Two-Stage Stochastic Energy Scheduling for Multi-Energy Rural Microgrids With Irrigation Systems and Biomass Fermentation

Multi-energy rural microgrids (MERMs) hold both economic potential and multi-energy coordination ability, emerging as a promising energy management paradigm in rural areas. In this paper, an energy scheduling method is investigated for a MERM with renewable energy and biomass resources, aiming to sa...

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Vydáno v:IEEE transactions on smart grid Ročník 16; číslo 2; s. 1075 - 1087
Hlavní autoři: Li, Wanhao, Zou, Yunyang, Yang, Hongming, Fu, Xueqian, Xiang, Sheng, Li, Zhengmao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Alternative energy sources
Ambient temperature
anaerobic biomass fermentation
Biomass
Biomass energy production
Costs
Crops
Decomposition
Distributed generation
Energy management
Fermentation
Irrigation
irrigation system
Irrigation systems
Microgrids
Mixed integer
Multi-energy rural microgrids
Natural gas
Precipitation
progress hedging
Quadratic programming
Renewable energy sources
Rural areas
Scheduling
Stochastic processes
Thermal analysis
two-stage stochastic optimization
Uncertainty
ISSN:1949-3053, 1949-3061
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Shrnutí:Multi-energy rural microgrids (MERMs) hold both economic potential and multi-energy coordination ability, emerging as a promising energy management paradigm in rural areas. In this paper, an energy scheduling method is investigated for a MERM with renewable energy and biomass resources, aiming to satisfy the rural electrical, thermal, natural gas, and irrigation demands economically. Mathematically, biomass flows are formulated by adopting a differential dynamics model of anaerobic biomass fermentation. The irrigation system is accurately formulated by fully taking into account meteorological information such as ambient temperature and precipitation. To handle the uncertainties in precipitation, reservoir inflows, renewable power generation as well as electrical and thermal load demands, a two-stage stochastic optimization method is employed, and the proposed model is then reformulated into a stochastic mixed integer quadratic programming (SMIQP) problem. To mitigate the computational burden arising from integer variables and enhance the solution efficiency, a scenario decomposition algorithm, progressive hedging (PH), is used to decompose the SMIQP into scenario-wise subproblems, which are then solved in parallel. Finally, the simulation results demonstrate the effectiveness of the proposed MERM scheduling method and the efficiency of the PH algorithm.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2024.3483444