In view of the ineluctable uncertainties induced by renewables and load demand, it becomes challenging to realize reliable online energy scheduling for microgrid clusters. To overcome this challenge, this paper propos...
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In view of the ineluctable uncertainties induced by renewables and load demand, it becomes challenging to realize reliable online energy scheduling for microgrid clusters. To overcome this challenge, this paper proposes a novel energy management framework based on tube-based model predictive control for off-grid microgrid clusters, which enables the robustness against system uncertainties in the energy scheduling strategy with less sacrifice in economic performance and computational efficiency. The proposed energy management framework adopts a mixed-stage optimization structure, where the concerned problem is progressively optimized along with diverse energy management scales and various prediction horizons. A novel decentralized decomposition and coordination algorithm based on the alternating direction method of multipliers is developed, which enhances privacy preserving and overall convergency. Case studies in presence of high system uncertainties on a typical microgrid cluster demonstrate the effectiveness and computational efficiency of the proposed framework and solving algorithm.
In this study, a simulation-based multi-objective two-level optimization decision-making approach is developed for optimal irrigation water allocation, improving irrigation water productivity and controlling regional ...
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In this study, a simulation-based multi-objective two-level optimization decision-making approach is developed for optimal irrigation water allocation, improving irrigation water productivity and controlling regional accumulated salts. Techniques of multi-objective programming, two-level programming and simulation model of water and salt physical movement process are incorporated into the modeling framework. The simulated processes are parameterized and calibrated with field experimental data while the optimization model is used to generate optimal solutions though predefined objectives and the associated constraints. This model is applied to a case study on irrigation water allocation in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Firstly, the study area is delineated into several homogeneous irrigation decision-making units (IDMUs) for better characterizing their spatial variability because it's spatially heterogeneous. Afterwards, decomposition-coordinationalgorithm is introduced to solve such an integrated simulation-based multi-objective two-level optimization model. Finally, optimal solutions of irrigation water allocation for different crops during crop growth periods in different IDMUs can be obtained for supporting sustainable strategies of irrigation. The results can achieve balanced tradeoffs between different stakeholders (i.e., the upper-level decision-makers and the lower-level farmers) and between conflicting economic objectives and environmental objectives. Moreover, optimal solutions have a slight increase in economic returns over the baseline scenario (i.e., status quo), but the irrigation water productivity is increased by nearly 60% due to less irrigation water used. Regional accumulated salts can be controlled because the soil salinity is constrained within the predetermined salt accumulation constraint. Therefore, these findings can provide evidence for efficient use of irrigation water resources and furthe
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