Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty. (15th November 2018)

• Record Type: Journal Article
• Authors: Zheng, Yingying; Jenkins, Bryan M.; Kornbluth, Kurt; Kendall, Alissa; Træholt, Chresten
• Is Part Of: Applied energy. Volume 230(2018)
• Page Start: 836
• Page End: 844
• Publication Date: 2018-11-15
• Subjects: Biomass CHP -- Optimization -- Uncertainty -- Demand side management -- Load shifting -- Microgrid; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.09.015
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Optimize the design of a biomass-integrated microgrid employing demand response. Evaluate the influence of demand response on the smart grid performance. Provide a means to determine major risk factors associated with smart grid options. Demonstrate that demand response can cause increased peak demand with lower operating lost. Abstract: A load shifting algorithm based on economic linear programming with model predictive control was developed to minimize the operating cost of a biomass combined heat and power based microgrid system. The model simultaneously manages supply and demand of both electrical and thermal energy as decision variables. An algorithm was developed to optimize the shifting of loads based on the renewable energy generation and time-of-use tariff. As an illustrative example, a case study was examined for a conceptual utility grid-connected microgrid application in Davis, California. For the assumptions used, the proposed load shifting algorithm improved the performance of the microgrid by changing the load pattern and reduced the operating cost by 6.06% and increased the renewable energy fraction by 6.34% compared with the conventional no-load shift case. Monte Carlo simulation was used to evaluate uncertainties among the renewable energy, demand side, and economic assumptions, generating a probability density function for the cost of energy.