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Dynamic Operation Management of a Renewable Microgrid including Battery Energy Storage 时间:2018-09-22 23:08   来源:未知   作者:admin   点击: 次 Abstract:In this paper, a novel dynamic programming technique is presented for optimal operation of a typical renewable microgrid including battery energy storage. The main idea is to use the scenarios analysis technique to proceed the uncertainties related to the available output power of wind and photovoltaic units and dynamic programming technique to obtain the optimal control strategy for a renewable microgrid system in a finite time period. First, to properly model the system, a mathematical model including power losses of the renewable microgrid is established, where the uncertainties due to the fluctuating generation from renewable energy sources are considered. Next, considering the dynamic power constraints of the battery, a new performance index function is established, where the Lagrange multipliers and interior point method will be presented for the equality and inequality operation constraints. Then, a feedback control scheme based on the dynamic programming is proposed to solve the model and obtain the optimal solution. Finally, simulation and comparison results are given to illustrate the performance of the presented method.
1. Introduction
   Nowadays, renewable energy sources (RESs) such as wind or photovoltaics have become more wide spread due to needs for satisfying the environment concerns. On the other hand, distributed generators (DGs) like diesel engines, microturbines, and fuel cells can be used to enhance the resiliency of power system and yield other social economic benefits. Therefore, renewable microgrid is expected to play an important role in future power systems [1, 2]. As a key enabling element of renewable microgrids, battery energy storages make microgrid become a strong coupling system in the time domain. In this regard, the methodologies applied to operation management of a renewable microgrid are getting more complicated and challengeable; therefore there is a strong need for more reliable scheduling of energy sources in renewable microgrid including battery energy storage.
  So far, many researchers have dealt with the optimal operation scheduling of energy sources in microgrids [3–7]. Previously conventional mathematical programming such as Lagrange relaxation [8, 9], lambda iteration [10, 11], Newton-Raphson [12], interior point method [13], weighted minimax [14], and quadratic programming [15] have been used to determine the least cost solution. However, the conventional mathematical programming methods have major disadvantages such that they can be trapped in local optimal, exhibited sensitivity to the initial starting points. And many of the methods cannot solve the nonsmooth, convex, and nonmonotonically increasing cost functions. Recently, computational intelligence [16, 17] and artificial intelligence based nonconventional methods [18] have been used to solve the optimal operation scheduling of energy sources in microgrids. Artificial intelligence based methods such as artificial neural network and computational intelligence methods such as genetic algorithm, particle swarm optimization, harmony search, simulated annealing, differential evolution, gravitational search algorithm, biogeography based optimization, bacterial foraging algorithm, ant colony optimization, cuckoo search, bat algorithm, artificial bee colony, firefly algorithm, and flower pollination algorithm have been used to solve the problem. These methods can enable us to solve the nonlinear and no-convex cost functions and can obtain nearly the global solutions. However, these methods have major disadvantages such as the evolutionary algorithms greatly depending on their parameters and having high computational time. Besides, hybrid methods which combine different algorithms have been used to solve the optimal operation scheduling of energy sources in microgrid. But, these