Calculation of levelized costs of electricity for various electrical energy storage systems

被引:133
作者
Obi, Manasseh [1 ]
Jensen, S. M. [2 ]
Ferris, Jennifer B. [3 ]
Bass, Robert B. [1 ]
机构
[1] Portland State Univ, Dept Elect & Comp Engn, Portland, OR 97207 USA
[2] Portland Gen Elect, Portland, OR USA
[3] Bonneville Power Adm, Portland, OR USA
关键词
LCOE; Energy Storage; Monte Carlo; WIND POWER; SENSITIVITY-ANALYSIS; UNIT COMMITMENT; LCOE; IMPROVEMENT; GENERATION; PLANT; PV;
D O I
10.1016/j.rser.2016.09.043
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Installed capacity of renewable energy resources has increased dramatically in recent years, particularly for wind and photovoltaic solar. Concurrently, the costs of utility-scale electrical energy storage options have been decreasing, making inevitable a crossing point at which it will become economically viable to couple renewable energy generation with utility-scale storage systems. This paper proposes a methodology for calculating Levelized Cost of Electricity (LCOE) for utility-scale storage systems, with the intent of providing engineers, financiers and policy makers the means by which to evaluate disparate storage systems using a common economic metric. We discuss the variables influencing LCOE in detail, particularly those pertinent to electrical energy storage systems. We present results of LCOE calculations for various storage systems, specifically pumped hydro, compressed air, and chemical batteries, which we then compare with a more traditional arbitrage option, the simple-cycle combustion turbine. Federal and State government electrical energy storage tax incentives are considered as well. We also analyze the sensitivities of LCOE to several key variables using Monte Carlo analysis. Considering the downward-sloping cost trends of storage systems and the increased penetration levels of stochastic and non-dispatchable renewable resources, large-scale storage is becoming a significant issue for utilities, thus justifying the development of a levelized costing algorithm. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:908 / 920
页数:13
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