participating in the peak shaving from utility companies’ point of view. This procedure is based on the smart meter data in advanced metering infrastructure (AMI), data mining and pattern recognition algorithms. Studies implies that customers with different consumption behaviors show different effects on the peak load. Consumption pattern recognition in addition to considering networks condition from consumers’ distribution point of view culminates in the most efficient group of them for this aim. The most efficient selection is made when the expected load profile is achieved by affecting the least number of customer as possible. The analysis and results of this paper confirm effectiveness of the proposed data-driven method. This method is able to reduce the number of affected customers in a peak shaving program by identifying the most efficient group of customers in a near real-time data exchanging in the grid. It should be noted that, the proposed method is implemented on a real dataset related to the Irish anonymized households’ consumption data which is provided from Irish Social Science Data Archive (ISSDA).

There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This innovative method to enhance the efficiency posts that are needed to install storage is very convenient. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.