Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage. (August 2018)

• Record Type: Journal Article
• Authors: Zheng, Yingying; Jenkins, Bryan M.; Kornbluth, Kurt; Træholt, Chresten
• Is Part Of: Renewable energy. Volume 123(2018)
• Page Start: 204
• Page End: 217
• Publication Date: 2018-08
• Subjects: Microgrids -- Renewables integration -- Combined heat and power -- Biomass -- Modeling -- Energy storage -- Uncertainty -- Stochastic analysis; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.01.120
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: 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. Highlights: A model was developed to optimize the design of a biomass-integrated microgrid employing combined heat and power with energy storage. A receding horizon optimization with Monte Carlo simulation was proposed to evaluate optimal microgrid design and dispatch under uncertainty. The model application provides a means to determine major risk factors associated with alternative design integration.