سایزبندی بهینه ذخیره انرژی باتری برای مدیریت عملیات ریز شبکه با استفاده از یک الگوریتم جدید خفاش
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|11731||2014||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Electrical Power & Energy Systems, Volume 56, March 2014, Pages 42–54
In recent years, due to large integration of Renewable Energy Sources (RESs) like wind turbine and photovoltaic unit into the Micro-Grid (MG), the necessity of Battery Energy Storage (BES) has increased dramatically. The BES has several benefits and advantages in the MG-based applications such as short term power supply, power quality improvement, facilitating integration of RES, ancillary service and arbitrage. This paper presents the cost-based formulation to determine the optimal size of the BES in the operation management of MG. Also, some restrictions, i.e. power capacity of Distributed Generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction should be considered as well. The suggested problem is a complicated optimization problem, the complexity of which is increased by considering the above constraints. Therefore, a robust and strong optimization algorithm is required to solve it. Herein, this paper proposes a new evolutionary technique named improved bat algorithm that is used for developing corrective strategies and to perform least cost dispatches. The performance of the approach is evaluated by one grid-connected low voltage MG where the optimal size of BES is determined professionally.
Micro-Grid (MG) is the corner stone and indispensable infrastructure of smart grid . Nowadays, with increasing concerns and challenges about the fluctuation and intermittency of Wind Turbine (WT) and Photo-Voltaic (PV) units as Renewable Energy Sources (RESs) in the MG system, the Micro-Grid Central Controller (MGCC) needs to implement Battery Energy Storage (BES). Combination of the BES can buffer the power output of RESs by storing excess energy throughout times of high availability and inject it to the MG during a power shortage. So, in recent years, the studies of researchers have been compulsorily gravitated to determine the appropriate capacity or size of BES for an optimized Operation Management of MG (OMMG). Lee and Chen  introduced the first BES sizing formulation for two industrial customers in Taiwan Power Company System. Mitra proposed a suitable technique of selecting the size of a BES in such a manner as to satisfy a reliability index . Le and Nguyen presented the BES sizing approach for wind turbine systems to guarantee the peak load demand . Kaldellis et al. offered a selection method of the most cost-efficient BES in order to match an inconstant solar-based energy system in . Chen et al. focused on determining the size of BES for a MG system in Singapore using a modeling language for mathematical programming . Mohammadi et al.  investigated an optimized design of MG containing PV array, Fuel Cell (FC) and BES in the presence of other Distributed Generators (DGs) under pool and hybrid electricity market model. Ekren and Ekren Banu  investigated the size optimization of a PV/WT hybrid energy conversion system with BES using Simulated Annealing (SA) algorithm. Aghamohammadi and Abdolahinia  presented a new method for determining optimal size of a BES for primary frequency control of a MG consisting of Micro-Turbine (MT), diesel generator, FC and PV system. Jia et al.  proposed a statistical model based on Monte Carlo to determine the capacity of BES-super capacitor hybrid energy storage system in an autonomous MG.
نتیجه گیری انگلیسی
In this paper, an efficient framework for MG operation management studies is proposed with regards to operation, maintenance and financial points. The fixed and maintenance cost of BES was taken into account in the optimization of MG studies as well as addressing an appropriate robust and effective meta-heuristic IBA approach in MG operation solving are some of the major superiorities in the presented structure. Considering various technical benefits and advantages of BES in the MG led to introducing invaluable BES sizing in the way of operation studies. In response, a new version of BA called IBA was applied as an efficacious method for handling these criteria in optimization process. The offered framework was tested over a day in a typical MG depicted in Fig. 2 and its usefulness was widely approved. Several conclusions are made from the simulation of the problem as follows. First, the comparison results of the case study A and three complex benchmark test functions (Generalized Rastrigin, Generalized Grienwangk and Generalized Ackley) demonstrate the superiority of the IBA in terms of the computational effort, robustness, convergence speed, and the performance of the solutions. Second, the quantitative results of the case study B show that considering a BES with optimal size for the MG could decrease the cost of the MG. The decrease in the total costs is because the BES can store the surplus powers of RESs and redispatch them appropriately. Also, it could make the DGs operate at a stable situation and lower their cost by reducing the start-up and shut-down frequency. Third, from the comparison results of case studies A, B and C, one can easily say that installing an optimal size of 150 kW h BES without initial charge will decrease the cost about 40% per day compared with the MG without BES. In addition, installing an optimal size of 250 kW h BES with the initial charge of 250 kW h will decrease the cost about 15% per day compared to the MG consisting of optimal size of 150 kW h BES without initial charge. By considering the discharging and charging efficiencies for BES, the optimal solution will also minimize the frequency of discharging or charging for BES. This will increase the LT of BES. Future works will concentrate on modeling the intermittency of RESs in the presence of optimal BES sizing for an unbalanced MG operation management considering simultaneous cost and emission objectives.