Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage

被引：78

作者：

Zheng, Yingying ^{[1
]}

Jenkins, Bryan M. ^{[1
]}

Kornbluth, Kurt ^{[1
]}

Traeholt, Chresten ^{[2
]}

机构：

[1] Univ Calif Davis, Dept Biol & Agr Engn, One Shields Ave, Davis, CA 95616 USA

[2] Tech Univ Denmark, Dept Elect Engn, Elektrovej, DK-2800 Lyngby, Denmark

来源：

RENEWABLE ENERGY | 2018年 / 123卷

基金：

美国国家科学基金会;

关键词：

Microgrids; Renewables integration; Combined heat and power; Biomass; Modeling; Energy storage; Uncertainty; Stochastic analysis; GREENHOUSE-GAS EMISSIONS; SUPPLY-AND-DEMAND; MANAGEMENT-SYSTEM; COMBINED HEAT; MULTIOBJECTIVE OPTIMIZATION; CHP SYSTEMS; WIND; OPERATION; COST; RESOURCES;

D O I：

10.1016/j.renene.2018.01.120

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Deterministic constrained optimization and stochastic optimization approaches were used to evaluate uncertainties in biomass-integrated microgrids supplying both electricity and heat. An economic linear programming model with a sliding time window was developed to assess design and scheduling of biomass combined heat and power (BCHP) based microgrid systems. Other available technologies considered within the microgrid were small-scale wind turbines, photovoltaic modules (PV), producer gas storage, battery storage, thermal energy storage and heat-only boilers. As an illustrative example, a case study was examined for a conceptual utility grid-connected microgrid application in Davis, California. The results show that for the assumptions used, a BCHP/PV with battery storage combination is the most cost effective design based on the assumed energy load profile, local climate data, utility tariff structure, and technical and financial performance of the various components of the microgrid. Monte Carlo simulation was used to evaluate uncertainties in weather and economic assumptions, generating a probability density function for the cost of energy. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：204 / 217

页数：14

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