Abstract
Aiming at the peak shaving and poor power quality issues resulted from the intermittent power sources, battery energy storage system (BESS) is generally a key distributed generation unit in microgrid. Therefore this paper presents a sizing optimization method for battery energy storage system in the typical household microgrid based on solar PV and BESS. The mathematic models and the optimization solution algorithm are proposed respectively as to achieve the optimization goal of maximum economic benefit of BESS. The purpose of the proposed method here is to make full use of BESS to improve the system load peak shaving and power quality, as well as take the maximum advantage of the renewable energy resources available. Introduction Renewable energy sources are an effective alternative schemes for traditional petrochemical energy as to improve energy saving and environmental protection [1]. In supports of China’s renewable energy sources development policy, household microgrid based on distributed generation sources (DG) is becoming one of the solutions to this energy concern recently, which is gaining more and more attention for improving the power reliability, reducing the carbon emissions and cost of electric power. Aiming at the intermittent output features of solar photovoltaic (PV) array and wind turbine generator (WT), battery energy storage system (BESS) is the key factor for sustainable energy to realize flexible control and optimal operation of household microgrid, due to its rapid power adjustment capacity as well as the characteristic of supply and storage capability [2]. Recently design and optimization of BESS have been of recent considerable attention in the literatures [3-11]. Reference [6] introduces various energy storage technologies and their features such as battery energy storage system. Reference [7] builds a chance constrained programming model for the battery size optimization problem based on the analysis of the key factors for battery capacity and the probability model of wind, solar energy and load stochastic characteristics. Similarly, Reference [8] gives a novel method to optimize the hybrid energy storage capacity based on the chance constrained programming, which adopts genetic algorithm (GA) to get the trade-off solution between the minimum cost goal of energy storage and the confidence level of wind power fluctuation. Reference [9] proposes an optimal sizing method to determine the battery size and the corresponding configures of WT and PV unit, which can achieve the customers required loss of power supply probability with a minimum annualized cost of system with genetic algorithm. Reference [10] gives an energy storage sizing optimization approach which considers the convergence speed of the stability region and the state trajectory. In addition, considering the effect of the battery control strategy on the sizing optimization, Reference [11] presents a method to reduce the battery capacity by adopting neural network to control the charging and discharging process of the energy storage device. In conclusion, the methods mentioned as above are all given from its individual different research view points such as the optimization algorithm, the battery control strategy or the minimum cost optimization objective. However, those optimization mathematic models are simple and incomplete not to determine the optimum sizing of battery energy storage system. 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015) © 2015. The authors Published by Atlantis Press 121 This paper presents a sizing optimization method for battery energy storage system in the typical PV-BESS household microgrid, which is usually installed on the roof of building as to make full use of the green solar renewable energy. Its typical system configuration diagram is shown in Fig.1, where the load can get the power supply from the grid through the power flow controller (PFC). The mathematic model is given to optimize the battery size as to maximize the power economic benefit of BESS, subjected to various equality and inequality constraints. The proposed optimization solution algorithm is built with the operation strategy and constraints of BESS, which can make full use of solar PV renewable energy source available and improve the system load peak shaving. Fig.1. Structure diagram of PV-BESS household microgrid Problem Formulation The battery sizing optimization problem is formulated to gain a maximum economic benefit by allocating the optimum capacity and output power rating of BESS, subjected to a set of equality and inequality constraints according to the characteristics of BESS and microgrid. Thus, the optimization objective of BESS is to maximize the total economic profit which is equal to the electricity power revenue minus its generation cost, based on its key function of load peak shaving. 0 max ( ) [ ( ) ( )] T BESS BESS j profit f x Saving Sizing Cost Sizing
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